Chen, Xiyuan; Li, Qinghua
2014-01-01
As the core of the integrated navigation system, the data fusion algorithm should be designed seriously. In order to improve the accuracy of data fusion, this work proposed an adaptive iterated extended Kalman (AIEKF) which used the noise statistics estimator in the iterated extended Kalman (IEKF), and then AIEKF is used to deal with the nonlinear problem in the inertial navigation systems (INS)/wireless sensors networks (WSNs)-integrated navigation system. Practical test has been done to evaluate the performance of the proposed method. The results show that the proposed method is effective to reduce the mean root-mean-square error (RMSE) of position by about 92.53%, 67.93%, 55.97%, and 30.09% compared with the INS only, WSN, EKF, and IEKF. PMID:24693225
Xu, Yuan; Chen, Xiyuan; Li, Qinghua
2014-01-01
As the core of the integrated navigation system, the data fusion algorithm should be designed seriously. In order to improve the accuracy of data fusion, this work proposed an adaptive iterated extended Kalman (AIEKF) which used the noise statistics estimator in the iterated extended Kalman (IEKF), and then AIEKF is used to deal with the nonlinear problem in the inertial navigation systems (INS)/wireless sensors networks (WSNs)-integrated navigation system. Practical test has been done to evaluate the performance of the proposed method. The results show that the proposed method is effective to reduce the mean root-mean-square error (RMSE) of position by about 92.53%, 67.93%, 55.97%, and 30.09% compared with the INS only, WSN, EKF, and IEKF.
Chen, Xiyuan; Wang, Xiying; Xu, Yuan
2014-01-01
This paper deals with the problem of state estimation for the vector-tracking loop of a software-defined Global Positioning System (GPS) receiver. For a nonlinear system that has the model error and white Gaussian noise, a noise statistics estimator is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) named adaptive iterated Kalman filter (AIEKF) is proposed. A vector-tracking GPS receiver utilizing AIEKF is implemented to evaluate the performance of the proposed method. Through road tests, it is shown that the proposed method has an obvious accuracy advantage over the IEKF and Adaptive Extended Kalman filter (AEKF) in position determination. The results show that the proposed method is effective to reduce the root-mean-square error (RMSE) of position (including longitude, latitude and altitude). Comparing with EKF, the position RMSE values of AIEKF are reduced by about 45.1%, 40.9% and 54.6% in the east, north and up directions, respectively. Comparing with IEKF, the position RMSE values of AIEKF are reduced by about 25.7%, 19.3% and 35.7% in the east, north and up directions, respectively. Compared with AEKF, the position RMSE values of AIEKF are reduced by about 21.6%, 15.5% and 30.7% in the east, north and up directions, respectively. PMID:25502124
Chen, Xiyuan; Wang, Xiying; Xu, Yuan
2014-12-09
This paper deals with the problem of state estimation for the vector-tracking loop of a software-defined Global Positioning System (GPS) receiver. For a nonlinear system that has the model error and white Gaussian noise, a noise statistics estimator is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) named adaptive iterated Kalman filter (AIEKF) is proposed. A vector-tracking GPS receiver utilizing AIEKF is implemented to evaluate the performance of the proposed method. Through road tests, it is shown that the proposed method has an obvious accuracy advantage over the IEKF and Adaptive Extended Kalman filter (AEKF) in position determination. The results show that the proposed method is effective to reduce the root-mean-square error (RMSE) of position (including longitude, latitude and altitude). Comparing with EKF, the position RMSE values of AIEKF are reduced by about 45.1%, 40.9% and 54.6% in the east, north and up directions, respectively. Comparing with IEKF, the position RMSE values of AIEKF are reduced by about 25.7%, 19.3% and 35.7% in the east, north and up directions, respectively. Compared with AEKF, the position RMSE values of AIEKF are reduced by about 21.6%, 15.5% and 30.7% in the east, north and up directions, respectively.
Neural Network Aided Adaptive Extended Kalman Filtering Approach for DGPS Positioning
NASA Astrophysics Data System (ADS)
Jwo, Dah-Jing; Huang, Hung-Chih
2004-09-01
The extended Kalman filter, when employed in the GPS receiver as the navigation state estimator, provides optimal solutions if the noise statistics for the measurement and system are completely known. In practice, the noise varies with time, which results in performance degradation. The covariance matching method is a conventional adaptive approach for estimation of noise covariance matrices. The technique attempts to make the actual filter residuals consistent with their theoretical covariance. However, this innovation-based adaptive estimation shows very noisy results if the window size is small. To resolve the problem, a multilayered neural network is trained to identify the measurement noise covariance matrix, in which the back-propagation algorithm is employed to iteratively adjust the link weights using the steepest descent technique. Numerical simulations show that based on the proposed approach the adaptation performance is substantially enhanced and the positioning accuracy is substantially improved.
NASA Astrophysics Data System (ADS)
Dong, Gangqi; Zhu, Zheng H.
2016-05-01
This paper presents a real-time, vision-based algorithm for the pose and motion estimation of non-cooperative targets and its application in visual servo robotic manipulator to perform autonomous capture. A hybrid approach of adaptive extended Kalman filter and photogrammetry is developed for the real-time pose and motion estimation of non-cooperative targets. Based on the pose and motion estimates, the desired pose and trajectory of end-effector is defined and the corresponding desired joint angles of the robotic manipulator are derived by inverse kinematics. A close-loop visual servo control scheme is then developed for the robotic manipulator to track, approach and capture the target. Validating experiments are designed and performed on a custom-built six degrees of freedom robotic manipulator with an eye-in-hand configuration. The experimental results demonstrate the feasibility, effectiveness and robustness of the proposed adaptive extended Kalman filter enabled pose and motion estimation and visual servo strategy.
NASA Astrophysics Data System (ADS)
Wang, Xudong; Syrmos, Vassilis L.
2004-07-01
In this paper, an adaptive reconfigurable control system based on extended Kalman filter approach and eigenstructure assignments is proposed. System identification is carried out using an extended Kalman filter (EKF) approach. An eigenstructure assignment (EA) technique is applied for reconfigurable feedback control law design to recover the system dynamic performance. The reconfigurable feedforward controllers are designed to achieve the steady-state tracking using input weighting approach. The proposed scheme can identify not only actuator and sensor variations, but also changes in the system structures using the extended Kalman filtering method. The overall design is robust with respect to uncertainties in the state-space matrices of the reconfigured system. To illustrate the effectiveness of the proposed reconfigurable control system design technique, an aircraft longitudinal vertical takeoff and landing (VTOL) control system is used to demonstrate the reconfiguration procedure.
Wang, Xin; Wu, Linhui; Yi, Xi; Zhang, Yanqi; Zhang, Limin; Zhao, Huijuan; Gao, Feng
2015-01-01
Due to both the physiological and morphological differences in the vascularization between healthy and diseased tissues, pharmacokinetic diffuse fluorescence tomography (DFT) can provide contrast-enhanced and comprehensive information for tumor diagnosis and staging. In this regime, the extended Kalman filtering (EKF) based method shows numerous advantages including accurate modeling, online estimation of multiparameters, and universal applicability to any optical fluorophore. Nevertheless the performance of the conventional EKF highly hinges on the exact and inaccessible prior knowledge about the initial values. To address the above issues, an adaptive-EKF scheme is proposed based on a two-compartmental model for the enhancement, which utilizes a variable forgetting-factor to compensate the inaccuracy of the initial states and emphasize the effect of the current data. It is demonstrated using two-dimensional simulative investigations on a circular domain that the proposed adaptive-EKF can obtain preferable estimation of the pharmacokinetic-rates to the conventional-EKF and the enhanced-EKF in terms of quantitativeness, noise robustness, and initialization independence. Further three-dimensional numerical experiments on a digital mouse model validate the efficacy of the method as applied in realistic biological systems.
Robust adaptive extended Kalman filtering for real time MR-thermometry guided HIFU interventions.
Roujol, Sébastien; de Senneville, Baudouin Denis; Hey, Silke; Moonen, Chrit; Ries, Mario
2012-03-01
Real time magnetic resonance (MR) thermometry is gaining clinical importance for monitoring and guiding high intensity focused ultrasound (HIFU) ablations of tumorous tissue. The temperature information can be employed to adjust the position and the power of the HIFU system in real time and to determine the therapy endpoint. The requirement to resolve both physiological motion of mobile organs and the rapid temperature variations induced by state-of-the-art high-power HIFU systems require fast MRI-acquisition schemes, which are generally hampered by low signal-to-noise ratios (SNRs). This directly limits the precision of real time MR-thermometry and thus in many cases the feasibility of sophisticated control algorithms. To overcome these limitations, temporal filtering of the temperature has been suggested in the past, which has generally an adverse impact on the accuracy and latency of the filtered data. Here, we propose a novel filter that aims to improve the precision of MR-thermometry while monitoring and adapting its impact on the accuracy. For this, an adaptive extended Kalman filter using a model describing the heat transfer for acoustic heating in biological tissues was employed together with an additional outlier rejection to address the problem of sparse artifacted temperature points. The filter was compared to an efficient matched FIR filter and outperformed the latter in all tested cases. The filter was first evaluated on simulated data and provided in the worst case (with an approximate configuration of the model) a substantial improvement of the accuracy by a factor 3 and 15 during heat up and cool down periods, respectively. The robustness of the filter was then evaluated during HIFU experiments on a phantom and in vivo in porcine kidney. The presence of strong temperature artifacts did not affect the thermal dose measurement using our filter whereas a high measurement variation of 70% was observed with the FIR filter.
Adaptable Iterative and Recursive Kalman Filter Schemes
NASA Technical Reports Server (NTRS)
Zanetti, Renato
2014-01-01
Nonlinear filters are often very computationally expensive and usually not suitable for real-time applications. Real-time navigation algorithms are typically based on linear estimators, such as the extended Kalman filter (EKF) and, to a much lesser extent, the unscented Kalman filter. The Iterated Kalman filter (IKF) and the Recursive Update Filter (RUF) are two algorithms that reduce the consequences of the linearization assumption of the EKF by performing N updates for each new measurement, where N is the number of recursions, a tuning parameter. This paper introduces an adaptable RUF algorithm to calculate N on the go, a similar technique can be used for the IKF as well.
Speed adaptation as Kalman filtering.
Barraza, Jose F; Grzywacz, Norberto M
2008-10-01
If the purpose of adaptation is to fit sensory systems to different environments, it may implement an optimization of the system. What the optimum is depends on the statistics of these environments. Therefore, the system should update its parameters as the environment changes. A Kalman-filtering strategy performs such an update optimally by combining current estimations of the environment with those from the past. We investigate whether the visual system uses such a strategy for speed adaptation. We performed a matching-speed experiment to evaluate the time course of adaptation to an abrupt velocity change. Experimental results are in agreement with Kalman-modeling predictions for speed adaptation. When subjects adapt to a low speed and it suddenly increases, the time course of adaptation presents two phases, namely, a rapid decrease of perceived speed followed by a slower phase. In contrast, when speed changes from fast to slow, adaptation presents a single phase. In the Kalman-model simulations, this asymmetry is due to the prevalence of low speeds in natural images. However, this asymmetry disappears both experimentally and in simulations when the adapting stimulus is noisy. In both transitions, adaptation now occurs in a single phase. Finally, the model also predicts the change in sensitivity to speed discrimination produced by the adaptation.
Huang, Haoqian; Chen, Xiyuan; Zhou, Zhikai; Xu, Yuan; Lv, Caiping
2014-01-01
High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle) becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF) based on the quaternion expanded to the state variable (BD-AEKF). The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF) based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method. PMID:25479331
Huang, Haoqian; Chen, Xiyuan; Zhou, Zhikai; Xu, Yuan; Lv, Caiping
2014-12-03
High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle) becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF) based on the quaternion expanded to the state variable (BD-AEKF). The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF) based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method.
Extended Kalman filtering of point process observation.
Salimpour, Yousef; Soltanian-Zadeh, Hamid; Abolhassani, Mohammad D
2010-01-01
A temporal point process is a stochastic time series of binary events that occurs in continuous time. In computational neuroscience, the point process is used to model neuronal spiking activity; however, estimating the model parameters from spike train is a challenging problem. The state space point process filtering theory is a new technique for the estimation of the states and parameters. In order to use the stochastic filtering theory for the states of neuronal system with the Gaussian assumption, we apply the extended Kalman filter. In this regard, the extended Kalman filtering equations are derived for the point process observation. We illustrate the new filtering algorithm by estimating the effect of visual stimulus on the spiking activity of object selective neurons from the inferior temporal cortex of macaque monkey. Based on the goodness-offit assessment, the extended Kalman filter provides more accurate state estimate than the conventional methods.
Q-Method Extended Kalman Filter
NASA Technical Reports Server (NTRS)
Zanetti, Renato; Ainscough, Thomas; Christian, John; Spanos, Pol D.
2012-01-01
A new algorithm is proposed that smoothly integrates non-linear estimation of the attitude quaternion using Davenport s q-method and estimation of non-attitude states through an extended Kalman filter. The new method is compared to a similar existing algorithm showing its similarities and differences. The validity of the proposed approach is confirmed through numerical simulations.
Kalman filter based control for Adaptive Optics
NASA Astrophysics Data System (ADS)
Petit, Cyril; Quiros-Pacheco, Fernando; Conan, Jean-Marc; Kulcsár, Caroline; Raynaud, Henri-François; Fusco, Thierry
2004-12-01
Classical Adaptive Optics suffer from a limitation of the corrected Field Of View. This drawback has lead to the development of MultiConjugated Adaptive Optics. While the first MCAO experimental set-ups are presently under construction, little attention has been paid to the control loop. This is however a key element in the optimization process especially for MCAO systems. Different approaches have been proposed in recent articles for astronomical applications : simple integrator, Optimized Modal Gain Integrator and Kalman filtering. We study here Kalman filtering which seems a very promising solution. Following the work of Brice Leroux, we focus on a frequential characterization of kalman filters, computing a transfer matrix. The result brings much information about their behaviour and allows comparisons with classical controllers. It also appears that straightforward improvements of the system models can lead to static aberrations and vibrations filtering. Simulation results are proposed and analysed thanks to our frequential characterization. Related problems such as model errors, aliasing effect reduction or experimental implementation and testing of Kalman filter control loop on a simplified MCAO experimental set-up could be then discussed.
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-07-26
This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches.
Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems
Tseng, Chien-Hao; Lin, Sheng-Fuu; Jwo, Dah-Jing
2016-01-01
This paper presents a sensor fusion method based on the combination of cubature Kalman filter (CKF) and fuzzy logic adaptive system (FLAS) for the integrated navigation systems, such as the GPS/INS (Global Positioning System/inertial navigation system) integration. The third-degree spherical-radial cubature rule applied in the CKF has been employed to avoid the numerically instability in the system model. In processing navigation integration, the performance of nonlinear filter based estimation of the position and velocity states may severely degrade caused by modeling errors due to dynamics uncertainties of the vehicle. In order to resolve the shortcoming for selecting the process noise covariance through personal experience or numerical simulation, a scheme called the fuzzy adaptive cubature Kalman filter (FACKF) is presented by introducing the FLAS to adjust the weighting factor of the process noise covariance matrix. The FLAS is incorporated into the CKF framework as a mechanism for timely implementing the tuning of process noise covariance matrix based on the information of degree of divergence (DOD) parameter. The proposed FACKF algorithm shows promising accuracy improvement as compared to the extended Kalman filter (EKF), unscented Kalman filter (UKF), and CKF approaches. PMID:27472336
Extended Kalman Filter Based Neural Networks Controller For Hot Strip Rolling mill
Moussaoui, A. K.; Abbassi, H. A.; Bouazza, S.
2008-06-12
The present paper deals with the application of an Extended Kalman filter based adaptive Neural-Network control scheme to improve the performance of a hot strip rolling mill. The suggested Neural Network model was implemented using Bayesian Evidence based training algorithm. The control input was estimated iteratively by an on-line extended Kalman filter updating scheme basing on the inversion of the learned neural networks model. The performance of the controller is evaluated using an accurate model estimated from real rolling mill input/output data, and the usefulness of the suggested method is proved.
NASA Astrophysics Data System (ADS)
Ahrens, H.; Argin, F.; Klinkenbusch, L.
2013-07-01
The non-invasive and radiation-free imaging of the electrical activity of the heart with Electrocardiography (ECG) or Magnetocardiography (MCG) can be helpful for physicians for instance in the localization of the origin of cardiac arrhythmia. In this paper we compare two Kalman Filter algorithms for the solution of a nonlinear state-space model and for the subsequent imaging of the activation/depolarization times of the heart muscle: the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). The algorithms are compared for simulations of a (6×6) magnetometer array, a torso model with piecewise homogeneous conductivities, 946 current dipoles located in a small part of the heart (apex), and several noise levels. It is found that for all tested noise levels the convergence of the activation times is faster for the UKF.
Estimating Power System Dynamic States Using Extended Kalman Filter
Huang, Zhenyu; Schneider, Kevin P.; Nieplocha, Jaroslaw; Zhou, Ning
2014-10-31
Abstract—The state estimation tools which are currently deployed in power system control rooms are based on a steady state assumption. As a result, the suite of operational tools that rely on state estimation results as inputs do not have dynamic information available and their accuracy is compromised. This paper investigates the application of Extended Kalman Filtering techniques for estimating dynamic states in the state estimation process. The new formulated “dynamic state estimation” includes true system dynamics reflected in differential equations, not like previously proposed “dynamic state estimation” which only considers the time-variant snapshots based on steady state modeling. This new dynamic state estimation using Extended Kalman Filter has been successfully tested on a multi-machine system. Sensitivity studies with respect to noise levels, sampling rates, model errors, and parameter errors are presented as well to illustrate the robust performance of the developed dynamic state estimation process.
State estimation using parallel extended Kalman filters on nonlinear measurements
Sheives, T.C.
1980-01-01
The extended Kalman filter applied to state estimation using nonlinear measurements has demonstrated divergence in many applications involving large initial uncertainties because of invalid linearizations performed by the filter on the nonlinear measurement functions. No quantitative results can be found as to how invalid these linearizations must be before divergence occurs. An attempt is made to fill this void by deriving the extended Kalman filter as an approximate nonlinear least squares estimator, through the minimization of a measurement squared error function. The convergence of the extended Kalman filter is then determined by examining the squared error function and verifying the usefulness of this function through Monte Carlo simulation. The results of this analysis are then used to specify parameters for the Gaussian sum approximation. Simulation results for nonlinear measurements generated by harmonic and Gauss-Markov processes demonstrate that successful state estimation occurs using the Gaussian sum approximation, through proper parameter specification obtained by the convergence analysis. This technique is then applied to estimating the errors in an inertial navigation system through the use of nonlinear radar altimeter measurements and terrain elevation data. The results of applying this technique to terrain-aided navigation demonstrate that the Gaussian sum approximation provides good terrain-aided navigation performance for large initial uncertainties.
An Adaptive Kalman Filter Excisor for Suppressing Narrowband Interference
1993-11-01
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Training neural networks using sequential extended Kalman filtering
Plumer, E.S.
1995-03-01
Recent work has demonstrated the use of the extended Kalman filter (EKF) as an alternative to gradient-descent backpropagation when training multi-layer perceptrons. The EKF approach significantly improves convergence properties but at the cost of greater storage and computational complexity. Feldkamp et al. have described a decoupled version of the EKF which preserves the training advantages of the general EKF but which reduces the storage and computational requirements. This paper reviews the general and decoupled EKF approaches and presents sequentialized versions which provide further computational savings over the batch forms. The usefulness of the sequentialized EKF algorithms is demonstrated on a pattern classification problem.
Tracking whole hand kinematics using extended Kalman filter.
Fu, Qiushi; Santello, Marco
2010-01-01
This paper describes the general procedure, model construction, and experimental results of tracking whole hand kinematics using extended Kalman filter (EKF) based on data recorded from active surface markers. We used a hand model with 29 degrees of freedom that consists of hand global posture, wrist, and digits. The marker protocol had 4 markers on the distal forearm and 20 markers on the dorsal surface of the joints of the digits. To reduce computational load, we divided the state space into four sub-spaces, each of which were estimated with an EKF in a specific order. We tested our framework and found reasonably accurate results (2-4 mm tip position error) when sampling tip to tip pinch at 120 Hz.
An augmented extended Kalman filter algorithm for complex-valued recurrent neural networks.
Goh, Su Lee; Mandic, Danilo P
2007-04-01
An augmented complex-valued extended Kalman filter (ACEKF) algorithm for the class of nonlinear adaptive filters realized as fully connected recurrent neural networks is introduced. This is achieved based on some recent developments in the so-called augmented complex statistics and the use of general fully complex nonlinear activation functions within the neurons. This makes the ACEKF suitable for processing general complex-valued nonlinear and nonstationary signals and also bivariate signals with strong component correlations. Simulations on benchmark and real-world complex-valued signals support the approach.
Analysis and implementation of a neural extended Kalman filter for target tracking.
Kramer, Kathleen A; Stubberud, Stephen C
2006-02-01
Having a better motion model in the state estimator is one way to improve target tracking performance. Since the motion model of the target is not known a priori, either robust modeling techniques or adaptive modeling techniques are required. The neural extended Kalman filter is a technique that learns unmodeled dynamics while performing state estimation in the feedback loop of a control system. This coupled system performs the standard estimation of the states of the plant while estimating a function to approximate the difference between the given state-coupling function model and the behavior of the true plant dynamics. At each sample step, this new model is added to the existing model to improve the state estimate. The neural extended Kalman filter has also been investigated as a target tracking estimation routine. Implementation issues for this adaptive modeling technique, including neural network training parameters, were investigated and an analysis was made of the quality of performance that the technique can have for tracking maneuvering targets.
Estimating ice-affected streamflow by extended Kalman filtering
Holtschlag, D.J.; Grewal, M.S.
1998-01-01
An extended Kalman filter was developed to automate the real-time estimation of ice-affected streamflow on the basis of routine measurements of stream stage and air temperature and on the relation between stage and streamflow during open-water (ice-free) conditions. The filter accommodates three dynamic modes of ice effects: sudden formation/ablation, stable ice conditions, and eventual elimination. The utility of the filter was evaluated by applying it to historical data from two long-term streamflow-gauging stations, St. John River at Dickey, Maine and Platte River at North Bend, Nebr. Results indicate that the filter was stable and that parameters converged for both stations, producing streamflow estimates that are highly correlated with published values. For the Maine station, logarithms of estimated streamflows are within 8% of the logarithms of published values 87.2% of the time during periods of ice effects and within 15% 96.6% of the time. Similarly, for the Nebraska station, logarithms of estimated streamflows are within 8% of the logarithms of published values 90.7% of the time and within 15% 97.7% of the time. In addition, the correlation between temporal updates and published streamflows on days of direct measurements at the Maine station was 0.777 and 0.998 for ice-affected and open-water periods, respectively; for the Nebraska station, corresponding correlations were 0.864 and 0.997.
Estimating short-period dynamics using an extended Kalman filter
NASA Technical Reports Server (NTRS)
Bauer, Jeffrey E.; Andrisani, Dominick
1990-01-01
An extended Kalman filter (EKF) is used to estimate the parameters of a low-order model from aircraft transient response data. The low-order model is a state space model derived from the short-period approximation of the longitudinal aircraft dynamics. The model corresponds to the pitch rate to stick force transfer function currently used in flying qualities analysis. Because of the model chosen, handling qualities information is also obtained. The parameters are estimated from flight data as well as from a six-degree-of-freedom, nonlinear simulation of the aircraft. These two estimates are then compared and the discrepancies noted. The low-order model is able to satisfactorily match both flight data and simulation data from a high-order computer simulation. The parameters obtained from the EKF analysis of flight data are compared to those obtained using frequency response analysis of the flight data. Time delays and damping ratios are compared and are in agreement. This technique demonstrates the potential to determine, in near real time, the extent of differences between computer models and the actual aircraft. Precise knowledge of these differences can help to determine the flying qualities of a test aircraft and lead to more efficient envelope expansion.
Application of Consider Covariance to the Extended Kalman Filter
NASA Technical Reports Server (NTRS)
Lundberg, John B.
1996-01-01
The extended Kalman filter (EKF) is the basis for many applications of filtering theory to real-time problems where estimates of the state of a dynamical system are to be computed based upon some set of observations. The form of the EKF may vary somewhat from one application to another, but the fundamental principles are typically unchanged among these various applications. As is the case in many filtering applications, models of the dynamical system (differential equations describing the state variables) and models of the relationship between the observations and the state variables are created. These models typically employ a set of constants whose values are established my means of theory or experimental procedure. Since the estimates of the state are formed assuming that the models are perfect, any modeling errors will affect the accuracy of the computed estimates. Note that the modeling errors may be errors of commission (errors in terms included in the model) or omission (errors in terms excluded from the model). Consequently, it becomes imperative when evaluating the performance of real-time filters to evaluate the effect of modeling errors on the estimates of the state.
Identifying Bearing Rotodynamic Coefficients Using an Extended Kalman Filter
NASA Technical Reports Server (NTRS)
Miller, Brad A.; Howard, Samuel A.
2008-01-01
An Extended Kalman Filter is developed to estimate the linearized direct and indirect stiffness and damping force coefficients for bearings in rotor dynamic applications from noisy measurements of the shaft displacement in response to imbalance and impact excitation. The bearing properties are modeled as stochastic random variables using a Gauss-Markov model. Noise terms are introduced into the system model to account for all of the estimation error, including modeling errors and uncertainties and the propagation of measurement errors into the parameter estimates. The system model contains two user-defined parameters that can be tuned to improve the filter's performance; these parameters correspond to the covariance of the system and measurement noise variables. The filter is also strongly influenced by the initial values of the states and the error covariance matrix. The filter is demonstrated using numerically simulated data for a rotor bearing system with two identical bearings, which reduces the number of unknown linear dynamic coefficients to eight. The filter estimates for the direct damping coefficients and all four stiffness coefficients correlated well with actual values, whereas the estimates for the cross-coupled damping coefficients were the least accurate.
Identifying Bearing Rotordynamic Coefficients using an Extended Kalman Filter
NASA Technical Reports Server (NTRS)
Miller, Bard A.; Howard, Samuel A.
2008-01-01
An Extended Kalman Filter is developed to estimate the linearized direct and indirect stiffness and damping force coefficients for bearings in rotor-dynamic applications from noisy measurements of the shaft displacement in response to imbalance and impact excitation. The bearing properties are modeled as stochastic random variables using a Gauss-Markov model. Noise terms are introduced into the system model to account for all of the estimation error, including modeling errors and uncertainties and the propagation of measurement errors into the parameter estimates. The system model contains two user-defined parameters that can be tuned to improve the filter s performance; these parameters correspond to the covariance of the system and measurement noise variables. The filter is also strongly influenced by the initial values of the states and the error covariance matrix. The filter is demonstrated using numerically simulated data for a rotor-bearing system with two identical bearings, which reduces the number of unknown linear dynamic coefficients to eight. The filter estimates for the direct damping coefficients and all four stiffness coefficients correlated well with actual values, whereas the estimates for the cross-coupled damping coefficients were the least accurate.
Streamflow data assimilation in SWAT model using Extended Kalman Filter
NASA Astrophysics Data System (ADS)
Sun, Leqiang; Nistor, Ioan; Seidou, Ousmane
2015-12-01
The Extended Kalman Filter (EKF) is coupled with the Soil and Water Assessment Tools (SWAT) model in the streamflow assimilation of the upstream Senegal River in West Africa. Given the large number of distributed variables in SWAT, only the average watershed scale variables are included in the state vector and the Hydrological Response Unit (HRU) scale variables are updated with the a posteriori/a priori ratio of their watershed scale counterparts. The Jacobian matrix is calculated numerically by perturbing the state variables. Both the soil moisture and CN2 are significantly updated in the wet season, yet they have opposite update patterns. A case study for a large flood forecast shows that for up to seven days, the streamflow forecast is moderately improved using the EKF-subsequent open loop scheme but significantly improved with a newly designed quasi-error update scheme. The former has better performances in the flood rising period while the latter has better performances in the recession period. For both schemes, the streamflow forecast is improved more significantly when the lead time is shorter.
An extended Kalman filter for spinning spacecraft attitude estimation
NASA Technical Reports Server (NTRS)
Baker, David F.
1991-01-01
An extended Kalman filter for real-time ground attitude estimation of a gyro-less spinning spacecraft was developed and tested. The filter state vector includes the angular momentum direction, phase angle, inertial nutation angle, and inertial and body nutation rates. The filter solves for the nutating three-axis attitude and accounts for effects due to principle axes offset from the body axes. The attitude is propagated using the kinematics of a rigid body symmetric about the principle spin axis; disturbance torques are assumed to be small. Filter updates consist only of the measured angles between celestial objects (Sun, Earth, etc.) and the nominal spin axis, and the times these angles were measured. Both simulated data and real data from the Dynamics Explorer -A (DE-A) spacecraft were used to test the filter; the results are presented. Convergence was achieved rapidly from a wide range of a priori state estimates, and sub-degree accuracy was attained. Systematic errors affecting the solution accuracy are discussed, as are the results of an attempt to solve for sensor measurement angle biases in the state vector.
Objects tracking with adaptive correlation filters and Kalman filtering
NASA Astrophysics Data System (ADS)
Ontiveros-Gallardo, Sergio E.; Kober, Vitaly
2015-09-01
Object tracking is commonly used for applications such as video surveillance, motion based recognition, and vehicle navigation. In this work, a tracking system using adaptive correlation filters and robust Kalman prediction of target locations is proposed. Tracking is performed by means of multiple object detections in reduced frame areas. A bank of filters is designed from multiple views of a target using synthetic discriminant functions. An adaptive approach is used to improve discrimination capability of the synthesized filters adapting them to multiple types of backgrounds. With the help of computer simulation, the performance of the proposed algorithm is evaluated in terms of detection efficiency and accuracy of object tracking.
A Kalman filter approach to adaptive estimation of multispectral signatures
NASA Technical Reports Server (NTRS)
Crane, R. B.
1973-01-01
The signatures of remote sensing data from agricultural crops exhibit significant non-stationarity, so that the performance of fixed parameter classifiers degenerates with time and distance from the initial training data. A class of adaptive decision-directed classifiers are being developed, based on Kalman filter theory. Limited results to date on two data sets indicate approximately a 25 to 40% reduction in rates of misclassification.
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-01-01
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix ‘R’ and the system noise V-C matrix ‘Q’. Then, the global filter uses R to calculate the information allocation factor ‘β’ for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively. PMID:27438835
The Joint Adaptive Kalman Filter (JAKF) for Vehicle Motion State Estimation.
Gao, Siwei; Liu, Yanheng; Wang, Jian; Deng, Weiwen; Oh, Heekuck
2016-07-16
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local filters, which aims to adaptively adjust the measurement noise variance-covariance (V-C) matrix 'R' and the system noise V-C matrix 'Q'. Then, the global filter uses R to calculate the information allocation factor 'β' for data fusion. Finally, the global filter completes optimal data fusion and feeds back to the local filters to improve the measurement accuracy of the local filters. Extensive simulation and experimental results show that the JAKF has better adaptive ability and fault tolerance. JAKF enables one to bridge the gap of the accuracy difference of various sensors to improve the integral filtering effectivity. If any sensor breaks down, the filtered results of JAKF still can maintain a stable convergence rate. Moreover, the JAKF outperforms the conventional Kalman filter (CKF) and the innovation-based adaptive Kalman filter (IAKF) with respect to the accuracy of displacement, velocity, and acceleration, respectively.
Extended Kalman filtering applied to a two-axis robotic arm with flexible links
Lertpiriyasuwat, V.; Berg, M.C.; Buffinton, K.W.
2000-03-01
An industrial robot today uses measurements of its joint positions and models of its kinematics and dynamics to estimate and control its end-effector position. Substantially better end-effector position estimation and control performance would be obtainable if direct measurements of its end-effector position were also used. The subject of this paper is extended Kalman filtering for precise estimation of the position of the end-effector of a robot using, in addition to the usual measurements of the joint positions, direct measurements of the end-effector position. The estimation performances of extended Kalman filters are compared in applications to a planar two-axis robotic arm with very flexible links. The comparisons shed new light on the dependence of extended Kalman filter estimation performance on the quality of the model of the arm dynamics that the extended Kalman filter operates with.
Extended Kalman filter based structural damage detection for MR damper controlled structures
NASA Astrophysics Data System (ADS)
Jin, Chenhao; Jang, Shinae; Sun, Xiaorong; Jiang, Zhaoshuo; Christenson, Richard
2016-04-01
The Magneto-rheological (MR) dampers have been widely used in many building and bridge structures against earthquake and wind loadings due to its advantages including mechanical simplicity, high dynamic range, low power requirements, large force capacity, and robustness. However, research about structural damage detection methods for MR damper controlled structures is limited. This paper aims to develop a real-time structural damage detection method for MR damper controlled structures. A novel state space model of MR damper controlled structure is first built by combining the structure's equation of motion and MR damper's hyperbolic tangent model. In this way, the state parameters of both the structure and MR damper are added in the state vector of the state space model. Extended Kalman filter is then used to provide prediction for state variables from measurement data. The two techniques are synergistically combined to identify parameters and track the changes of both structure and MR damper in real time. The proposed method is tested using response data of a three-floor MR damper controlled linear building structure under earthquake excitation. The testing results show that the adaptive extended Kalman filter based approach is capable to estimate not only structural parameters such as stiffness and damping of each floor, but also the parameters of MR damper, so that more insights and understanding of the damage can be obtained. The developed method also demonstrates high damage detection accuracy and light computation, as well as the potential to implement in a structural health monitoring system.
Extended Kalman filtering for continuous volumetric MR-temperature imaging.
Denis de Senneville, Baudouin; Roujol, Sébastien; Hey, Silke; Moonen, Chrit; Ries, Mario
2013-04-01
Real time magnetic resonance (MR) thermometry has evolved into the method of choice for the guidance of high-intensity focused ultrasound (HIFU) interventions. For this role, MR-thermometry should preferably have a high temporal and spatial resolution and allow observing the temperature over the entire targeted area and its vicinity with a high accuracy. In addition, the precision of real time MR-thermometry for therapy guidance is generally limited by the available signal-to-noise ratio (SNR) and the influence of physiological noise. MR-guided HIFU would benefit of the large coverage volumetric temperature maps, including characterization of volumetric heating trajectories as well as near- and far-field heating. In this paper, continuous volumetric MR-temperature monitoring was obtained as follows. The targeted area was continuously scanned during the heating process by a multi-slice sequence. Measured data and a priori knowledge of 3-D data derived from a forecast based on a physical model were combined using an extended Kalman filter (EKF). The proposed reconstruction improved the temperature measurement resolution and precision while maintaining guaranteed output accuracy. The method was evaluated experimentally ex vivo on a phantom, and in vivo on a porcine kidney, using HIFU heating. On the in vivo experiment, it allowed the reconstruction from a spatio-temporally under-sampled data set (with an update rate for each voxel of 1.143 s) to a 3-D dataset covering a field of view of 142.5×285×54 mm(3) with a voxel size of 3×3×6 mm(3) and a temporal resolution of 0.127 s. The method also provided noise reduction, while having a minimal impact on accuracy and latency.
Moment estimation for chemically reacting systems by extended Kalman filtering.
Ruess, J; Milias-Argeitis, A; Summers, S; Lygeros, J
2011-10-28
In stochastic models of chemically reacting systems that contain bimolecular reactions, the dynamics of the moments of order up to n of the species populations do not form a closed system, in the sense that their time-derivatives depend on moments of order n + 1. To close the dynamics, the moments of order n + 1 are generally approximated by nonlinear functions of the lower order moments. If the molecule counts of some of the species have a high probability of becoming zero, such approximations may lead to imprecise results and stochastic simulation is the only viable alternative for system analysis. Stochastic simulation can produce exact realizations of chemically reacting systems, but tends to become computationally expensive, especially for stiff systems that involve reactions at different time scales. Further, in some systems, important stochastic events can be very rare and many simulations are necessary to obtain accurate estimates. The computational cost of stochastic simulation can then be prohibitively large. In this paper, we propose a novel method for estimating the moments of chemically reacting systems. The method is based on closing the moment dynamics by replacing the moments of order n + 1 by estimates calculated from a small number of stochastic simulation runs. The resulting stochastic system is then used in an extended Kalman filter, where estimates of the moments of order up to n, obtained from the same simulation, serve as outputs of the system. While the initial motivation for the method was improving over the performance of stochastic simulation and moment closure methods, we also demonstrate that it can be used in an experimental setting to estimate moments of species that cannot be measured directly from time course measurements of the moments of other species.
Comparison of Sigma-Point and Extended Kalman Filters on a Realistic Orbit Determination Scenario
NASA Technical Reports Server (NTRS)
Gaebler, John; Hur-Diaz. Sun; Carpenter, Russell
2010-01-01
Sigma-point filters have received a lot of attention in recent years as a better alternative to extended Kalman filters for highly nonlinear problems. In this paper, we compare the performance of the additive divided difference sigma-point filter to the extended Kalman filter when applied to orbit determination of a realistic operational scenario based on the Interstellar Boundary Explorer mission. For the scenario studied, both filters provided equivalent results. The performance of each is discussed in detail.
Adaptive probabilistic collocation based Kalman filter for unsaturated flow problem
NASA Astrophysics Data System (ADS)
Man, J.; Li, W.; Zeng, L.; Wu, L.
2015-12-01
The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a relatively large ensemble size is usually required to guarantee the accuracy. As an alternative approach, the probabilistic collocation based Kalman filter (PCKF) employs the Polynomial Chaos to approximate the original system. In this way, the sampling error can be reduced. However, PCKF suffers from the so called "cure of dimensionality". When the system nonlinearity is strong and number of parameters is large, PCKF is even more computationally expensive than EnKF. Motivated by recent developments in uncertainty quantification, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problem. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected. The "restart" technology is used to alleviate the inconsistency between model parameters and states. The performance of RAPCKF is tested by unsaturated flow numerical cases. It is shown that RAPCKF is more efficient than EnKF with the same computational cost. Compared with the traditional PCKF, the RAPCKF is more applicable in strongly nonlinear and high dimensional problems.
NASA Technical Reports Server (NTRS)
Ledsham, W. H.; Staelin, D. H.
1978-01-01
An extended Kalman-Bucy filter has been implemented for atmospheric temperature profile retrievals from observations made using the Scanned Microwave Spectrometer (SCAMS) instrument carried on the Nimbus 6 satellite. This filter has the advantage that it requires neither stationary statistics in the underlying processes nor linear production of the observed variables from the variables to be estimated. This extended Kalman-Bucy filter has yielded significant performance improvement relative to multiple regression retrieval methods. A multi-spot extended Kalman-Bucy filter has also been developed in which the temperature profiles at a number of scan angles in a scanning instrument are retrieved simultaneously. These multi-spot retrievals are shown to outperform the single-spot Kalman retrievals.
The extended Kalman filter for forecast of algal bloom dynamics.
Mao, J Q; Lee, Joseph H W; Choi, K W
2009-09-01
A deterministic ecosystem model is combined with an extended Kalman filter (EKF) to produce short term forecasts of algal bloom and dissolved oxygen dynamics in a marine fish culture zone (FCZ). The weakly flushed FCZ is modelled as a well-mixed system; the tidal exchange with the outer bay is lumped into a flushing rate that is numerically determined from a three-dimensional hydrodynamic model. The ecosystem model incorporates phytoplankton growth kinetics, nutrient uptake, photosynthetic production, nutrient sources from organic fish farm loads, and nutrient exchange with a sediment bed layer. High frequency field observations of chlorophyll, dissolved oxygen (DO) and hydro-meteorological parameters (sampling interval Deltat=1 day, 2h, 1h, respectively) and bi-weekly nutrient data are assimilated into the model to produce the combined state estimate accounting for the uncertainties. In addition to the water quality state variables, the EKF incorporates dynamic estimation of algal growth rate and settling velocity. The effectiveness of the EKF data assimilation is studied for a wide range of sampling intervals and prediction lead-times. The chlorophyll and dissolved oxygen estimated by the EKF are compared with field data of seven algal bloom events observed at Lamma Island, Hong Kong. The results show that the EKF estimate well captures the nonlinear error evolution in time; the chlorophyll level can be satisfactorily predicted by the filtered model estimate with a mean absolute error of around 1-2 microg/L. Predictions with 1-2 day lead-time are highly correlated with the observations (r=0.7-0.9); the correlation stays at a high level for a lead-time of 3 days (r=0.6-0.7). Estimated algal growth and settling rates are in accord with field observations; the more frequent DO data can compensate for less frequent algal biomass measurements. The present study is the first time the EKF is successfully applied to forecast an entire algal bloom cycle, suggesting the
Recursive starlight and bias estimation for high-contrast imaging with an extended Kalman filter
NASA Astrophysics Data System (ADS)
Riggs, A. J. Eldorado; Kasdin, N. Jeremy; Groff, Tyler D.
2016-01-01
For imaging faint exoplanets and disks, a coronagraph-equipped observatory needs focal plane wavefront correction to recover high contrast. The most efficient correction methods iteratively estimate the stellar electric field and suppress it with active optics. The estimation requires several images from the science camera per iteration. To maximize the science yield, it is desirable both to have fast wavefront correction and to utilize all the correction images for science target detection. Exoplanets and disks are incoherent with their stars, so a nonlinear estimator is required to estimate both the incoherent intensity and the stellar electric field. Such techniques assume a high level of stability found only on space-based observatories and possibly ground-based telescopes with extreme adaptive optics. In this paper, we implement a nonlinear estimator, the iterated extended Kalman filter (IEKF), to enable fast wavefront correction and a recursive, nearly-optimal estimate of the incoherent light. In Princeton's High Contrast Imaging Laboratory, we demonstrate that the IEKF allows wavefront correction at least as fast as with a Kalman filter and provides the most accurate detection of a faint companion. The nonlinear IEKF formalism allows us to pursue other strategies such as parameter estimation to improve wavefront correction.
An adaptive Kalman filter for ECG signal enhancement.
Vullings, Rik; de Vries, Bert; Bergmans, Jan W M
2011-04-01
The ongoing trend of ECG monitoring techniques to become more ambulatory and less obtrusive generally comes at the expense of decreased signal quality. To enhance this quality, consecutive ECG complexes can be averaged triggered on the heartbeat, exploiting the quasi-periodicity of the ECG. However, this averaging constitutes a tradeoff between improvement of the SNR and loss of clinically relevant physiological signal dynamics. Using a bayesian framework, in this paper, a sequential averaging filter is developed that, in essence, adaptively varies the number of complexes included in the averaging based on the characteristics of the ECG signal. The filter has the form of an adaptive Kalman filter. The adaptive estimation of the process and measurement noise covariances is performed by maximizing the bayesian evidence function of the sequential ECG estimation and by exploiting the spatial correlation between several simultaneously recorded ECG signals, respectively. The noise covariance estimates thus obtained render the filter capable of ascribing more weight to newly arriving data when these data contain morphological variability, and of reducing this weight in cases of no morphological variability. The filter is evaluated by applying it to a variety of ECG signals. To gauge the relevance of the adaptive noise-covariance estimation, the performance of the filter is compared to that of a Kalman filter with fixed, (a posteriori) optimized noise covariance. This comparison demonstrates that, without using a priori knowledge on signal characteristics, the filter with adaptive noise estimation performs similar to the filter with optimized fixed noise covariance, favoring the adaptive filter in cases where no a priori information is available or where signal characteristics are expected to fluctuate.
Algorithme d'adaptation du filtre de Kalman aux variations soudaines de bruit
NASA Astrophysics Data System (ADS)
Canciu, Vintila
This research targets the case of Kalman filtering as applied to linear time-invariant systems having unknown process noise covariance and measurement noise covariance matrices and addresses the problem represented by the incomplete a priori knowledge of these two filter initialization parameters. The goal of this research is to determine in realtime both the process covariance matrix and the noise covariance matrix in the context of adaptive Kalman filtering. The resultant filter, called evolutionary adaptive Kalman filter, is able to adapt to sudden noise variations and constitutes a hybrid solution for adaptive Kalman filtering based on metaheuristic algorithms. MATLAB/Simulink simulation using several processes and covariance matrices plus comparison with other filters was selected as validation method. The Cramer-Rae Lower Bound (CRLB) was used as performance criterion. The thesis begins with a description of the problem under consideration (the design of a Kalman filter that is able to adapt to sudden noise variations) followed by a typical application (INS-GPS integrated navigation system) and by a statistical analysis of publications related to adaptive Kalman filtering. Next, the thesis presents the current architectures of the adaptive Kalman filtering: the innovation adaptive estimator (IAE) and the multiple model adaptive estimator (MMAE). It briefly presents their formulation, their behavior, and the limit of their performances. The thesis continues with the architectural synthesis of the evolutionary adaptive Kalman filter. The steps involved in the solution of the problem under consideration is also presented: an analysis of Kalman filtering and sub-optimal filtering methods, a comparison of current adaptive Kalman and sub-optimal filtering methods, the emergence of evolutionary adaptive Kalman filter as an enrichment of sub-optimal filtering with the help of biological-inspired computational intelligence methods, and the step-by-step architectural
Investigation of Adaptive Robust Kalman Filtering Algorithms for GPS/DR Navigation System Filters
NASA Astrophysics Data System (ADS)
Elzoghby, MOSTAFA; Arif, USMAN; Li, FU; Zhi Yu, XI
2017-03-01
The conventional Kalman filter (KF) algorithm is suitable if the characteristic noise covariance for states as well as measurements is readily known but in most cases these are unknown. Similarly robustness is required instead of smoothing if states are changing abruptly. Such an adaptive as well as robust Kalman filter is vital for many real time applications, like target tracking and navigating aerial vehicles. A number of adaptive as well as robust Kalman filtering methods are available in the literature. In order to investigate the performance of some of these methods, we have selected three different Kalman filters, namely Sage Husa KF, Modified Adaptive Robust KF and Adaptively Robust KF, which are easily simulate able as well as implementable for real time applications. These methods are simulated for land based vehicle and the results are compared with conventional Kalman filter. Results show that the Modified Adaptive Robust KF is best amongst the selected methods and can be used for Navigation applications.
Adaptive distributed Kalman filtering with wind estimation for astronomical adaptive optics.
Massioni, Paolo; Gilles, Luc; Ellerbroek, Brent
2015-12-01
In the framework of adaptive optics (AO) for astronomy, it is a common assumption to consider the atmospheric turbulent layers as "frozen flows" sliding according to the wind velocity profile. For this reason, having knowledge of such a velocity profile is beneficial in terms of AO control system performance. In this paper we show that it is possible to exploit the phase estimate from a Kalman filter running on an AO system in order to estimate wind velocity. This allows the update of the Kalman filter itself with such knowledge, making it adaptive. We have implemented such an adaptive controller based on the distributed version of the Kalman filter, for a realistic simulation of a multi-conjugate AO system with laser guide stars on a 30 m telescope. Simulation results show that this approach is effective and promising and the additional computational cost with respect to the distributed filter is negligible. Comparisons with a previously published slope detection and ranging wind profiler are made and the impact of turbulence profile quantization is assessed. One of the main findings of the paper is that all flavors of the adaptive distributed Kalman filter are impacted more significantly by turbulence profile quantization than the static minimum mean square estimator which does not incorporate wind profile information.
A Hybrid Extended Kalman Filter as an Observer for a Pot-Electro-Magnetic Actuator
NASA Astrophysics Data System (ADS)
Schmidt, Simon; Mercorelli, Paolo
2017-01-01
This paper deals with an application in which a hybrid extended Kalman Filter (HEKF) is used to estimate state variables in a U-shaped electro-magnetic actuator to be used in mechanical systems. In this context a hybrid Kalman Filter is the one which switches between different models. The paper proposes a hybrid model for an extended Kalman Filter to be used as an observer to estimate the state and to control the force of the actuator. Applications include position, velocity and force control in automotive, engine and manufacturing systems. This work is focused on the estimation of state variables of the actuator. Simulated results show the effectiveness of the proposed approach.
Kalman filtering to suppress spurious signals in adaptive optics control.
Poyneer, Lisa A; Véran, Jean-Pierre
2010-11-01
In many scenarios, an adaptive optics (AO) control system operates in the presence of temporally non-white noise. We use a Kalman filter with a state space formulation that allows suppression of this colored noise, hence improving residual error over the case where the noise is assumed to be white. We demonstrate the effectiveness of this new filter in the case of the estimated Gemini Planet Imager tip-tilt environment, where there are both common-path and non-common-path vibrations. We discuss how this same framework can also be used to suppress spatial aliasing during predictive wavefront control assuming frozen flow in a low-order AO system without a spatially filtered wavefront sensor, and present experimental measurements from Altair that clearly reveal these aliased components.
Kalman filtering to suppress spurious signals in Adaptive Optics control
Poyneer, L; Veran, J P
2010-03-29
In many scenarios, an Adaptive Optics (AO) control system operates in the presence of temporally non-white noise. We use a Kalman filter with a state space formulation that allows suppression of this colored noise, hence improving residual error over the case where the noise is assumed to be white. We demonstrate the effectiveness of this new filter in the case of the estimated Gemini Planet Imager tip-tilt environment, where there are both common-path and non-common path vibrations. We discuss how this same framework can also be used to suppress spatial aliasing during predictive wavefront control assuming frozen flow in a low-order AO system without a spatially filtered wavefront sensor, and present experimental measurements from Altair that clearly reveal these aliased components.
NASA Technical Reports Server (NTRS)
Lisano, M. E.
2003-01-01
This paper describes the design and initial test results of an extended Kalman filter that has been developed at Jet Propulsion Laboratory (JPL) for post-flight reconstruction of the trajectory and attitude history of a spacecraft entering a planetary atmosphere and descending upon a parachute.
Spacecraft Attitude Estimation Integrating the Q-Method into an Extended Kalman Filter
NASA Astrophysics Data System (ADS)
Ainscough, Thomas G.
A new algorithm is proposed that smoothly integrates the nonlinear estimation of the attitude quaternion using Davenport's q-method and the estimation of non-attitude states within the framework of an extended Kalman filter. A modification to the q-method and associated covariance analysis is derived with the inclusion of an a priori attitude estimate. The non-attitude states are updated from the nonlinear attitude estimate based on linear optimal Kalman filter techniques. The proposed filter is compared to existing methods and is shown to be equivalent to second-order in the attitude update and exactly equivalent in the non-attitude state update with the Sequential Optimal Attitude Recursion filter. Monte Carlo analysis is used in numerical simulations to demonstrate the validity of the proposed approach. This filter successfully estimates the nonlinear attitude and non-attitude states in a single Kalman filter without the need for iterations.
Model-Based Engine Control Architecture with an Extended Kalman Filter
NASA Technical Reports Server (NTRS)
Csank, Jeffrey T.; Connolly, Joseph W.
2016-01-01
This paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The nonlinear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.
Model-Based Engine Control Architecture with an Extended Kalman Filter
NASA Technical Reports Server (NTRS)
Csank, Jeffrey T.; Connolly, Joseph W.
2016-01-01
This paper discusses the design and implementation of an extended Kalman filter (EKF) for model-based engine control (MBEC). Previously proposed MBEC architectures feature an optimal tuner Kalman Filter (OTKF) to produce estimates of both unmeasured engine parameters and estimates for the health of the engine. The success of this approach relies on the accuracy of the linear model and the ability of the optimal tuner to update its tuner estimates based on only a few sensors. Advances in computer processing are making it possible to replace the piece-wise linear model, developed off-line, with an on-board nonlinear model running in real-time. This will reduce the estimation errors associated with the linearization process, and is typically referred to as an extended Kalman filter. The non-linear extended Kalman filter approach is applied to the Commercial Modular Aero-Propulsion System Simulation 40,000 (C-MAPSS40k) and compared to the previously proposed MBEC architecture. The results show that the EKF reduces the estimation error, especially during transient operation.
Lall, Pradeep; Wei, Junchao; Davis, J Lynn
2014-06-24
Abstract— Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time causing lumen depreciation. The underlying TM-21 Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, Kalman Filter (KF) and Extended Kalman Filters (EKF) have been used to develop a 70-percent Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. Life prediction of L70 life for the LEDs used in SSL luminaires from KF and EKF based models have
NASA Astrophysics Data System (ADS)
Roffel, A. J.; Narasimhan, S.
2014-11-01
Pendulum tuned mass dampers (PTMDs) have been employed in several full-scale applications to attenuate excessive structural motions, which are mostly due to wind. Conducting periodic condition assessments of the devices to ascertain their health is necessary to ensure their continued optimal performance, which involves identifying the modal parameters of the underlying (bare) structure to which they are tuned to. Such an identification is also necessary for the design of control systems such as adaptive tuned mass dampers. Existing methods of arresting the motion of the damper to estimate the modal properties are expensive, time-consuming, and not suitable for online tuning. Instead, in this paper, parameter estimation using the Extended Kalman Filter (EKF) is proposed to undertake this task. The central task accomplished here is to estimate the dynamic characteristics of the bare structure (structure without the PTMD) from response measurements of the coupled main structure and PTMD system. The proposed methodology relies on ambient acceleration measurements of TMD-attenuated responses to estimate the bare structural modal frequencies, damping, and mode shapes, which can then be used either for condition assessment or for control. The application of EKF to modal parameter estimation is not new. However, a methodology to address the problem in wind engineering arising from stochastic disturbances present in both the measurement and state equations, and unknown process and noise covariances arising due to ambient excitations, is presented for the first time. Extensively studied for synthetic data, these two challenges have limited thus far the application of Kalman filtering to practical wind engineering parameter estimation problems using experimentally obtained measurements. In this paper, a detailed methodology is presented to address these challenges by using a modified form of the standard EKF equations, together with an algorithm to estimate the unknown
Fixed Interval Smoothing Algorithm for an Extended Kalman Filter for Over-the-Horizon Ship Tracking
1989-03-01
accurate than an estimate based only on the estimates up to time k, (.k,). Meditch [Ref. 10: p. 193] categorizes smoothing algorithms into three...NAVAL POSTGRADUATE SCHOOL Monterey, California DTIC ELECTE A JUN0 89 * TEESIS In * FIXED INTERVAL SMOOTHING ALGORITHM FOR A\\ EXTENDED KALMAN 0 FILTER...track a maneuvering surface target using HFDF lines-of-bearing is sub -tantially improved by implementing a fixed interval smoothing algorithm and a
Hybrid vs Adaptive Ensemble Kalman Filtering for Storm Surge Forecasting
NASA Astrophysics Data System (ADS)
Altaf, M. U.; Raboudi, N.; Gharamti, M. E.; Dawson, C.; McCabe, M. F.; Hoteit, I.
2014-12-01
Recent storm surge events due to Hurricanes in the Gulf of Mexico have motivated the efforts to accurately forecast water levels. Toward this goal, a parallel architecture has been implemented based on a high resolution storm surge model, ADCIRC. However the accuracy of the model notably depends on the quality and the recentness of the input data (mainly winds and bathymetry), model parameters (e.g. wind and bottom drag coefficients), and the resolution of the model grid. Given all these uncertainties in the system, the challenge is to build an efficient prediction system capable of providing accurate forecasts enough ahead of time for the authorities to evacuate the areas at risk. We have developed an ensemble-based data assimilation system to frequently assimilate available data into the ADCIRC model in order to improve the accuracy of the model. In this contribution we study and analyze the performances of different ensemble Kalman filter methodologies for efficient short-range storm surge forecasting, the aim being to produce the most accurate forecasts at the lowest possible computing time. Using Hurricane Ike meteorological data to force the ADCIRC model over a domain including the Gulf of Mexico coastline, we implement and compare the forecasts of the standard EnKF, the hybrid EnKF and an adaptive EnKF. The last two schemes have been introduced as efficient tools for enhancing the behavior of the EnKF when implemented with small ensembles by exploiting information from a static background covariance matrix. Covariance inflation and localization are implemented in all these filters. Our results suggest that both the hybrid and the adaptive approach provide significantly better forecasts than those resulting from the standard EnKF, even when implemented with much smaller ensembles.
Adaptive Kalman filtering for real-time mapping of the visual field.
Ward, B Douglas; Janik, John; Mazaheri, Yousef; Ma, Yan; DeYoe, Edgar A
2012-02-15
This paper demonstrates the feasibility of real-time mapping of the visual field for clinical applications. Specifically, three aspects of this problem were considered: (1) experimental design, (2) statistical analysis, and (3) display of results. Proper experimental design is essential to achieving a successful outcome, particularly for real-time applications. A random-block experimental design was shown to have less sensitivity to measurement noise, as well as greater robustness to error in modeling of the hemodynamic impulse response function (IRF) and greater flexibility than common alternatives. In addition, random encoding of the visual field allows for the detection of voxels that are responsive to multiple, not necessarily contiguous, regions of the visual field. Due to its recursive nature, the Kalman filter is ideally suited for real-time statistical analysis of visual field mapping data. An important feature of the Kalman filter is that it can be used for nonstationary time series analysis. The capability of the Kalman filter to adapt, in real time, to abrupt changes in the baseline arising from subject motion inside the scanner and other external system disturbances is important for the success of clinical applications. The clinician needs real-time information to evaluate the success or failure of the imaging run and to decide whether to extend, modify, or terminate the run. Accordingly, the analytical software provides real-time displays of (1) brain activation maps for each stimulus segment, (2) voxel-wise spatial tuning profiles, (3) time plots of the variability of response parameters, and (4) time plots of activated volume.
NASA Technical Reports Server (NTRS)
Kelly, D. A.; Fermelia, A.; Lee, G. K. F.
1990-01-01
An adaptive Kalman filter design that utilizes recursive maximum likelihood parameter identification is discussed. At the center of this design is the Kalman filter itself, which has the responsibility for attitude determination. At the same time, the identification algorithm is continually identifying the system parameters. The approach is applicable to nonlinear, as well as linear systems. This adaptive Kalman filter design has much potential for real time implementation, especially considering the fast clock speeds, cache memory and internal RAM available today. The recursive maximum likelihood algorithm is discussed in detail, with special attention directed towards its unique matrix formulation. The procedure for using the algorithm is described along with comments on how this algorithm interacts with the Kalman filter.
Study on GPS attitude determination system aided INS using adaptive Kalman filter
NASA Astrophysics Data System (ADS)
Bian, Hongwei; Jin, Zhihua; Tian, Weifeng
2005-10-01
A marine INS/GPS (inertial navigation system/global positioning system) adaptive navigation system is presented in this paper. The GPS with two antennae providing vessel attitude is selected as the auxiliary system to fuse with INS. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The conventional Kalman filter (CKF) assumes that the statistics of the noise of each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However, the GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce a fuzzy logic control method into innovation-based adaptive estimation Kalman filtering (IAE-AKF) algorithm, and accordingly propose various adaptive Kalman filters. However, how to design the fuzzy logic controller is a very complicated problem, which is still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAE-AKF algorithm theoretically in detail, the approach is tested in the developed INS/GPS integrated marine navigation system. Real field test results show that the adaptive Kalman filter outperforms the CKF with higher accuracy and robustness. It is demonstrated that this proposed approach is a valid solution for the unknown changing measurement noise existing in the Kalman filter.
A cognition-based method to ease the computational load for an extended Kalman filter.
Li, Yanpeng; Li, Xiang; Deng, Bin; Wang, Hongqiang; Qin, Yuliang
2014-12-03
The extended Kalman filter (EKF) is the nonlinear model of a Kalman filter (KF). It is a useful parameter estimation method when the observation model and/or the state transition model is not a linear function. However, the computational requirements in EKF are a difficulty for the system. With the help of cognition-based designation and the Taylor expansion method, a novel algorithm is proposed to ease the computational load for EKF in azimuth predicting and localizing under a nonlinear observation model. When there are nonlinear functions and inverse calculations for matrices, this method makes use of the major components (according to current performance and the performance requirements) in the Taylor expansion. As a result, the computational load is greatly lowered and the performance is ensured. Simulation results show that the proposed measure will deliver filtering output with a similar precision compared to the regular EKF. At the same time, the computational load is substantially lowered.
NASA Technical Reports Server (NTRS)
Kobayashi, Takahisa; Simon, Donald L.; Litt, Jonathan S.
2005-01-01
An approach based on the Constant Gain Extended Kalman Filter (CGEKF) technique is investigated for the in-flight estimation of non-measurable performance parameters of aircraft engines. Performance parameters, such as thrust and stall margins, provide crucial information for operating an aircraft engine in a safe and efficient manner, but they cannot be directly measured during flight. A technique to accurately estimate these parameters is, therefore, essential for further enhancement of engine operation. In this paper, a CGEKF is developed by combining an on-board engine model and a single Kalman gain matrix. In order to make the on-board engine model adaptive to the real engine s performance variations due to degradation or anomalies, the CGEKF is designed with the ability to adjust its performance through the adjustment of artificial parameters called tuning parameters. With this design approach, the CGEKF can maintain accurate estimation performance when it is applied to aircraft engines at offnominal conditions. The performance of the CGEKF is evaluated in a simulation environment using numerous component degradation and fault scenarios at multiple operating conditions.
NASA Technical Reports Server (NTRS)
Lam, Quang; Chipman, Richard; Sunkel, John
1991-01-01
Two algorithms, extended Kalman filter and neuro-filter, are formulated to perform mass property identification for the Space Station Freedom. Control moment gyros that are part of the Station's basic momentum management system are chosen to provide input excitation in the form of applied torques. These torques together with the measured angular body rate responses are supplied to the filters. From these data, both algorithms are shown to accurately identify the station mass properties when excitation levels are high and balanced between axes. The neuro-filter, however, is shown to be more robust and to perform well even with weakly persistent, unbalanced signals contaminated with noise.
NASA Technical Reports Server (NTRS)
Klein, V.; Schiess, J. R.
1977-01-01
An extended Kalman filter smoother and a fixed point smoother were used for estimation of the state variables in the six degree of freedom kinematic equations relating measured aircraft responses and for estimation of unknown constant bias and scale factor errors in measured data. The computing algorithm includes an analysis of residuals which can improve the filter performance and provide estimates of measurement noise characteristics for some aircraft output variables. The technique developed was demonstrated using simulated and real flight test data. Improved accuracy of measured data was obtained when the data were corrected for estimated bias errors.
Dual extended Kalman filter for combined estimation of vehicle state and road friction
NASA Astrophysics Data System (ADS)
Zong, Changfu; Hu, Dan; Zheng, Hongyu
2013-03-01
Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, many estimation methods have been put forward to solve such problems, in which Kalman filter becomes one of the most popular techniques. Nevertheless, the use of complicated model always leads to poor real-time estimation while the role of road friction coefficient is often ignored. For the purpose of enhancing the real time performance of the algorithm and pursuing precise estimation of vehicle states, a model-based estimator is proposed to conduct combined estimation of vehicle states and road friction coefficients. The estimator is designed based on a three-DOF vehicle model coupled with the Highway Safety Research Institute(HSRI) tire model; the dual extended Kalman filter (DEKF) technique is employed, which can be regarded as two extended Kalman filters operating and communicating simultaneously. Effectiveness of the estimation is firstly examined by comparing the outputs of the estimator with the responses of the vehicle model in CarSim under three typical road adhesion conditions(high-friction, low-friction, and joint-friction). On this basis, driving simulator experiments are carried out to further investigate the practical application of the estimator. Numerical results from CarSim and driving simulator both demonstrate that the estimator designed is capable of estimating the vehicle states and road friction coefficient with reasonable accuracy. The DEKF-based estimator proposed provides the essential information for the vehicle active control system with low expense and decent precision, and offers the possibility of real car application in future.
An Adaptive Kalman Filter using a Simple Residual Tuning Method
NASA Technical Reports Server (NTRS)
Harman, Richard R.
1999-01-01
One difficulty in using Kalman filters in real world situations is the selection of the correct process noise, measurement noise, and initial state estimate and covariance. These parameters are commonly referred to as tuning parameters. Multiple methods have been developed to estimate these parameters. Most of those methods such as maximum likelihood, subspace, and observer Kalman Identification require extensive offline processing and are not suitable for real time processing. One technique, which is suitable for real time processing, is the residual tuning method. Any mismodeling of the filter tuning parameters will result in a non-white sequence for the filter measurement residuals. The residual tuning technique uses this information to estimate corrections to those tuning parameters. The actual implementation results in a set of sequential equations that run in parallel with the Kalman filter. Equations for the estimation of the measurement noise have also been developed. These algorithms are used to estimate the process noise and measurement noise for the Wide Field Infrared Explorer star tracker and gyro.
An Adaptive Kalman Filter Using a Simple Residual Tuning Method
NASA Technical Reports Server (NTRS)
Harman, Richard R.
1999-01-01
One difficulty in using Kalman filters in real world situations is the selection of the correct process noise, measurement noise, and initial state estimate and covariance. These parameters are commonly referred to as tuning parameters. Multiple methods have been developed to estimate these parameters. Most of those methods such as maximum likelihood, subspace, and observer Kalman Identification require extensive offline processing and are not suitable for real time processing. One technique, which is suitable for real time processing, is the residual tuning method. Any mismodeling of the filter tuning parameters will result in a non-white sequence for the filter measurement residuals. The residual tuning technique uses this information to estimate corrections to those tuning parameters. The actual implementation results in a set of sequential equations that run in parallel with the Kalman filter. A. H. Jazwinski developed a specialized version of this technique for estimation of process noise. Equations for the estimation of the measurement noise have also been developed. These algorithms are used to estimate the process noise and measurement noise for the Wide Field Infrared Explorer star tracker and gyro.
Rhythmic Extended Kalman Filter for Gait Rehabilitation Motion Estimation and Segmentation.
Joukov, Vladimir; Bonnet, Vincent; Karg, Michelle; Venture, Gentiane; Kulic, Dana
2017-01-26
This work proposes a method to enable the use of non-intrusive, small, wearable, wireless sensors to estimate the pose of the lower body during gait and other periodic motions and to extract objective performance measures useful for physiotherapy. The Rhythmic Extended Kalman Filter (Rhythmic- EKF) algorithm is developed to estimate the pose, learn an individualized model of periodic movement over time, and use the learned model to improve pose estimation. The proposed approach learns a canonical dynamical system model of the movement during online observation, which is used to accurately model the acceleration during pose estimation. The canonical dynamical system models the motion as a periodic signal. The estimated phase and frequency of the motion also allow the proposed approach to segment the motion into repetitions and extract useful features such as gait symmetry, step length, and mean joint movement and variance. The algorithm is shown to outperform the extended Kalman filter in simulation, on healthy participant data, and stroke patient data. For the healthy participant marching dataset, the Rhythmic-EKF improves joint acceleration and velocity estimates over regular EKF by 40% and 37% respectively, estimates joint angles with 2.4° RMSE, and segments the motion into repetitions with 96% accuracy.
Control of Thermo-Acoustics Instabilities: The Multi-Scale Extended Kalman Approach
NASA Technical Reports Server (NTRS)
Le, Dzu K.; DeLaat, John C.; Chang, Clarence T.
2003-01-01
"Multi-Scale Extended Kalman" (MSEK) is a novel model-based control approach recently found to be effective for suppressing combustion instabilities in gas turbines. A control law formulated in this approach for fuel modulation demonstrated steady suppression of a high-frequency combustion instability (less than 500Hz) in a liquid-fuel combustion test rig under engine-realistic conditions. To make-up for severe transport-delays on control effect, the MSEK controller combines a wavelet -like Multi-Scale analysis and an Extended Kalman Observer to predict the thermo-acoustic states of combustion pressure perturbations. The commanded fuel modulation is composed of a damper action based on the predicted states, and a tones suppression action based on the Multi-Scale estimation of thermal excitations and other transient disturbances. The controller performs automatic adjustments of the gain and phase of these actions to minimize the Time-Scale Averaged Variances of the pressures inside the combustion zone and upstream of the injector. The successful demonstration of Active Combustion Control with this MSEK controller completed an important NASA milestone for the current research in advanced combustion technologies.
Şengül, G; Baysal, U
2012-04-01
In this study, we propose an extended Kalman filter approach for the estimation of the human head tissue conductivities in vivo by using electroencephalogram (EEG) data. Since the relationship between the surface potentials and conductivity distribution is nonlinear, the proposed algorithm first linearizes the system and applies extended Kalman filtering. By using a three-compartment realistic head model obtained from the magnetic resonance images of a real subject, a known dipole assumption and 32 electrode positions, the performance of the proposed method is tested in simulation studies and it is shown that the proposed algorithm estimates the tissue conductivities with less than 1% error in noiseless measurements and less than 5% error when the signal-to-noise ratio is 40 dB or higher. We conclude that the proposed extended Kalman filter approach successfully estimates the tissue conductivities in vivo.
The application of dummy noise adaptive Kalman filter in underwater navigation
NASA Astrophysics Data System (ADS)
Li, Song; Zhang, Chun-Hua; Luan, Jingde
2011-10-01
The track of underwater target is easy to be affected by the various by the various factors, which will cause poor performance in Kalman filter with the error in the state and measure model. In order to solve the situation, a method is provided with dummy noise compensative technology. Dummy noise is added to state and measure model artificially, and then the question can be solved by the adaptive Kalman filter with unknown time-changed statistical character. The simulation result of underwater navigation proves the algorithm is effective.
Liu, Xin; Wang, Hongkai; Yan, Zhuangzhi
2016-01-01
Dynamic fluorescence molecular tomography (FMT) plays an important role in drug delivery research. However, the majority of current reconstruction methods focus on solving the stationary FMT problems. If the stationary reconstruction methods are applied to the time-varying fluorescence measurements, the reconstructed results may suffer from a high level of artifacts. In addition, based on the stationary methods, only one tomographic image can be obtained after scanning one circle projection data. As a result, the movement of fluorophore in imaged object may not be detected due to the relative long data acquisition time (typically >1 min). In this paper, we apply extended kalman filter (EKF) technique to solve the non-stationary fluorescence tomography problem. Especially, to improve the EKF reconstruction performance, the generalized inverse of kalman gain is calculated by a second-order iterative method. The numerical simulation, phantom, and in vivo experiments are performed to evaluate the performance of the method. The experimental results indicate that by using the proposed EKF-based second-order iterative (EKF-SOI) method, we cannot only clearly resolve the time-varying distributions of fluorophore within imaged object, but also greatly improve the reconstruction time resolution (~2.5 sec/frame) which makes it possible to detect the movement of fluorophore during the imaging processes. PMID:27895993
Liu, Xin; Wang, Hongkai; Yan, Zhuangzhi
2016-11-01
Dynamic fluorescence molecular tomography (FMT) plays an important role in drug delivery research. However, the majority of current reconstruction methods focus on solving the stationary FMT problems. If the stationary reconstruction methods are applied to the time-varying fluorescence measurements, the reconstructed results may suffer from a high level of artifacts. In addition, based on the stationary methods, only one tomographic image can be obtained after scanning one circle projection data. As a result, the movement of fluorophore in imaged object may not be detected due to the relative long data acquisition time (typically >1 min). In this paper, we apply extended kalman filter (EKF) technique to solve the non-stationary fluorescence tomography problem. Especially, to improve the EKF reconstruction performance, the generalized inverse of kalman gain is calculated by a second-order iterative method. The numerical simulation, phantom, and in vivo experiments are performed to evaluate the performance of the method. The experimental results indicate that by using the proposed EKF-based second-order iterative (EKF-SOI) method, we cannot only clearly resolve the time-varying distributions of fluorophore within imaged object, but also greatly improve the reconstruction time resolution (~2.5 sec/frame) which makes it possible to detect the movement of fluorophore during the imaging processes.
Tracking and Data Relay Satellite (TDRS) Orbit Estimation Using an Extended Kalman Filter
NASA Technical Reports Server (NTRS)
Ward, Douglas T.; Dang, Ket D.; Slojkowski, Steve; Blizzard, Mike; Jenkins, Greg
2007-01-01
Alternatives to the Tracking and Data Relay Satellite (TDRS) orbit estimation procedure were studied to develop a technique that both produces more reliable results and is more amenable to automation than the prior procedure. The Earth Observing System (EOS) Terra mission has TDRS ephemeris prediction 3(sigma) requirements of 75 meters in position and 5.5 millimeters per second in velocity over a 1.5-day prediction span. Meeting these requirements sometimes required reruns of the prior orbit determination (OD) process, with manual editing of tracking data to get an acceptable solution. After a study of the available alternatives, the Flight Dynamics Facility (FDF) began using the Real-Time Orbit Determination (RTOD(Registered TradeMark)) Kalman filter program for operational support of TDRSs in February 2007. This extended Kalman filter (EKF) is used for daily support, including within hours after most thrusting, to estimate the spacecraft position, velocity, and solar radiation coefficient of reflectivity (C(sub R)). The tracking data used are from the Bilateration Ranging Transponder System (BRTS), selected TDRS System (TDRSS) User satellite tracking data, and Telemetry, Tracking, and Command (TT&C) data. Degraded filter results right after maneuvers and some momentum unloads provided incentive for a hybrid OD technique. The results of combining EKF strengths with the Goddard Trajectory Determination System (GTDS) Differential Correction (DC) program batch-least-squares solutions, as recommended in a 2005 paper on the chain-bias technique, are also presented.
Filtering skill for turbulent signals for a suite of nonlinear and linear extended Kalman filters
NASA Astrophysics Data System (ADS)
Branicki, M.; Gershgorin, B.; Majda, A. J.
2012-02-01
The filtering skill for turbulent signals from nature is often limited by errors due to utilizing an imperfect forecast model. In particular, real-time filtering and prediction when very limited or no a posteriori analysis is possible (e.g. spread of pollutants, storm surges, tsunami detection, etc.) introduces a number of additional challenges to the problem. Here, a suite of filters implementing stochastic parameter estimation for mitigating model error through additive and multiplicative bias correction is examined on a nonlinear, exactly solvable, stochastic test model mimicking turbulent signals in regimes ranging from configurations with strongly intermittent, transient instabilities associated with positive finite-time Lyapunov exponents to laminar behavior. Stochastic Parameterization Extended Kalman Filter (SPEKF), used as a benchmark here, involves exact formulas for propagating the mean and covariance of the augmented forecast model including the unresolved parameters. The remaining filters use the same nonlinear forecast model but they introduce model error through different moment closure approximations and/or linear tangent approximation used for computing the second-order statistics of the augmented stochastic forecast model. A comprehensive study of filter performance is carried out in the presence of various moment closure errors which are enhanced by additional model errors due to incorrect parameters inducing additive and multiplicative stochastic biases. The estimation skill of the unresolved stochastic parameters is also discussed and it is shown that the linear tangent filter, despite its popularity, is completely unreliable in many turbulent regimes for both parameter estimation and filtering; moreover, regimes of filter divergence for the linear tangent filter are identified. The results presented here provide useful guidelines for filtering turbulent, high-dimensional, spatially extended systems with more general model errors, as well as
Hesar, Hamed; Mohebbi, Maryam
2016-06-20
In this paper a model-based Bayesian filtering framework called the "marginalized particle-extended Kalman filter (MP-EKF) algorithm" is proposed for electrocardiogram (ECG) denoising. This algorithm does not have the extended Kalman filter (EKF) shortcoming in handling non-Gaussian nonstationary situations because of its nonlinear framework. In addition, it has less computational complexity compared with particle filter. This filter improves ECG denoising performance by implementing marginalized particle filter framework while reducing its computational complexity using EKF framework. An automatic particle weighting strategy is also proposed here that controls the reliance of our framework to the acquired measurements. We evaluated the proposed filter on several normal ECGs selected from MIT-BIH normal sinus rhythm database. To do so, artificial white Gaussian and colored noises as well as nonstationary real muscle artifact (MA) noise over a range of low SNRs from 10 to -5 dB were added to these normal ECG segments. The benchmark methods were the EKF and extended Kalman smoother (EKS) algorithms which are the first model-based Bayesian algorithms introduced in the field of ECG denoising. From SNR viewpoint, the experiments showed that in the presence of Gaussian white noise, the proposed framework outperforms the EKF and EKS algorithms in lower input SNRs where the measurements and state model are not reliable. Owing to its nonlinear framework and particle weighting strategy, the proposed algorithm attained better results at all input SNRs in non-Gaussian non-stationary situations (such as presence of pink noise, brown noise, and real muscle artifacts). In addition, the impact of the proposed filtering method on the distortion of diagnostic features of the ECG was investigated and compared with EKF/EKS methods using an ECG diagnostic distortion measure called the "Multi-Scale Entropy Based Weighted Distortion Measure" or MSEWPRD. The results revealed that our
LIDAR-Aided Inertial Navigation with Extended Kalman Filtering for Pinpoint Landing
NASA Technical Reports Server (NTRS)
Busnardo, David M.; Aitken, Matthew L.; Tolson, Robert H.; Pierrottet, Diego; Amzajerdian, Farzin
2011-01-01
In support of NASA s Autonomous Landing and Hazard Avoidance Technology (ALHAT) project, an extended Kalman filter routine has been developed for estimating the position, velocity, and attitude of a spacecraft during the landing phase of a planetary mission. The proposed filter combines measurements of acceleration and angular velocity from an inertial measurement unit (IMU) with range and Doppler velocity observations from an onboard light detection and ranging (LIDAR) system. These high-precision LIDAR measurements of distance to the ground and approach velocity will enable both robotic and manned vehicles to land safely and precisely at scientifically interesting sites. The filter has been extensively tested using a lunar landing simulation and shown to improve navigation over flat surfaces or rough terrain. Experimental results from a helicopter flight test performed at NASA Dryden in August 2008 demonstrate that LIDAR can be employed to significantly improve navigation based exclusively on IMU integration.
Low-cost attitude determination system using an extended Kalman filter (EKF) algorithm
NASA Astrophysics Data System (ADS)
Esteves, Fernando M.; Nehmetallah, Georges; Abot, Jandro L.
2016-05-01
Attitude determination is one of the most important subsystems in spacecraft, satellite, or scientific balloon mission s, since it can be combined with actuators to provide rate stabilization and pointing accuracy for payloads. In this paper, a low-cost attitude determination system with a precision in the order of arc-seconds that uses low-cost commercial sensors is presented including a set of uncorrelated MEMS gyroscopes, two clinometers, and a magnetometer in a hierarchical manner. The faster and less precise sensors are updated by the slower, but more precise ones through an Extended Kalman Filter (EKF)-based data fusion algorithm. A revision of the EKF algorithm fundamentals and its implementation to the current application, are presented along with an analysis of sensors noise. Finally, the results from the data fusion algorithm implementation are discussed in detail.
Extended Kalman filter for attitude estimation of the earth radiation budget satellite
NASA Technical Reports Server (NTRS)
Deutschmann, Julie; Bar-Itzhack, Itzhack Y.
1989-01-01
The design and testing of an Extended Kalman Filter (EKF) for ground attitude determination, misalignment estimation and sensor calibration of the Earth Radiation Budget Satellite (ERBS) are described. Attitude is represented by the quaternion of rotation and the attitude estimation error is defined as an additive error. Quaternion normalization is used for increasing the convergence rate and for minimizing the need for filter tuning. The development of the filter dynamic model, the gyro error model and the measurement models of the Sun sensors, the IR horizon scanner and the magnetometers which are used to generate vector measurements are also presented. The filter is applied to real data transmitted by ERBS sensors. Results are presented and analyzed and the EKF advantages as well as sensitivities are discussed. On the whole the filter meets the expected synergism, accuracy and robustness.
Extended Kalman filtering for joint mitigation of phase and amplitude noise in coherent QAM systems.
Pakala, Lalitha; Schmauss, Bernhard
2016-03-21
We numerically investigate our proposed carrier phase and amplitude noise estimation (CPANE) algorithm using extend Kalman filter (EKF) for joint mitigation of linear and non-linear phase noise as well as amplitude noise on 4, 16 and 64 polarization multiplexed (PM) quadrature amplitude modulation (QAM) 224 Gb/s systems. The results are compared to decision directed (DD) carrier phase estimation (CPE), DD phase locked loop (PLL) and universal CPE (U-CPE) algorithms. Besides eliminating the necessity of phase unwrapping function, EKF-CPANE shows improved performance for both back-to-back (BTB) and transmission scenarios compared to the aforementioned algorithms. We further propose a weighted innovation approach (WIA) of the EKF-CPANE which gives an improvement of 0.3 dB in the Q-factor, compared to the original algorithm.
Decentralized neural identifier and control for nonlinear systems based on extended Kalman filter.
Castañeda, Carlos E; Esquivel, P
2012-07-01
A time-varying learning algorithm for recurrent high order neural network in order to identify and control nonlinear systems which integrates the use of a statistical framework is proposed. The learning algorithm is based in the extended Kalman filter, where the associated state and measurement noises covariance matrices are composed by the coupled variance between the plant states. The formulation allows identification of interactions associate between plant state and the neural convergence. Furthermore, a sliding window-based method for dynamical modeling of nonstationary systems is presented to improve the neural identification in the proposed methodology. The efficiency and accuracy of the proposed method is assessed to a five degree of freedom (DOF) robot manipulator where based on the time-varying neural identifier model, the decentralized discrete-time block control and sliding mode techniques are used to design independent controllers and develop the trajectory tracking for each DOF.
An Extended Kalman Filter to Estimate Human Gait Parameters and Walking Distance
Bennett, Terrell; Jafari, Roozbeh; Gans, Nicholas
2017-01-01
In this work, we present a novel method to estimate joint angles and distance traveled by a human while walking. We model the human leg as a two-link revolute robot. Inertial measurement sensors placed on the thigh and shin provide the required measurement inputs. The model and inputs are then used to estimate the desired state parameters associated with forward motion using an extended Kalman filter (EKF). Experimental results with subjects walking in a straight line show that distance walked can be measured with accuracy comparable to a state of the art motion tracking systems. The EKF had an average RMSE of 7 cm over the trials with an average accuracy of greater than 97% for linear displacement.
NASA Technical Reports Server (NTRS)
Deutschmann, Julie; Harman, Rick; Bar-Itzhack, Itzhack
1998-01-01
An innovative approach to autonomous attitude and trajectory estimation is available using only magnetic field data and rate data. The estimation is performed simultaneously using an Extended Kalman Filter, a well known algorithm used extensively in onboard applications. The magnetic field is measured on a satellite by a magnetometer, an inexpensive and reliable sensor flown on virtually all satellites in low earth orbit. Rate data is provided by a gyro, which can be costly. This system has been developed and successfully tested in a post-processing mode using magnetometer and gyro data from 4 satellites supported by the Flight Dynamics Division at Goddard. In order for this system to be truly low cost, an alternative source for rate data must be utilized. An independent system which estimate spacecraft rate has been successfully developed and tested using only magnetometer data or a combination of magnetometer data and sun sensor data, which is less costly than a gyro. This system also uses an Extended Kalman Filter. Merging the two systems will provide an extremely low cost, autonomous approach to attitude and trajectory estimation. In this work we provide the theoretical background of the combined system. The measurement matrix is developed by combining the measurement matrix of the orbit and attitude estimation EKF with the measurement matrix of the rate estimation EKF, which is composed of a pseudo-measurement which makes the effective measurement a function of the angular velocity. Associated with this is the development of the noise covariance matrix associated with the original measurement combined with the new pseudo-measurement. In addition, the combination of the dynamics from the two systems is presented along with preliminary test results.
Structural damage detection using extended Kalman filter combined with statistical process control
NASA Astrophysics Data System (ADS)
Jin, Chenhao; Jang, Shinae; Sun, Xiaorong
2015-04-01
Traditional modal-based methods, which identify damage based upon changes in vibration characteristics of the structure on a global basis, have received considerable attention in the past decades. However, the effectiveness of the modalbased methods is dependent on the type of damage and the accuracy of the structural model, and these methods may also have difficulties when applied to complex structures. The extended Kalman filter (EKF) algorithm which has the capability to estimate parameters and catch abrupt changes, is currently used in continuous and automatic structural damage detection to overcome disadvantages of traditional methods. Structural parameters are typically slow-changing variables under effects of operational and environmental conditions, thus it would be difficult to observe the structural damage and quantify the damage in real-time with EKF only. In this paper, a Statistical Process Control (SPC) is combined with EFK method in order to overcome this difficulty. Based on historical measurements of damage-sensitive feathers involved in the state-space dynamic models, extended Kalman filter (EKF) algorithm is used to produce real-time estimations of these features as well as standard derivations, which can then be used to form control ranges for SPC to detect any abnormality of the selected features. Moreover, confidence levels of the detection can be adjusted by choosing different times of sigma and number of adjacent out-of-range points. The proposed method is tested using simulated data of a three floors linear building in different damage scenarios, and numerical results demonstrate high damage detection accuracy and light computation of this presented method.
A Reduced Extended Kalman Filter Method For Data Assimilation And Parameter Optimization
NASA Astrophysics Data System (ADS)
Kao, C. J.
2007-12-01
This work is an extension of our two recent papers [Kao et al., Data assimilation with an extended Kalman filter for an impact-produced shock-wave study, J. Comp. Phys., 196 (2004), 705-723, and Kao et al., Estimating model parameters for an impact-produced shock-wave simulation: Optimal use of partial data with the extended Kalman filter, J. Comp. Phys., 214 (2006), 725-737 ] about the applications of the extended Kalman filter (EKF) to data assimilation in predictive codes. We have shown through the above two studies that the EKF method successfully estimates the evolving model state variables as well as model parameters of a shock-wave system by merging single-point pressure data into an Euler-equations computer code. We here intend to introduce a reduced EKF for the same purposes in terms of data assimilation and parameter optimization, but with a much smaller computational cost so that the applications of EKF to multi-dimensional realistic problems can be made possible. One of the distinctive features of EKF is that, as the system evolves forward in time, the EKF algorithm tracks the time-dependent error-covariance matrix of the model's state variables and parameters based on a consistent tangent-linear approximation of the model dynamics. When data becomes available at one instant in time, the update of the model state variables and parameters is achieved through a functional form of the linear merger of the model prediction and the data, subjective to the minimization of the trace of the error-covariance matrix of the model state variables and parameters. It, however, has been a concern that the calculation for the time evolution of the error-covariance matrix in applying EKF is computationally demanding and prohibitively expensive for real multi-dimensional problems. Several simplified approaches of EKF have been proposed to reduce the computational burden. This current study was actually motivated by Dee's work [Dee, P. D., 1991: Simplification of the
Adaptive Kalman filtering methods for tracking GPS signals in high noise/high dynamic environments
NASA Astrophysics Data System (ADS)
Zuo, Qiyao; Yuan, Hong; Lin, Baojun
2007-11-01
GPS C/A signal tracking algorithms have been developed based on adaptive Kalman filtering theory. In the research, an adaptive Kalman filter is used to substitute for standard tracking loop filters. The goal is to improve estimation accuracy and tracking stabilization in high noise and high dynamic environments. The linear dynamics model and the measurements model are designed to estimate code phase, carrier phase, Doppler shift, and rate of change of Doppler shift. Two adaptive algorithms are applied to improve robustness and adaptive faculty of the tracking, one is Sage adaptive filtering approach and the other is strong tracking method. Both the new algorithms and the conventional tracking loop have been tested by using simulation data. In the simulation experiment, the highest jerk of the receiver is set to 10G m/s 3 with the lowest C/No 30dBHz. The results indicate that the Kalman filtering algorithms are more robust than the standard tracking loop, and performance of tracking loop using the algorithms is satisfactory in such extremely adverse circumstances.
Adaptive error covariances estimation methods for ensemble Kalman filters
Zhen, Yicun; Harlim, John
2015-08-01
This paper presents a computationally fast algorithm for estimating, both, the system and observation noise covariances of nonlinear dynamics, that can be used in an ensemble Kalman filtering framework. The new method is a modification of Belanger's recursive method, to avoid an expensive computational cost in inverting error covariance matrices of product of innovation processes of different lags when the number of observations becomes large. When we use only product of innovation processes up to one-lag, the computational cost is indeed comparable to a recently proposed method by Berry–Sauer's. However, our method is more flexible since it allows for using information from product of innovation processes of more than one-lag. Extensive numerical comparisons between the proposed method and both the original Belanger's and Berry–Sauer's schemes are shown in various examples, ranging from low-dimensional linear and nonlinear systems of SDEs and 40-dimensional stochastically forced Lorenz-96 model. Our numerical results suggest that the proposed scheme is as accurate as the original Belanger's scheme on low-dimensional problems and has a wider range of more accurate estimates compared to Berry–Sauer's method on L-96 example.
NASA Astrophysics Data System (ADS)
Rahimi, Afshin; Kumar, Krishna Dev; Alighanbari, Hekmat
2017-05-01
Reaction wheels, as one of the most commonly used actuators in satellite attitude control systems, are prone to malfunction which could lead to catastrophic failures. Such malfunctions can be detected and addressed in time if proper analytical redundancy algorithms such as parameter estimation and control reconfiguration are employed. Major challenges in parameter estimation include speed and accuracy of the employed algorithm. This paper presents a new approach for improving parameter estimation with adaptive unscented Kalman filter. The enhancement in tracking speed of unscented Kalman filter is achieved by systematically adapting the covariance matrix to the faulty estimates using innovation and residual sequences combined with an adaptive fault annunciation scheme. The proposed approach provides the filter with the advantage of tracking sudden changes in the system non-measurable parameters accurately. Results showed successful detection of reaction wheel malfunctions without requiring a priori knowledge about system performance in the presence of abrupt, transient, intermittent, and incipient faults. Furthermore, the proposed approach resulted in superior filter performance with less mean squared errors for residuals compared to generic and adaptive unscented Kalman filters, and thus, it can be a promising method for the development of fail-safe satellites.
Lepine, Nicholas N; Tajima, Takuro; Ogasawara, Takayuki; Kasahara, Ryoichi; Koizumi, Hiroshi; Lepine, Nicholas N; Tajima, Takuro; Ogasawara, Takayuki; Kasahara, Ryoichi; Koizumi, Hiroshi; Koizumi, Hiroshi; Ogasawara, Takayuki; Tajima, Takuro; Kasahara, Ryoichi; Lepine, Nicholas N
2016-08-01
An adaptive Kalman filter-based fusion algorithm capable of estimating respiration rate for unobtrusive respiratory monitoring is proposed. Using both signal characteristics and a priori information, the Kalman filter is adaptively optimized to improve accuracy. Furthermore, the system is able to combine the respiration-related signals extracted from a textile ECG sensor and an accelerometer to create a single robust measurement. We measured derived respiratory rates and, when compared to a reference, found root-mean-square error of 2.11 breaths-per-minute (BrPM) while lying down, 2.30 BrPM while sitting, 5.97 BrPM while walking, and 5.98 BrPM while running. These results demonstrate that the proposed system is applicable to unobtrusive monitoring for various applications.
NASA Astrophysics Data System (ADS)
Man, Jun; Li, Weixuan; Zeng, Lingzao; Wu, Laosheng
2016-06-01
The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a sufficiently large ensemble size is usually required to guarantee the accuracy. As an alternative approach, the probabilistic collocation based Kalman filter (PCKF) employs the polynomial chaos expansion (PCE) to represent and propagate the uncertainties in parameters and states. However, PCKF suffers from the so-called "curse of dimensionality". Its computational cost increases drastically with the increasing number of parameters and system nonlinearity. Furthermore, PCKF may fail to provide accurate estimations due to the joint updating scheme for strongly nonlinear models. Motivated by recent developments in uncertainty quantification and EnKF, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problems. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected at each assimilation step; the "restart" scheme is utilized to eliminate the inconsistency between updated model parameters and states variables. The performance of RAPCKF is systematically tested with numerical cases of unsaturated flow models. It is shown that the adaptive approach and restart scheme can significantly improve the performance of PCKF. Moreover, RAPCKF has been demonstrated to be more efficient than EnKF with the same computational cost.
NASA Astrophysics Data System (ADS)
Plett, Gregory L.
Battery management systems in hybrid electric vehicle battery packs must estimate values descriptive of the pack's present operating condition. These include: battery state of charge, power fade, capacity fade, and instantaneous available power. The estimation mechanism must adapt to changing cell characteristics as cells age and therefore provide accurate estimates over the lifetime of the pack. In a series of three papers, we propose a method, based on extended Kalman filtering (EKF), that is able to accomplish these goals on a lithium ion polymer battery pack. We expect that it will also work well on other battery chemistries. These papers cover the required mathematical background, cell modeling and system identification requirements, and the final solution, together with results. In order to use EKF to estimate the desired quantities, we first require a mathematical model that can accurately capture the dynamics of a cell. In this paper we "evolve" a suitable model from one that is very primitive to one that is more advanced and works well in practice. The final model includes terms that describe the dynamic contributions due to open-circuit voltage, ohmic loss, polarization time constants, electro-chemical hysteresis, and the effects of temperature. We also give a means, based on EKF, whereby the constant model parameters may be determined from cell test data. Results are presented that demonstrate it is possible to achieve root-mean-squared modeling error smaller than the level of quantization error expected in an implementation.
Extended Kalman smoother with differential evolution technique for denoising of ECG signal.
Panigrahy, D; Sahu, P K
2016-09-01
Electrocardiogram (ECG) signal gives a lot of information on the physiology of heart. In reality, noise from various sources interfere with the ECG signal. To get the correct information on physiology of the heart, noise cancellation of the ECG signal is required. In this paper, the effectiveness of extended Kalman smoother (EKS) with the differential evolution (DE) technique for noise cancellation of the ECG signal is investigated. DE is used as an automatic parameter selection method for the selection of ten optimized components of the ECG signal, and those are used to create the ECG signal according to the real ECG signal. These parameters are used by the EKS for the development of the state equation and also for initialization of the parameters of EKS. EKS framework is used for denoising the ECG signal from the single channel. The effectiveness of proposed noise cancellation technique has been evaluated by adding white, colored Gaussian noise and real muscle artifact noise at different SNR to some visually clean ECG signals from the MIT-BIH arrhythmia database. The proposed noise cancellation technique of ECG signal shows better signal to noise ratio (SNR) improvement, lesser mean square error (MSE) and percent of distortion (PRD) compared to other well-known methods.
Fusing inertial sensor data in an extended Kalman filter for 3D camera tracking.
Erdem, Arif Tanju; Ercan, Ali Özer
2015-02-01
In a setup where camera measurements are used to estimate 3D egomotion in an extended Kalman filter (EKF) framework, it is well-known that inertial sensors (i.e., accelerometers and gyroscopes) are especially useful when the camera undergoes fast motion. Inertial sensor data can be fused at the EKF with the camera measurements in either the correction stage (as measurement inputs) or the prediction stage (as control inputs). In general, only one type of inertial sensor is employed in the EKF in the literature, or when both are employed they are both fused in the same stage. In this paper, we provide an extensive performance comparison of every possible combination of fusing accelerometer and gyroscope data as control or measurement inputs using the same data set collected at different motion speeds. In particular, we compare the performances of different approaches based on 3D pose errors, in addition to camera reprojection errors commonly found in the literature, which provides further insight into the strengths and weaknesses of different approaches. We show using both simulated and real data that it is always better to fuse both sensors in the measurement stage and that in particular, accelerometer helps more with the 3D position tracking accuracy, whereas gyroscope helps more with the 3D orientation tracking accuracy. We also propose a simulated data generation method, which is beneficial for the design and validation of tracking algorithms involving both camera and inertial measurement unit measurements in general.
Online estimation of Allan variance coefficients based on a neural-extended Kalman filter.
Miao, Zhiyong; Shen, Feng; Xu, Dingjie; He, Kunpeng; Tian, Chunmiao
2015-01-23
As a noise analysis method for inertial sensors, the traditional Allan variance method requires the storage of a large amount of data and manual analysis for an Allan variance graph. Although the existing online estimation methods avoid the storage of data and the painful procedure of drawing slope lines for estimation, they require complex transformations and even cause errors during the modeling of dynamic Allan variance. To solve these problems, first, a new state-space model that directly models the stochastic errors to obtain a nonlinear state-space model was established for inertial sensors. Then, a neural-extended Kalman filter algorithm was used to estimate the Allan variance coefficients. The real noises of an ADIS16405 IMU and fiber optic gyro-sensors were analyzed by the proposed method and traditional methods. The experimental results show that the proposed method is more suitable to estimate the Allan variance coefficients than the traditional methods. Moreover, the proposed method effectively avoids the storage of data and can be easily implemented using an online processor.
NASA Technical Reports Server (NTRS)
Deutschmann, Julie; Bar-Itzhack, Itzhack Y.; Rokni, Mohammad
1990-01-01
The testing and comparison of two Extended Kalman Filters (EKFs) developed for the Earth Radiation Budget Satellite (ERBS) is described. One EKF updates the attitude quaternion using a four component additive error quaternion. This technique is compared to that of a second EKF, which uses a multiplicative error quaternion. A brief development of the multiplicative algorithm is included. The mathematical development of the additive EKF was presented in the 1989 Flight Mechanics/Estimation Theory Symposium along with some preliminary testing results using real spacecraft data. A summary of the additive EKF algorithm is included. The convergence properties, singularity problems, and normalization techniques of the two filters are addressed. Both filters are also compared to those from the ERBS operational ground support software, which uses a batch differential correction algorithm to estimate attitude and gyro biases. Sensitivity studies are performed on the estimation of sensor calibration states. The potential application of the EKF for real time and non-real time ground attitude determination and sensor calibration for future missions such as the Gamma Ray Observatory (GRO) and the Small Explorer Mission (SMEX) is also presented.
Extended Kalman Doppler tracking and model determination for multi-sensor short-range radar
NASA Astrophysics Data System (ADS)
Mittermaier, Thomas J.; Siart, Uwe; Eibert, Thomas F.; Bonerz, Stefan
2016-09-01
A tracking solution for collision avoidance in industrial machine tools based on short-range millimeter-wave radar Doppler observations is presented. At the core of the tracking algorithm there is an Extended Kalman Filter (EKF) that provides dynamic estimation and localization in real-time. The underlying sensor platform consists of several homodyne continuous wave (CW) radar modules. Based on In-phase-Quadrature (IQ) processing and down-conversion, they provide only Doppler shift information about the observed target. Localization with Doppler shift estimates is a nonlinear problem that needs to be linearized before the linear KF can be applied. The accuracy of state estimation depends highly on the introduced linearization errors, the initialization and the models that represent the true physics as well as the stochastic properties. The important issue of filter consistency is addressed and an initialization procedure based on data fitting and maximum likelihood estimation is suggested. Models for both, measurement and process noise are developed. Tracking results from typical three-dimensional courses of movement at short distances in front of a multi-sensor radar platform are presented.
Pose and Motion Estimation Using Dual Quaternion-Based Extended Kalman Filtering
Goddard, J.S.; Abidi, M.A.
1998-06-01
A solution to the remote three-dimensional (3-D) measurement problem is presented for a dynamic system given a sequence of two-dimensional (2-D) intensity images of a moving object. The 3-D transformation is modeled as a nonlinear stochastic system with the state estimate providing the six-degree-of-freedom motion and position values as well as structure. The stochastic model uses the iterated extended Kalman filter (IEKF) as a nonlinear estimator and a screw representation of the 3-D transformation based on dual quaternions. Dual quaternions, whose elements are dual numbers, provide a means to represent both rotation and translation in a unified notation. Linear object features, represented as dual vectors, are transformed using the dual quaternion transformation and are then projected to linear features in the image plane. The method has been implemented and tested with both simulated and actual experimental data. Simulation results are provided, along with comparisons to a point-based IEKF method using rotation and translation, to show the relative advantages of this method. Experimental results from testing using a camera mounted on the end effector of a robot arm are also given.
Szczęsna, Agnieszka; Pruszowski, Przemysław
2016-01-01
Inertial orientation tracking is still an area of active research, especially in the context of out-door, real-time, human motion capture. Existing systems either propose loosely coupled tracking approaches where each segment is considered independently, taking the resulting drawbacks into account, or tightly coupled solutions that are limited to a fixed chain with few segments. Such solutions have no flexibility to change the skeleton structure, are dedicated to a specific set of joints, and have high computational complexity. This paper describes the proposal of a new model-based extended quaternion Kalman filter that allows for estimation of orientation based on outputs from the inertial measurements unit sensors. The filter considers interdependencies resulting from the construction of the kinematic chain so that the orientation estimation is more accurate. The proposed solution is a universal filter that does not predetermine the degree of freedom at the connections between segments of the model. To validation the motion of 3-segments single link pendulum captured by optical motion capture system is used. The next step in the research will be to use this method for inertial motion capture with a human skeleton model.
Orbit Determination for the Lunar Reconnaissance Orbiter Using an Extended Kalman Filter
NASA Technical Reports Server (NTRS)
Slojkowski, Steven; Lowe, Jonathan; Woodburn, James
2015-01-01
Orbit determination (OD) analysis results are presented for the Lunar Reconnaissance Orbiter (LRO) using a commercially available Extended Kalman Filter, Analytical Graphics' Orbit Determination Tool Kit (ODTK). Process noise models for lunar gravity and solar radiation pressure (SRP) are described and OD results employing the models are presented. Definitive accuracy using ODTK meets mission requirements and is better than that achieved using the operational LRO OD tool, the Goddard Trajectory Determination System (GTDS). Results demonstrate that a Vasicek stochastic model produces better estimates of the coefficient of solar radiation pressure than a Gauss-Markov model, and prediction accuracy using a Vasicek model meets mission requirements over the analysis span. Modeling the effect of antenna motion on range-rate tracking considerably improves residuals and filter-smoother consistency. Inclusion of off-axis SRP process noise and generalized process noise improves filter performance for both definitive and predicted accuracy. Definitive accuracy from the smoother is better than achieved using GTDS and is close to that achieved by precision OD methods used to generate definitive science orbits. Use of a multi-plate dynamic spacecraft area model with ODTK's force model plugin capability provides additional improvements in predicted accuracy.
Non-invasive Estimation of Global Activation Sequence using the Extended Kalman Filter
Liu, Chenguang; He, Bin
2011-01-01
A new algorithm for three-dimensional (3D) imaging of the activation sequence from noninvasive body surface potentials is proposed. After formulating the nonlinear relationship between the 3D activation sequence and the body surface recordings during activation, the extended Kalman filter (EKF) is utilized to estimate the activation sequence in a recursive way. The state vector containing the activation sequence is optimized during iteration by updating the error covariance matrix. A new regularization scheme is incorporated into the “predict” procedure of EKF to tackle the ill-posedness of the inverse problem. The EKF based algorithm shows good performance in simulation under single-site pacing. Between the estimated activation sequences and true values, the average correlation coefficient (CC) is 0.95, and the relative error (RE) is 0.13. The average localization error (LE) when localizing the pacing site is 3.0 mm. Good results are also obtained under dual-site pacing (CC = 0.93, RE = 0.16, LE = 4.3 mm). Furthermore, the algorithm shows robustness to noise. The present promising results demonstrate that the proposed EKF-based inverse approach can noninvasively estimate the 3D activation sequence with good accuracy and the new algorithm shows good features due to the application of EKF. PMID:20716498
Man, Jun; Li, Weixuan; Zeng, Lingzao; Wu, Laosheng
2016-06-01
The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a relatively large ensemble size is usually required to guarantee the accuracy. As an alternative approach, the probabilistic collocation based Kalman filter (PCKF) employs the polynomial chaos to approximate the original system. In this way, the sampling error can be reduced. However, PCKF suffers from the so-called "curse of dimensionality". When the system nonlinearity is strong and number of parameters is large, PCKF could be even more computationally expensive than EnKF. Motivated by most recent developments in uncertainty quantification, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problems. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected. The "restart" technology is used to eliminate the inconsistency between model parameters and states. The performance of RAPCKF is tested with numerical cases of unsaturated flow models. It is shown that RAPCKF is more efficient than EnKF with the same computational cost. Compared with the traditional PCKF, the RAPCKF is more applicable in strongly nonlinear and high dimensional problems.
Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter
Chu, Hairong; Sun, Tingting; Zhang, Baiqiang; Zhang, Hongwei; Chen, Yang
2017-01-01
In airborne MEMS SINS transfer alignment, the error of MEMS IMU is highly environment-dependent and the parameters of the system model are also uncertain, which may lead to large error and bad convergence of the Kalman filter. In order to solve this problem, an improved adaptive incremental Kalman filter (AIKF) algorithm is proposed. First, the model of SINS transfer alignment is defined based on the “Velocity and Attitude” matching method. Then the detailed algorithm progress of AIKF and its recurrence formulas are presented. The performance and calculation amount of AKF and AIKF are also compared. Finally, a simulation test is designed to verify the accuracy and the rapidity of the AIKF algorithm by comparing it with KF and AKF. The results show that the AIKF algorithm has better estimation accuracy and shorter convergence time, especially for the bias of the gyroscope and the accelerometer, which can meet the accuracy and rapidity requirement of transfer alignment. PMID:28098829
NASA Astrophysics Data System (ADS)
Li, Jing; Song, Ningfang; Yang, Gongliu; Jiang, Rui
2016-07-01
In the initial alignment process of strapdown inertial navigation system (SINS), large misalignment angles always bring nonlinear problem, which can usually be processed using the scaled unscented Kalman filter (SUKF). In this paper, the problem of large misalignment angles in SINS alignment is further investigated, and the strong tracking scaled unscented Kalman filter (STSUKF) is proposed with fixed parameters to improve convergence speed, while these parameters are artificially constructed and uncertain in real application. To further improve the alignment stability and reduce the parameters selection, this paper proposes a fuzzy adaptive strategy combined with STSUKF (FUZZY-STSUKF). As a result, initial alignment scheme of large misalignment angles based on FUZZY-STSUKF is designed and verified by simulations and turntable experiment. The results show that the scheme improves the accuracy and convergence speed of SINS initial alignment compared with those based on SUKF and STSUKF.
Li, Jing; Song, Ningfang; Yang, Gongliu; Jiang, Rui
2016-07-01
In the initial alignment process of strapdown inertial navigation system (SINS), large misalignment angles always bring nonlinear problem, which can usually be processed using the scaled unscented Kalman filter (SUKF). In this paper, the problem of large misalignment angles in SINS alignment is further investigated, and the strong tracking scaled unscented Kalman filter (STSUKF) is proposed with fixed parameters to improve convergence speed, while these parameters are artificially constructed and uncertain in real application. To further improve the alignment stability and reduce the parameters selection, this paper proposes a fuzzy adaptive strategy combined with STSUKF (FUZZY-STSUKF). As a result, initial alignment scheme of large misalignment angles based on FUZZY-STSUKF is designed and verified by simulations and turntable experiment. The results show that the scheme improves the accuracy and convergence speed of SINS initial alignment compared with those based on SUKF and STSUKF.
Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter.
Chu, Hairong; Sun, Tingting; Zhang, Baiqiang; Zhang, Hongwei; Chen, Yang
2017-01-14
In airborne MEMS SINS transfer alignment, the error of MEMS IMU is highly environment-dependent and the parameters of the system model are also uncertain, which may lead to large error and bad convergence of the Kalman filter. In order to solve this problem, an improved adaptive incremental Kalman filter (AIKF) algorithm is proposed. First, the model of SINS transfer alignment is defined based on the "Velocity and Attitude" matching method. Then the detailed algorithm progress of AIKF and its recurrence formulas are presented. The performance and calculation amount of AKF and AIKF are also compared. Finally, a simulation test is designed to verify the accuracy and the rapidity of the AIKF algorithm by comparing it with KF and AKF. The results show that the AIKF algorithm has better estimation accuracy and shorter convergence time, especially for the bias of the gyroscope and the accelerometer, which can meet the accuracy and rapidity requirement of transfer alignment.
Zhang, Yin; Chase, Steve M
2013-01-01
Neural prosthetics are a promising technology for alleviating paralysis by actuating devices directly from the intention to move. Typical implementations of these devices require a calibration session to define decoding parameters that map recorded neural activity into movement of the device. However, a major factor limiting the clinical deployment of this technology is stability: with fixed decoding parameters, control of the prosthetic device has been shown to degrade over time. Here we apply a dual estimation procedure to adaptively capture changes in decoding parameters. In simulation, we find that our stabilized dual Kalman filter can run autonomously for hundreds of thousands of trials with little change in performance. Further, when we apply our algorithm off-line to estimate arm trajectories from neural data recorded over five consecutive days, we find that it outperforms a static Kalman filter, even when it is re-calibrated at the beginning of each day.
Adaptive Kalman filtering for histogram-based appearance learning in infrared imagery.
Venkataraman, Vijay; Fan, Guoliang; Havlicek, Joseph P; Fan, Xin; Zhai, Yan; Yeary, Mark B
2012-11-01
Targets of interest in video acquired from imaging infrared sensors often exhibit profound appearance variations due to a variety of factors, including complex target maneuvers, ego-motion of the sensor platform, background clutter, etc., making it difficult to maintain a reliable detection process and track lock over extended time periods. Two key issues in overcoming this problem are how to represent the target and how to learn its appearance online. In this paper, we adopt a recent appearance model that estimates the pixel intensity histograms as well as the distribution of local standard deviations in both the foreground and background regions for robust target representation. Appearance learning is then cast as an adaptive Kalman filtering problem where the process and measurement noise variances are both unknown. We formulate this problem using both covariance matching and, for the first time in a visual tracking application, the recent autocovariance least-squares (ALS) method. Although convergence of the ALS algorithm is guaranteed only for the case of globally wide sense stationary process and measurement noises, we demonstrate for the first time that the technique can often be applied with great effectiveness under the much weaker assumption of piecewise stationarity. The performance advantages of the ALS method relative to the classical covariance matching are illustrated by means of simulated stationary and nonstationary systems. Against real data, our results show that the ALS-based algorithm outperforms the covariance matching as well as the traditional histogram similarity-based methods, achieving sub-pixel tracking accuracy against the well-known AMCOM closure sequences and the recent SENSIAC automatic target recognition dataset.
Adaptive Parametric Spectral Estimation with Kalman Smoothing for Online Early Seizure Detection
Park, Yun S.; Hochberg, Leigh R.; Eskandar, Emad N.; Cash, Sydney S.; Truccolo, Wilson
2014-01-01
Tracking spectral changes in neural signals, such as local field potentials (LFPs) and scalp or intracranial electroencephalograms (EEG, iEEG), is an important problem in early detection and prediction of seizures. Most approaches have focused on either parametric or nonparametric spectral estimation methods based on moving time windows. Here, we explore an adaptive (time-varying) parametric ARMA approach for tracking spectral changes in neural signals based on the fixed-interval Kalman smoother. We apply the method to seizure detection based on spectral features of intracortical LFPs recorded from a person with pharmacologically intractable focal epilepsy. We also devise and test an approach for real-time tracking of spectra based on the adaptive parametric method with the fixed-interval Kalman smoother. The order of ARMA models is determined via the AIC computed in moving time windows. We quantitatively demonstrate the advantages of using the adaptive parametric estimation method in seizure detection over nonparametric alternatives based exclusively on moving time windows. Overall, the adaptive parametric approach significantly improves the statistical separability of interictal and ictal epochs. PMID:24663686
AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal
Yang, Gongliu; Liu, Yuanyuan; Li, Ming; Song, Shunguang
2015-01-01
An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal. PMID:26512665
AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal.
Yang, Gongliu; Liu, Yuanyuan; Li, Ming; Song, Shunguang
2015-10-23
An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal.
Improvement of the PEST parameter estimation algorithm through Extended Kalman Filtering
NASA Astrophysics Data System (ADS)
Pauwels, V. R. N.; Goegebeur, M.
2006-12-01
The estimation of the parameters needed for the various processes represented in hydrologic models has always been a major difficulty in the application of these models. To solve this problem, a number of methods have been developed to automatically estimate model parameters. One frequently used and relatively simple algorithm is the Parameter Estimation (PEST) method. A close examination of this algorithm shows that it is very similar to the Extended Kalman Filter (EKF). The differences between the methods are caused by the derivation of the algorithms: the EKF is derived through a minimization of the square difference between the true and the estimated model state, while PEST has been derived through a minimization of an objective function related to the Root Mean Square Error between the model results and the observations. The objective of this paper is to analyze the performance of these two algorithms. A synthetic experiment has been developed for this purpose. It has been found that under high observation errors and/or temporally sparse observations the EKF can lead to a stable parameter estimation, while it is possible that under the same circumstances PEST does not yield a solution. Also, the choice of the initial guess for the parameter values can be an important issue in the application of PEST, while this is not so important for the EKF. The application of the Marquardt algorithm can lead to stable parameter estimates in case the PEST algorithm fails, but numerically the EKF is still superior. In order to solve this problem, a simple alternative to the Marquardt algorithm has been suggested, which leads to a quicker convergence. The overall conclusion from this work is that generally PEST and the EKF will lead to similar results, but that under high observation errors, infrequent observations, and/or strongly erroneous initial parameter values, the PEST method can fail while the EKF can still yield stable parameter estimates.
NASA Technical Reports Server (NTRS)
Deutschmann, Julie; Harman, Rick; Bar-Itzhack, Itzhack
1998-01-01
An innovative approach to autonomous attitude and trajectory estimation is available using only magnetic field data and rate data. The estimation is performed simultaneously using an Extended Kalman Filter (EKF), a well known algorithm used extensively in onboard applications. The magnetic field is measured on a satellite by a magnetometer, an inexpensive and reliable sensor flown on virtually all satellites in low earth orbit. Rate data is provided by a gyro, which can be costly. This system has been developed and successfully tested in a post-processing mode using magnetometer and gyro data from 4 satellites supported by the Flight Dynamics Division at Goddard. In order for this system to be truly low cost, an alternative source for rate data must be utilized. An independent system which estimates spacecraft rate has been successfully developed and tested using only magnetometer data or a combination of magnetometer data and sun sensor data, which is less costly than a gyro. This system also uses an EKF. Merging the two systems will provide an extremely low cost, autonomous approach to attitude and trajectory estimation. In this work we provide the theoretical background of the combined system. The measurement matrix is developed by combining the measurement matrix of the orbit and attitude estimation EKF with the measurement matrix of the rate estimation EKF, which is composed of a pseudo-measurement which makes the effective measurement a function of the angular velocity. Associated with this is the development of the noise covariance matrix associated with the original measurement combined with the new pseudo-measurement. In addition, the combination of the dynamics from the two systems is presented along with preliminary test results.
NASA Astrophysics Data System (ADS)
Fairbairn, D.; Barbu, A. L.; Mahfouf, J.-F.; Calvet, J.-C.; Gelati, E.
2015-12-01
Two data assimilation (DA) methods are compared for their ability to produce an accurate soil moisture analysis using the Météo-France land surface model: (i) SEKF, a simplified extended Kalman filter, which uses a climatological background-error covariance, and (ii) EnSRF, the ensemble square root filter, which uses an ensemble background-error covariance and approximates random rainfall errors stochastically. In situ soil moisture observations at 5 cm depth are assimilated into the surface layer and 30 cm deep observations are used to evaluate the root-zone analysis on 12 sites in south-western France (SMOSMANIA network). These sites differ in terms of climate and soil texture. The two methods perform similarly and improve on the open loop. Both methods suffer from incorrect linear assumptions which are particularly degrading to the analysis during water-stressed conditions: the EnSRF by a dry bias and the SEKF by an over-sensitivity of the model Jacobian between the surface and the root-zone layers. These problems are less severe for the sites with wetter climates. A simple bias correction technique is tested on the EnSRF. Although this reduces the bias, it modifies the soil moisture fluxes and suppresses the ensemble spread, which degrades the analysis performance. However, the EnSRF flow-dependent background-error covariance evidently captures seasonal variability in the soil moisture errors and should exploit planned improvements in the model physics. Synthetic twin experiments demonstrate that when there is only a random component in the precipitation forcing errors, the correct stochastic representation of these errors enables the EnSRF to perform better than the SEKF. It might therefore be possible for the EnSRF to perform better than the SEKF with real data, if the rainfall uncertainty was accurately captured. However, the simple rainfall error model is not advantageous in our real experiments. More realistic rainfall error models are suggested.
Aboy, Mateo; Márquez, Oscar W; McNames, James; Hornero, Roberto; Trong, Tran; Goldstein, Brahm
2005-08-01
We describe an algorithm to estimate the instantaneous power spectral density (PSD) of nonstationary signals. The algorithm is based on a dual Kalman filter that adaptively generates an estimate of the autoregressive model parameters at each time instant. The algorithm exhibits superior PSD tracking performance in nonstationary signals than classical nonparametric methodologies, and does not assume local stationarity of the data. Furthermore, it provides better time-frequency resolution, and is robust to model mismatches. We demonstrate its usefulness by a sample application involving PSD estimation of intracranial pressure signals (ICP) from patients with traumatic brain injury (TBI).
Panigrahy, D; Sahu, P K
2017-02-16
This paper proposes a five-stage based methodology to extract the fetal electrocardiogram (FECG) from the single channel abdominal ECG using differential evolution (DE) algorithm, extended Kalman smoother (EKS) and adaptive neuro fuzzy inference system (ANFIS) framework. The heart rate of the fetus can easily be detected after estimation of the fetal ECG signal. The abdominal ECG signal contains fetal ECG signal, maternal ECG component, and noise. To estimate the fetal ECG signal from the abdominal ECG signal, removal of the noise and the maternal ECG component presented in it is necessary. The pre-processing stage is used to remove the noise from the abdominal ECG signal. The EKS framework is used to estimate the maternal ECG signal from the abdominal ECG signal. The optimized parameters of the maternal ECG components are required to develop the state and measurement equation of the EKS framework. These optimized maternal ECG parameters are selected by the differential evolution algorithm. The relationship between the maternal ECG signal and the available maternal ECG component in the abdominal ECG signal is nonlinear. To estimate the actual maternal ECG component present in the abdominal ECG signal and also to recognize this nonlinear relationship the ANFIS is used. Inputs to the ANFIS framework are the output of EKS and the pre-processed abdominal ECG signal. The fetal ECG signal is computed by subtracting the output of ANFIS from the pre-processed abdominal ECG signal. Non-invasive fetal ECG database and set A of 2013 physionet/computing in cardiology challenge database (PCDB) are used for validation of the proposed methodology. The proposed methodology shows a sensitivity of 94.21%, accuracy of 90.66%, and positive predictive value of 96.05% from the non-invasive fetal ECG database. The proposed methodology also shows a sensitivity of 91.47%, accuracy of 84.89%, and positive predictive value of 92.18% from the set A of PCDB.
Improvement of the PEST parameter estimation algorithm through Extended Kalman Filtering
NASA Astrophysics Data System (ADS)
Goegebeur, Maarten; Pauwels, Valentijn R. N.
2007-04-01
SummaryDuring the last decades, a number of methods have been developed for the estimation of hydrologic model parameters. One frequently used and relatively simple algorithm is the parameter estimation (PEST) method. A close examination of this algorithm shows that it is very similar to the Extended Kalman Filter (EKF). The differences between the methods are caused by the derivation of the algorithms: the EKF is derived through a minimization of the square difference between the true and the estimated model state, while PEST has been derived through a minimization of an objective function related to, but not equal to, the root mean square error between the model results and the observations. The objective of this paper is to analyze the performance of these two algorithms. A synthetic-data experiment has been developed for this purpose. It has been found that under high observation errors and/or temporally sparse observations the EKF can lead to a stable parameter estimation, while it is possible that under the same circumstances PEST does not yield a solution. Also, the choice of the initial guess for the parameter values can be an important issue in the application of PEST, while this is not so important for the EKF. The application of the Marquardt algorithm can lead to stable parameter estimates in case the PEST algorithm fails (meaning that nonphysical parameter values were obtained which lead to a premature abortion of the model simulations), but numerically the EKF is still superior. In order to solve this problem, a simple alternative to the Marquardt algorithm has been developed, which leads to a quicker convergence. Application of both methods to a conceptual rainfall-runoff model with 10 parameters shows the robustness of the EKF for parameter calibration. The overall conclusion from this work is that generally PEST and the EKF will lead to similar results, but that under high observation errors, infrequent observations, and/or strongly erroneous
Adaptive Kalman snake for semi-autonomous 3D vessel tracking.
Lee, Sang-Hoon; Lee, Sanghoon
2015-10-01
In this paper, we propose a robust semi-autonomous algorithm for 3D vessel segmentation and tracking based on an active contour model and a Kalman filter. For each computed tomography angiography (CTA) slice, we use the active contour model to segment the vessel boundary and the Kalman filter to track position and shape variations of the vessel boundary between slices. For successful segmentation via active contour, we select an adequate number of initial points from the contour of the first slice. The points are set manually by user input for the first slice. For the remaining slices, the initial contour position is estimated autonomously based on segmentation results of the previous slice. To obtain refined segmentation results, an adaptive control spacing algorithm is introduced into the active contour model. Moreover, a block search-based initial contour estimation procedure is proposed to ensure that the initial contour of each slice can be near the vessel boundary. Experiments were performed on synthetic and real chest CTA images. Compared with the well-known Chan-Vese (CV) model, the proposed algorithm exhibited better performance in segmentation and tracking. In particular, receiver operating characteristic analysis on the synthetic and real CTA images demonstrated the time efficiency and tracking robustness of the proposed model. In terms of computational time redundancy, processing time can be effectively reduced by approximately 20%.
Kalman Filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry.
Zhang, Yuxin; Chen, Shuo; Deng, Kexin; Chen, Bingyao; Wei, Xing; Yang, Jiafei; Wang, Shi; Ying, Kui
2017-01-01
To develop a self-adaptive and fast thermometry method by combining the original hybrid magnetic resonance thermometry method and the bio heat transfer equation (BHTE) model. The proposed Kalman filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry, abbreviated as KalBHT hybrid method, introduced the BHTE model to synthesize a window on the regularization term of the hybrid algorithm, which leads to a self-adaptive regularization both spatially and temporally with change of temperature. Further, to decrease the sensitivity to accuracy of the BHTE model, Kalman filter is utilized to update the window at each iteration time. To investigate the effect of the proposed model, computer heating simulation, phantom microwave heating experiment and dynamic in-vivo model validation of liver and thoracic tumor were conducted in this study. The heating simulation indicates that the KalBHT hybrid algorithm achieves more accurate results without adjusting λ to a proper value in comparison to the hybrid algorithm. The results of the phantom heating experiment illustrate that the proposed model is able to follow temperature changes in the presence of motion and the temperature estimated also shows less noise in the background and surrounding the hot spot. The dynamic in-vivo model validation with heating simulation demonstrates that the proposed model has a higher convergence rate, more robustness to susceptibility problem surrounding the hot spot and more accuracy of temperature estimation. In the healthy liver experiment with heating simulation, the RMSE of the hot spot of the proposed model is reduced to about 50% compared to the RMSE of the original hybrid model and the convergence time becomes only about one fifth of the hybrid model. The proposed model is able to improve the accuracy of the original hybrid algorithm and accelerate the convergence rate of MR temperature estimation.
Bukhari, W; Hong, S-M
2015-01-07
Motion-adaptive radiotherapy aims to deliver a conformal dose to the target tumour with minimal normal tissue exposure by compensating for tumour motion in real time. The prediction as well as the gating of respiratory motion have received much attention over the last two decades for reducing the targeting error of the treatment beam due to respiratory motion. In this article, we present a real-time algorithm for predicting and gating respiratory motion that utilizes a model-based and a model-free Bayesian framework by combining them in a cascade structure. The algorithm, named EKF-GPR(+), implements a gating function without pre-specifying a particular region of the patient's breathing cycle. The algorithm first employs an extended Kalman filter (LCM-EKF) to predict the respiratory motion and then uses a model-free Gaussian process regression (GPR) to correct the error of the LCM-EKF prediction. The GPR is a non-parametric Bayesian algorithm that yields predictive variance under Gaussian assumptions. The EKF-GPR(+) algorithm utilizes the predictive variance from the GPR component to capture the uncertainty in the LCM-EKF prediction error and systematically identify breathing points with a higher probability of large prediction error in advance. This identification allows us to pause the treatment beam over such instances. EKF-GPR(+) implements the gating function by using simple calculations based on the predictive variance with no additional detection mechanism. A sparse approximation of the GPR algorithm is employed to realize EKF-GPR(+) in real time. Extensive numerical experiments are performed based on a large database of 304 respiratory motion traces to evaluate EKF-GPR(+). The experimental results show that the EKF-GPR(+) algorithm effectively reduces the prediction error in a root-mean-square (RMS) sense by employing the gating function, albeit at the cost of a reduced duty cycle. As an example, EKF-GPR(+) reduces the patient-wise RMS error to 37%, 39% and
Adaptive UAV Attitude Estimation Employing Unscented Kalman Filter, FOAM and Low-Cost MEMS Sensors
de Marina, Héctor García; Espinosa, Felipe; Santos, Carlos
2012-01-01
Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance. PMID:23012559
Yoon, Paul K; Zihajehzadeh, Shaghayegh; Bong-Soo Kang; Park, Edward J
2015-08-01
This paper proposes a novel indoor localization method using the Bluetooth Low Energy (BLE) and an inertial measurement unit (IMU). The multipath and non-line-of-sight errors from low-power wireless localization systems commonly result in outliers, affecting the positioning accuracy. We address this problem by adaptively weighting the estimates from the IMU and BLE in our proposed cascaded Kalman filter (KF). The positioning accuracy is further improved with the Rauch-Tung-Striebel smoother. The performance of the proposed algorithm is compared against that of the standard KF experimentally. The results show that the proposed algorithm can maintain high accuracy for position tracking the sensor in the presence of the outliers.
Adaptive UAV attitude estimation employing unscented Kalman Filter, FOAM and low-cost MEMS sensors.
de Marina, Héctor García; Espinosa, Felipe; Santos, Carlos
2012-01-01
Navigation employing low cost MicroElectroMechanical Systems (MEMS) sensors in Unmanned Aerial Vehicles (UAVs) is an uprising challenge. One important part of this navigation is the right estimation of the attitude angles. Most of the existent algorithms handle the sensor readings in a fixed way, leading to large errors in different mission stages like take-off aerobatic maneuvers. This paper presents an adaptive method to estimate these angles using off-the-shelf components. This paper introduces an Attitude Heading Reference System (AHRS) based on the Unscented Kalman Filter (UKF) using the Fast Optimal Attitude Matrix (FOAM) algorithm as the observation model. The performance of the method is assessed through simulations. Moreover, field experiments are presented using a real fixed-wing UAV. The proposed low cost solution, implemented in a microcontroller, shows a satisfactory real time performance.
A hybrid robust fault tolerant control based on adaptive joint unscented Kalman filter.
Shabbouei Hagh, Yashar; Mohammadi Asl, Reza; Cocquempot, Vincent
2017-01-01
In this paper, a new hybrid robust fault tolerant control scheme is proposed. A robust H∞ control law is used in non-faulty situation, while a Non-Singular Terminal Sliding Mode (NTSM) controller is activated as soon as an actuator fault is detected. Since a linear robust controller is designed, the system is first linearized through the feedback linearization method. To switch from one controller to the other, a fuzzy based switching system is used. An Adaptive Joint Unscented Kalman Filter (AJUKF) is used for fault detection and diagnosis. The proposed method is based on the simultaneous estimation of the system states and parameters. In order to show the efficiency of the proposed scheme, a simulated 3-DOF robotic manipulator is used.
Hongda Wang; Chiu-Sing Choy
2016-08-01
The ability of correlation integral for automatic seizure detection using scalp EEG data has been re-examined in this paper. To facilitate the detection performance and overcome the shortcoming of correlation integral, nonlinear adaptive denoising and Kalman filter have been adopted for pre-processing and post-processing. The three-stage algorithm has achieved 84.6% sensitivity and 0.087/h false detection rate, which are comparable to many machine learning based methods, but at much lower computational cost. Since this algorithm is tested with long-term scalp EEG, it has the potential to achieve higher performance with intracranial EEG. The clinical value of this algorithm includes providing a pre-judgement to assist the doctor's diagnosis procedure and acting as a reliable warning system in a wearable device for epilepsy patients.
Adaptive fused Kalman filter based on imaging laser radar for TAN
NASA Astrophysics Data System (ADS)
Gong, Junbin; Xu, Hongbo; Tian, Jinwen; Cheng, Hua; Zhang, Jun
2007-11-01
Terrain aided navigation (TAN) is an efficient way to periodically correct the error accumulation of INS. The imaging laser radar is an ideal imaging sensor in TAN for the low-flying aircraft and unmanned air vehicles for the high precision multi-dimensional data acquisition capability and concealable attribute. In this paper, a new framework for applying the laser radar to terrain aided navigation is put forward. Then a new adaptive fused Kalman Filter is proposed to improve the accuracy and robustness. At last, the key factors affected the algorithm are analyzed and the comparative experimentations are presented. The simulating experiments show that the proposed algorithm improves the location accuracy, and has good initial error tolerance and fine robustness. It shows that this approach is a valid solution for the application.
NASA Astrophysics Data System (ADS)
Sun, Yong; Ma, Zilin; Tang, Gongyou; Chen, Zheng; Zhang, Nong
2016-07-01
Since the main power source of hybrid electric vehicle(HEV) is supplied by the power battery, the predicted performance of power battery, especially the state-of-charge(SOC) estimation has attracted great attention in the area of HEV. However, the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected. A variable structure extended kalman filter(VSEKF)-based estimation method, which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition, is presented. First, the general lower-order battery equivalent circuit model(GLM), which includes column accumulation model, open circuit voltage model and the SOC output model, is established, and the off-line and online model parameters are calculated with hybrid pulse power characteristics(HPPC) test data. Next, a VSEKF estimation method of SOC, which integrates the ampere-hour(Ah) integration method and the extended Kalman filter(EKF) method, is executed with different adaptive weighting coefficients, which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes. According to the experimental analysis, the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV. The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method. In Summary, the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system, which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method. The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.
Towards denoising XMCD movies of fast magnetization dynamics using extended Kalman filter.
Kopp, M; Harmeling, S; Schütz, G; Schölkopf, B; Fähnle, M
2015-01-01
The Kalman filter is a well-established approach to get information on the time-dependent state of a system from noisy observations. It was developed in the context of the Apollo project to see the deviation of the true trajectory of a rocket from the desired trajectory. Afterwards it was applied to many different systems with small numbers of components of the respective state vector (typically about 10). In all cases the equation of motion for the state vector was known exactly. The fast dissipative magnetization dynamics is often investigated by x-ray magnetic circular dichroism movies (XMCD movies), which are often very noisy. In this situation the number of components of the state vector is extremely large (about 10(5)), and the equation of motion for the dissipative magnetization dynamics (especially the values of the material parameters of this equation) is not well known. In the present paper it is shown by theoretical considerations that - nevertheless - there is no principle problem for the use of the Kalman filter to denoise XMCD movies of fast dissipative magnetization dynamics.
NASA Astrophysics Data System (ADS)
Meng, Yang; Gao, Shesheng; Zhong, Yongmin; Hu, Gaoge; Subic, Aleksandar
2016-03-01
The use of the direct filtering approach for INS/GNSS integrated navigation introduces nonlinearity into the system state equation. As the unscented Kalman filter (UKF) is a promising method for nonlinear problems, an obvious solution is to incorporate the UKF concept in the direct filtering approach to address the nonlinearity involved in INS/GNSS integrated navigation. However, the performance of the standard UKF is dependent on the accurate statistical characterizations of system noise. If the noise distributions of inertial instruments and GNSS receivers are not appropriately described, the standard UKF will produce deteriorated or even divergent navigation solutions. This paper presents an adaptive UKF with noise statistic estimator to overcome the limitation of the standard UKF. According to the covariance matching technique, the innovation and residual sequences are used to determine the covariance matrices of the process and measurement noises. The proposed algorithm can estimate and adjust the system noise statistics online, and thus enhance the adaptive capability of the standard UKF. Simulation and experimental results demonstrate that the performance of the proposed algorithm is significantly superior to that of the standard UKF and adaptive-robust UKF under the condition without accurate knowledge on system noise, leading to improved navigation precision.
NASA Astrophysics Data System (ADS)
Fallahi, Kia; Raoufi, Reza; Khoshbin, Hossein
2008-07-01
In recent years chaotic secure communication and chaos synchronization have received ever increasing attention. In this paper a chaotic communication method using extended Kalman filter is presented. The chaotic synchronization is implemented by EKF design in the presence of channel additive noise and processing noise. Encoding chaotic communication is used to achieve a satisfactory, typical secure communication scheme. In the proposed system, a multi-shift cipher algorithm is also used to enhance the security and the key cipher is chosen as one of the chaos states. The key estimate is employed to recover the primary data. To illustrate the effectiveness of the proposed scheme, a numerical example based on Chen dynamical system is presented and the results are compared to two other chaotic systems.
Wang, Zidong; Liu, Xiaohui; Liu, Yurong; Liang, Jinling; Vinciotti, Veronica
2009-01-01
In this paper, the extended Kalman filter (EKF) algorithm is applied to model the gene regulatory network from gene time series data. The gene regulatory network is considered as a nonlinear dynamic stochastic model that consists of the gene measurement equation and the gene regulation equation. After specifying the model structure, we apply the EKF algorithm for identifying both the model parameters and the actual value of gene expression levels. It is shown that the EKF algorithm is an online estimation algorithm that can identify a large number of parameters (including parameters of nonlinear functions) through iterative procedure by using a small number of observations. Four real-world gene expression data sets are employed to demonstrate the effectiveness of the EKF algorithm, and the obtained models are evaluated from the viewpoint of bioinformatics.
NASA Astrophysics Data System (ADS)
Yu, Qiuli
2001-12-01
Aircraft flight test data are processed by optimal estimation programs to estimate the aircraft state trajectory (3 DOF) and to identify the unknown parameters, including constant biases and scale factor of the measurement instrumentation system. The methods applied in processing aircraft flight test data are the iterative extended Kalman filter/smoother and fixed-point smoother (IEKFSFPS) method and the two-step estimator (TSE) method. The models of an aircraft flight dynamic system and measurement instrumentation system are established. The principles of IEKFSFPS and TSE methods are derived and summarized, and their algorithms are programmed with MATLAB codes. Several numerical experiments of flight data processing and parameter identification are carried out by using IEKFSFPS and TSE algorithm programs. Comparison and discussion of the simulation results with the two methods are made. The TSE+IEKFSFPS combination method is presented and proven to be effective and practical. Figures and tables of the results are presented.
Akhbari, Mahsa; Shamsollahi, Mohammad B; Jutten, Christian; Coppa, Bertrand
2012-01-01
In this paper an efficient filtering procedure based on Extended Kalman Filter (EKF) has been proposed. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. The proposed method considers the angular velocity of ECG signal, as one of the states of an EKF. We have considered two cases for observation equations, in one case we have assumed a corresponding observation to angular velocity state and in the other case, we have not assumed any observations for it. Quantitative evaluation of the proposed algorithm on the MIT-BIH Normal Sinus Rhythm Database (NSRDB) shows that an average SNR improvement of 8 dB is achieved for an input signal of -4 dB.
Wang, Qian; Molenaar, Peter; Harsh, Saurabh; Freeman, Kenneth; Xie, Jinyu; Gold, Carol; Rovine, Mike; Ulbrecht, Jan
2014-03-01
An essential component of any artificial pancreas is on the prediction of blood glucose levels as a function of exogenous and endogenous perturbations such as insulin dose, meal intake, and physical activity and emotional tone under natural living conditions. In this article, we present a new data-driven state-space dynamic model with time-varying coefficients that are used to explicitly quantify the time-varying patient-specific effects of insulin dose and meal intake on blood glucose fluctuations. Using the 3-variate time series of glucose level, insulin dose, and meal intake of an individual type 1 diabetic subject, we apply an extended Kalman filter (EKF) to estimate time-varying coefficients of the patient-specific state-space model. We evaluate our empirical modeling using (1) the FDA-approved UVa/Padova simulator with 30 virtual patients and (2) clinical data of 5 type 1 diabetic patients under natural living conditions. Compared to a forgetting-factor-based recursive ARX model of the same order, the EKF model predictions have higher fit, and significantly better temporal gain and J index and thus are superior in early detection of upward and downward trends in glucose. The EKF based state-space model developed in this article is particularly suitable for model-based state-feedback control designs since the Kalman filter estimates the state variable of the glucose dynamics based on the measured glucose time series. In addition, since the model parameters are estimated in real time, this model is also suitable for adaptive control.
Kang, Zhizhong; Chen, Jinlei; Wang, Baoqian
2015-01-01
Because tunnels generally have tubular shapes, the distribution of tie points between adjacent scans is usually limited to a narrow region, which makes the problem of registration error accumulation inevitable. In this paper, a global registration method is proposed based on an augmented extended Kalman filter and a central-axis constraint. The point cloud registration is regarded as a stochastic system, and the global registration is considered to be a process that recursively estimates the rigid transformation parameters between each pair of adjacent scans. Therefore, the augmented extended Kalman filter (AEKF) is used to accurately estimate the rigid transformation parameters by eliminating the error accumulation caused by the pair-wise registration. Moreover, because the scanning range of a terrestrial laser scanner can reach hundreds of meters, a single scan can cover a tunnel segment with a length of more than one hundred meters, which means that the central axis extracted from the scan can be employed to control the registration of multiple scans. Therefore, the central axis of the subway tunnel is first determined through the 2D projection of the tunnel point cloud and curve fitting using the RANSAC (RANdom SAmple Consensus) algorithm. Because the extraction of the central axis by quadratic curve fitting may suffer from noise in the tunnel points and from variations in the tunnel, we present a global extraction algorithm that is based on segment-wise quadratic curve fitting. We then derive the central-axis constraint as an additional observation model of AEKF to optimize the registration parameters between each pair of adjacent scans. The proposed approach is tested on terrestrial point clouds that were acquired in a subway tunnel. The results show that the proposed algorithm is capable of improving the accuracy of aligning multiple scans by 48%.
Phase and amplitude imaging from noisy images by Kalman filtering.
Waller, Laura; Tsang, Mankei; Ponda, Sameera; Yang, Se Young; Barbastathis, George
2011-01-31
We propose and demonstrate a computational method for complex-field imaging from many noisy intensity images with varying defocus, using an extended complex Kalman filter. The technique offers dynamic smoothing of noisy measurements and is recursive rather than iterative, so is suitable for adaptive measurements. The Kalman filter provides near-optimal results in very low-light situations and may be adapted to propagation through turbulent, scattering, or nonlinear media.
Rucci, Michael; Hardie, Russell C; Barnard, Kenneth J
2014-05-01
In this paper, we present a computationally efficient video restoration algorithm to address both blur and noise for a Nyquist sampled imaging system. The proposed method utilizes a temporal Kalman filter followed by a correlation-model based spatial adaptive Wiener filter (AWF). The Kalman filter employs an affine background motion model and novel process-noise variance estimate. We also propose and demonstrate a new multidelay temporal Kalman filter designed to more robustly treat local motion. The AWF is a spatial operation that performs deconvolution and adapts to the spatially varying residual noise left in the Kalman filter stage. In image areas where the temporal Kalman filter is able to provide significant noise reduction, the AWF can be aggressive in its deconvolution. In other areas, where less noise reduction is achieved with the Kalman filter, the AWF balances the deconvolution with spatial noise reduction. In this way, the Kalman filter and AWF work together effectively, but without the computational burden of full joint spatiotemporal processing. We also propose a novel hybrid system that combines a temporal Kalman filter and BM3D processing. To illustrate the efficacy of the proposed methods, we test the algorithms on both simulated imagery and video collected with a visible camera.
An extended Kalman-filter for regional scale inverse emission estimation
NASA Astrophysics Data System (ADS)
Brunner, D.; Henne, S.; Keller, C. A.; Reimann, S.; Vollmer, M. K.; O'Doherty, S.; Maione, M.
2012-04-01
A Kalman-filter based inverse emission estimation method for long-lived trace gases is presented for use in conjunction with a Lagrangian particle dispersion model like FLEXPART. The sequential nature of the approach allows tracing slow seasonal and interannual changes rather than estimating a single period-mean emission field. Other important features include the estimation of a slowly varying concentration background at each measurement station, the possibility to constrain the solution to non-negative emissions, the quantification of uncertainties, the consideration of temporal correlations in the residuals, and the applicability to potentially large inversion problems. The method is first demonstrated for a set of synthetic observations created from a prescribed emission field with different levels of (correlated) noise, which closely mimics true observations. It is then applied to real observations of the three halocarbons HFC-125, HFC-152a and HCFC-141b at the remote research stations Jungfraujoch and Mace Head for the quantification of emissions in Western European countries from 2006 to 2010. Estimated HFC-125 emissions are mostly consistent with national totals reported to UNFCCC in the framework of the Kyoto Protocol and show a generally increasing trend over the considered period. Results for HFC-152a are much more variable with estimated emissions being both higher and lower than reported emissions in different countries. The highest emissions of the order of 700-800 Mg yr-1 are estimated for Italy, which so far does not report HFC-152a emissions. Emissions of HCFC-141b show a continuing strong decrease as expected due to its controls in developed countries under the Montreal Protocol. Emissions from France, however, were still rather large, in the range of 700-1000 Mg yr-1 in the years 2006 and 2007 but strongly declined thereafter.
NASA Astrophysics Data System (ADS)
Sun, Leqiang; Seidou, Ousmane; Nistor, Ioan; Goïta, Kalifa; Magagi, Ramata
2016-12-01
The Extended Kalman Filter (EKF) is used to assimilate in situ surface soil moisture and streamflow observation at the outlet of an experimental watershed outlet into a semi-distributed SWAT (Soil and Water Assessment Tool) model. Watershed scale, instead of HRU scale soil moisture was used in state vector to reduce computational burden. Numerical experiments were designed to select the best state vector which consists of streamflow and soil moisture in all vertical soil layers. Compared to open-loop model and direct-insert method, the estimate of both soil moisture and streamflow has been improved by EKF assimilation. The combined assimilation of surface soil moisture and streamflow outperforms the assimilation with only surface soil moisture or streamflow especially in the estimate of full profile soil moisture. The NSC has been improved to 0.63 from -4.45 and the RMSE has been reduced to 12.34 mm from 47.44 mm in open-loop. Such improvement is also reflected in the short term forecast of soil moisture. The improvement of streamflow prediction is relatively moderate in both simulation and forecast mode compared to quality of the soil moisture prediction. The quantification of the model error, especially the error covariance between different state variables, was found to be critical to the estimate of the state variable corresponding to the error covariance.
NASA Astrophysics Data System (ADS)
Quang Truong, Dinh; Ahn, Kyoung Kwan
2014-07-01
An ion polymer metal composite (IPMC) is an electroactive polymer that bends in response to a small applied electric field as a result of mobility of cations in the polymer network and vice versa. This paper presents an innovative and accurate nonlinear black-box model (NBBM) for estimating the bending behavior of IPMC actuators. The model is constructed via a general multilayer perceptron neural network (GMLPNN) integrated with a smart learning mechanism (SLM) that is based on an extended Kalman filter with self-decoupling ability (SDEKF). Here the GMLPNN is built with an ability to autoadjust its structure based on its characteristic vector. Furthermore, by using the SLM based on the SDEKF, the GMLPNN parameters are optimized with small computational effort, and the modeling accuracy is improved. An apparatus employing an IPMC actuator is first set up to investigate the IPMC characteristics and to generate the data for training and validating the model. The advanced NBBM model for the IPMC system is then created with the proper inputs to estimate IPMC tip displacement. Next, the model is optimized using the SLM mechanism with the training data. Finally, the optimized NBBM model is verified with the validating data. A comparison between this model and the previously developed model is also carried out to prove the effectiveness of the proposed modeling technique.
Ligorio, Gabriele; Sabatini, Angelo Maria
2013-02-04
In this paper measurements from a monocular vision system are fused with inertial/magnetic measurements from an Inertial Measurement Unit (IMU) rigidly connected to the camera. Two Extended Kalman filters (EKFs) were developed to estimate the pose of the IMU/camera sensor moving relative to a rigid scene (ego-motion), based on a set of fiducials. The two filters were identical as for the state equation and the measurement equations of the inertial/magnetic sensors. The DLT-based EKF exploited visual estimates of the ego-motion using a variant of the Direct Linear Transformation (DLT) method; the error-driven EKF exploited pseudo-measurements based on the projection errors from measured two-dimensional point features to the corresponding three-dimensional fiducials. The two filters were off-line analyzed in different experimental conditions and compared to a purely IMU-based EKF used for estimating the orientation of the IMU/camera sensor. The DLT-based EKF was more accurate than the error-driven EKF, less robust against loss of visual features, and equivalent in terms of computational complexity. Orientation root mean square errors (RMSEs) of 1° (1.5°), and position RMSEs of 3.5 mm (10 mm) were achieved in our experiments by the DLT-based EKF (error-driven EKF); by contrast, orientation RMSEs of 1.6° were achieved by the purely IMU-based EKF.
Rahimpour, M; Mohammadzadeh Asl, B
2016-07-01
Monitoring atrial activity via P waves, is an important feature of the arrhythmia detection procedure. The aim of this paper is to present an algorithm for P wave detection in normal and some abnormal records by improving existing methods in the field of signal processing. In contrast to the classical approaches, which are completely blind to signal dynamics, our proposed method uses the extended Kalman filter, EKF25, to estimate the state variables of the equations modeling the dynamic of an ECG signal. This method is a modified version of the nonlinear dynamical model previously introduced for a generation of synthetic ECG signals and fiducial point extraction in normal ones. It is capable of estimating the separate types of activity of the heart with reasonable accuracy and performs well in the presence of morphological variations in the waveforms and ectopic beats. The MIT-BIH Arrhythmia and QT databases have been used to evaluate the performance of the proposed method. The results show that this method has Se = 98.38% and Pr = 96.74% in the overall records (considering normal and abnormal rhythms).
NASA Technical Reports Server (NTRS)
Lary, David J.; Mussa, Yussuf
2004-01-01
In this study a new extended Kalman filter (EKF) learning algorithm for feed-forward neural networks (FFN) is used. With the EKF approach, the training of the FFN can be seen as state estimation for a non-linear stationary process. The EKF method gives excellent convergence performances provided that there is enough computer core memory and that the machine precision is high. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). The neural network was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9997. The neural network Fortran code used is available for download.
Zeng, Nianyin; Wang, Zidong; Li, Yurong; Du, Min; Liu, Xiaohui
2011-07-01
In this paper, a mathematical model for sandwich-type lateral flow immunoassay is developed via short available time series. A nonlinear dynamic stochastic model is considered that consists of the biochemical reaction system equations and the observation equation. After specifying the model structure, we apply the extended Kalman filter (EKF) algorithm for identifying both the states and parameters of the nonlinear state-space model. It is shown that the EKF algorithm can accurately identify the parameters and also predict the system states in the nonlinear dynamic stochastic model through an iterative procedure by using a small number of observations. The identified mathematical model provides a powerful tool for testing the system hypotheses and also for inspecting the effects from various design parameters in both rapid and inexpensive way. Furthermore, by means of the established model, the dynamic changes in the concentration of antigens and antibodies can be predicted, thereby making it possible for us to analyze, optimize, and design the properties of lateral flow immunoassay devices.
Ligorio, Gabriele; Sabatini, Angelo Maria
2013-01-01
In this paper measurements from a monocular vision system are fused with inertial/magnetic measurements from an Inertial Measurement Unit (IMU) rigidly connected to the camera. Two Extended Kalman filters (EKFs) were developed to estimate the pose of the IMU/camera sensor moving relative to a rigid scene (ego-motion), based on a set of fiducials. The two filters were identical as for the state equation and the measurement equations of the inertial/magnetic sensors. The DLT-based EKF exploited visual estimates of the ego-motion using a variant of the Direct Linear Transformation (DLT) method; the error-driven EKF exploited pseudo-measurements based on the projection errors from measured two-dimensional point features to the corresponding three-dimensional fiducials. The two filters were off-line analyzed in different experimental conditions and compared to a purely IMU-based EKF used for estimating the orientation of the IMU/camera sensor. The DLT-based EKF was more accurate than the error-driven EKF, less robust against loss of visual features, and equivalent in terms of computational complexity. Orientation root mean square errors (RMSEs) of 1° (1.5°), and position RMSEs of 3.5 mm (10 mm) were achieved in our experiments by the DLT-based EKF (error-driven EKF); by contrast, orientation RMSEs of 1.6° were achieved by the purely IMU-based EKF. PMID:23385409
Bonnet, V; Dumas, R; Cappozzo, A; Joukov, V; Daune, G; Kulić, D; Fraisse, P; Andary, S; Venture, G
2016-12-29
This paper presents a method for real-time estimation of the kinematics and kinetics of a human body performing a sagittal symmetric motor task, which would minimize the impact of the stereophotogrammetric soft tissue artefacts (STA). The method is based on a bi-dimensional mechanical model of the locomotor apparatus the state variables of which (joint angles, velocities and accelerations, and the segments lengths and inertial parameters) are estimated by a constrained extended Kalman filter (CEKF) that fuses input information made of both stereophotogrammetric and dynamometric measurement data. Filter gains are made to saturate in order to obtain plausible state variables and the measurement covariance matrix of the filter accounts for the expected STA maximal amplitudes. We hypothesised that the ensemble of constraints and input redundant information would allow the method to attenuate the STA propagation to the end results. The method was evaluated in ten human subjects performing a squat exercise. The CEKF estimated and measured skin marker trajectories exhibited a RMS difference lower than 4mm, thus in the range of STAs. The RMS differences between the measured ground reaction force and moment and those estimated using the proposed method (9N and 10Nm) were much lower than obtained using a classical inverse dynamics approach (22N and 30Nm). From the latter results it may be inferred that the presented method allows for a significant improvement of the accuracy with which kinematic variables and relevant time derivatives, model parameters and, therefore, intersegmental moments are estimated.
NASA Astrophysics Data System (ADS)
Ghasemi, S.; Khorasani, K.
2015-10-01
In this paper, the problem of fault detection and isolation (FDI) of the attitude control subsystem (ACS) of spacecraft formation flying systems is considered. For developing the FDI schemes, an extended Kalman filter (EKF) is utilised which belongs to a class of nonlinear state estimation methods. Three architectures, namely centralised, decentralised, and semi-decentralised, are considered and the corresponding FDI strategies are designed and constructed. Appropriate residual generation techniques and threshold selection criteria are proposed for these architectures. The capabilities of the proposed architectures for accomplishing the FDI tasks are studied through extensive numerical simulations for a team of four satellites in formation flight. Using a confusion matrix evaluation criterion, it is shown that the centralised architecture can achieve the most reliable results relative to the semi-decentralised and decentralised architectures at the expense of availability of a centralised processing module that requires the entire team information set. On the other hand, the semi-decentralised performance is close to the centralised scheme without relying on the availability of the entire team information set. Furthermore, the results confirm that the FDI results in formations with angular velocity measurement sensors achieve higher level of accuracy, true faulty, and precision, along with lower level of false healthy misclassification as compared to the formations that utilise attitude measurement sensors.
NASA Astrophysics Data System (ADS)
Luque, Pablo; Mántaras, Daniel A.; Fidalgo, Eloy; Álvarez, Javier; Riva, Paolo; Girón, Pablo; Compadre, Diego; Ferran, Jordi
2013-12-01
The main objective of this work is to determine the limit of safe driving conditions by identifying the maximal friction coefficient in a real vehicle. The study will focus on finding a method to determine this limit before reaching the skid, which is valuable information in the context of traffic safety. Since it is not possible to measure the friction coefficient directly, it will be estimated using the appropriate tools in order to get the most accurate information. A real vehicle is instrumented to collect information of general kinematics and steering tie-rod forces. A real-time algorithm is developed to estimate forces and aligning torque in the tyres using an extended Kalman filter and neural networks techniques. The methodology is based on determining the aligning torque; this variable allows evaluation of the behaviour of the tyre. It transmits interesting information from the tyre-road contact and can be used to predict the maximal tyre grip and safety margin. The maximal grip coefficient is estimated according to a knowledge base, extracted from computer simulation of a high detailed three-dimensional model, using Adams® software. The proposed methodology is validated and applied to real driving conditions, in which maximal grip and safety margin are properly estimated.
NASA Astrophysics Data System (ADS)
Pan, M.-Ch.; Chu, W.-Ch.; Le, Duc-Do
2016-12-01
The paper presents an alternative Vold-Kalman filter order tracking (VKF_OT) method, i.e. adaptive angular-velocity VKF_OT technique, to extract and characterize order components in an adaptive manner for the condition monitoring and fault diagnosis of rotary machinery. The order/spectral waveforms to be tracked can be recursively solved by using Kalman filter based on the one-step state prediction. The paper comprises theoretical derivation of computation scheme, numerical implementation, and parameter investigation. Comparisons of the adaptive VKF_OT scheme with two other ones are performed through processing synthetic signals of designated order components. Processing parameters such as the weighting factor and the correlation matrix of process noise, and data conditions like the sampling frequency, which influence tracking behavior, are explored. The merits such as adaptive processing nature and computation efficiency brought by the proposed scheme are addressed although the computation was performed in off-line conditions. The proposed scheme can simultaneously extract multiple spectral components, and effectively decouple close and crossing orders associated with multi-axial reference rotating speeds.
Fetal ECG extraction by extended state Kalman filtering based on single-channel recordings.
Niknazar, Mohammad; Rivet, Bertrand; Jutten, Christian
2013-05-01
In this paper, we present an extended nonlinear Bayesian filtering framework for extracting electrocardiograms (ECGs) from a single channel as encountered in the fetal ECG extraction from abdominal sensor. The recorded signals are modeled as the summation of several ECGs. Each of them is described by a nonlinear dynamic model, previously presented for the generation of a highly realistic synthetic ECG. Consequently, each ECG has a corresponding term in this model and can thus be efficiently discriminated even if the waves overlap in time. The parameter sensitivity analysis for different values of noise level, amplitude, and heart rate ratios between fetal and maternal ECGs shows its effectiveness for a large set of values of these parameters. This framework is also validated on the extractions of fetal ECG from actual abdominal recordings, as well as of actual twin magnetocardiograms.
Wang, Baofeng; Qi, Zhiquan; Chen, Sizhong; Liu, Zhaodu; Ma, Guocheng
2017-01-01
Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness.
Wang, Baofeng; Qi, Zhiquan; Chen, Sizhong; Liu, Zhaodu; Ma, Guocheng
2017-01-01
Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness. PMID:28296902
Akhbari, Mahsa; Shamsollahi, Mohammad B; Jutten, Christian; Armoundas, Antonis A; Sayadi, Omid
2016-02-01
In this paper we propose an efficient method for denoising and extracting fiducial point (FP) of ECG signals. The method is based on a nonlinear dynamic model which uses Gaussian functions to model ECG waveforms. For estimating the model parameters, we use an extended Kalman filter (EKF). In this framework called EKF25, all the parameters of Gaussian functions as well as the ECG waveforms (P-wave, QRS complex and T-wave) in the ECG dynamical model, are considered as state variables. In this paper, the dynamic time warping method is used to estimate the nonlinear ECG phase observation. We compare this new approach with linear phase observation models. Using linear and nonlinear EKF25 for ECG denoising and nonlinear EKF25 for fiducial point extraction and ECG interval analysis are the main contributions of this paper. Performance comparison with other EKF-based techniques shows that the proposed method results in higher output SNR with an average SNR improvement of 12 dB for an input SNR of -8 dB. To evaluate the FP extraction performance, we compare the proposed method with a method based on partially collapsed Gibbs sampler and an established EKF-based method. The mean absolute error and the root mean square error of all FPs, across all databases are 14 ms and 22 ms, respectively, for our proposed method, with an advantage when using a nonlinear phase observation. These errors are significantly smaller than errors obtained with other methods. For ECG interval analysis, with an absolute mean error and a root mean square error of about 22 ms and 29 ms, the proposed method achieves better accuracy and smaller variability with respect to other methods.
NASA Astrophysics Data System (ADS)
Nenna, Vanessa; Pidlisecky, Adam; Knight, Rosemary
2011-10-01
We apply an extended Kalman filter (EKF) approach to inversion of time-lapse electrical resistivity imaging (ERI) field data. The EKF is a method of time series signal processing that incorporates both a state evolution model, describing changes in the physical system, and an observation model, incorporating the physics of the electrical resistivity measurement. We test the feasibility of using an EKF approach to inverting ERI data collected with 2-D surface array geometries. As a first test, we invert synthetic data generated using a simulated recharge event and water saturation distributions converted to electrical conductivity values using an Archie's law relationship. In the synthetic example we demonstrate the impact that the noise structure of the state evolution and the regularization weight have on EKF-estimated model parameters and errors. We then apply the method to inversion of field data collected to monitor changes in electrical conductivity beneath a recharge pond that is part of an aquifer storage and recovery project in northern California. Using lines of electrodes buried at a depth of 0.25 m when the base of the pond is dry, we monitor the wetting front associated with the diversion of stormflow runoff to the pond. Using field data, we demonstrate that by oversampling in time, we are able to apply the so-called random walk model for the state evolution and to build the model of observation noise directly from collected data. EKF-estimated values track changes in conductivity associated with both increasing water content in subsurface sediments and changes in the properties of the pore water, showing the method is a feasible approach for inversion of time-lapse ERI field data.
NASA Astrophysics Data System (ADS)
Zhang, Weige; Shi, Wei; Ma, Zeyu
2015-09-01
Accurate estimations of battery energy and available power capability are of great of importance for realizing an efficient and reliable operation of electric vehicles. To improve the estimation accuracy and reliability for battery state of energy and power capability, a novel model-based joint estimation approach has been proposed against uncertain external operating conditions and internal degradation status of battery cells. Firstly, it proposes a three-dimensional response surface open circuit voltage model to calibrate the estimation inaccuracies of battery state of energy. Secondly, the adaptive unscented Kalman filter (AUKF) is employed to develop a novel model-based joint state estimator for battery state of energy and power capability. The AUKF algorithm utilizes the well-known features of the Kalman filter but employs the method of unscented transform (UT) and adaptive error covariance matching technology to improve the state estimation accuracy. Thirdly, the proposed joint estimator has been verified by a LiFePO4 lithium-ion battery cell under different operating temperatures and aging levels. The result indicates that the estimation errors of battery voltage and state-of-energy are less than 2% even if given a large erroneous initial value, which makes the state of available power capability predict more accurate and reliable for the electric vehicles application.
NASA Astrophysics Data System (ADS)
Olivares, A.; Górriz, J. M.; Ramírez, J.; Olivares, G.
2011-02-01
Inertial sensors are widely used in human body motion monitoring systems since they permit us to determine the position of the subject's limbs. Limb angle measurement is carried out through the integration of the angular velocity measured by a rate sensor and the decomposition of the components of static gravity acceleration measured by an accelerometer. Different factors derived from the sensors' nature, such as the angle random walk and dynamic bias, lead to erroneous measurements. Dynamic bias effects can be reduced through the use of adaptive filtering based on sensor fusion concepts. Most existing published works use a Kalman filtering sensor fusion approach. Our aim is to perform a comparative study among different adaptive filters. Several least mean squares (LMS), recursive least squares (RLS) and Kalman filtering variations are tested for the purpose of finding the best method leading to a more accurate and robust limb angle measurement. A new angle wander compensation sensor fusion approach based on LMS and RLS filters has been developed.
Noise adaptive fading Kalman filter for free-space laser communication beacon tracking.
Li, Lixing; Huang, Yongmei; Wang, Qiang; Yang, Fasheng
2016-10-20
We proposed a prediction algorithm for laser communication pointing, acquisition, and tracking (PAT) subsystems in order to further improve PAT accuracy and reduce the effect of processing delay. In terms of this prediction algorithm, a fading Kalman filter is employed, with the observation noise obtained by the gray value distribution of the laser images. Moreover, to better fit the dynamics of a laser target, the two-stage dynamic model has been chosen as the state transition model for Kalman filtering. In addition, the two-stage dynamic model has been modified by accommodating its form to a change of time lag, thereby compensating the effect of time delay. A series of horizontal path (17 km) experiments under different atmospheric conditions were conducted in the fields. According to the experimental results, the algorithm we proposed could effectively reduce the tracking error and improve pointing accuracy.
Generic Kalman Filter Software
NASA Technical Reports Server (NTRS)
Lisano, Michael E., II; Crues, Edwin Z.
2005-01-01
The Generic Kalman Filter (GKF) software provides a standard basis for the development of application-specific Kalman-filter programs. Historically, Kalman filters have been implemented by customized programs that must be written, coded, and debugged anew for each unique application, then tested and tuned with simulated or actual measurement data. Total development times for typical Kalman-filter application programs have ranged from months to weeks. The GKF software can simplify the development process and reduce the development time by eliminating the need to re-create the fundamental implementation of the Kalman filter for each new application. The GKF software is written in the ANSI C programming language. It contains a generic Kalman-filter-development directory that, in turn, contains a code for a generic Kalman filter function; more specifically, it contains a generically designed and generically coded implementation of linear, linearized, and extended Kalman filtering algorithms, including algorithms for state- and covariance-update and -propagation functions. The mathematical theory that underlies the algorithms is well known and has been reported extensively in the open technical literature. Also contained in the directory are a header file that defines generic Kalman-filter data structures and prototype functions and template versions of application-specific subfunction and calling navigation/estimation routine code and headers. Once the user has provided a calling routine and the required application-specific subfunctions, the application-specific Kalman-filter software can be compiled and executed immediately. During execution, the generic Kalman-filter function is called from a higher-level navigation or estimation routine that preprocesses measurement data and post-processes output data. The generic Kalman-filter function uses the aforementioned data structures and five implementation- specific subfunctions, which have been developed by the user on
NASA Astrophysics Data System (ADS)
Jiang, Xiao-Yu; Zong, Yan-Tao; Wang, Xi; Chen, Zhuo; Liu, Zhong-Xuan
2010-11-01
MEMS gyro is used in inertial measuring fields more and more widely, but random drift is considered as an important error restricting the precision of it. Establishing the proper models closed to actual state of movement and random drift, and designing a kind of effective filter are available to enhance the precision of the MEMS gyro. The dynamic model of angle movement is studied, the ARMA model describing random drift is established based on time series analysis method, and a modified self-adapted Kalman filter is designed for the signal processing. Finally, the random drift is distinguished and analyzed clearly by Allan variance. It is included that the above method can effectively eliminate the random drift and improve the precision of MEMS gyro.
Extended adaptive filtering for wide-angle SAR image formation
NASA Astrophysics Data System (ADS)
Wang, Yanwei; Roberts, William; Li, Jian
2005-05-01
For two-dimensional (2-D) spectral analysis, the adaptive filtering based technologies, such as CAPON and APES (Amplitude and Phase EStimation), are developed under the implicit assumption that the data sets are rectangular. However, in real SAR applications, especially for the wide-angle cases, the collected data sets are always non-rectangular. This raises the problem of how to extend the original adaptive filtering based algorithms for such kind of scenarios. In this paper, we propose an extended adaptive filtering (EAF) approach, which includes Extended APES (E-APES) and Extended CAPON (E-CAPON), for arbitrarily shaped 2-D data. The EAF algorithms adopt a missing-data approach where the unavailable data samples close to the collected data set are assumed missing. Using a group of filter-banks with varying sizes, these algorithms are non-iterative and do not require the estimation of the unavailable samples. The improved imaging results of the proposed algorithms are demonstrated by applying them to two different SAR data sets.
Gray, Morgan; Petit, Cyril; Rodionov, Sergey; Bocquet, Marc; Bertino, Laurent; Ferrari, Marc; Fusco, Thierry
2014-08-25
We propose a new algorithm for an adaptive optics system control law, based on the Linear Quadratic Gaussian approach and a Kalman Filter adaptation with localizations. It allows to handle non-stationary behaviors, to obtain performance close to the optimality defined with the residual phase variance minimization criterion, and to reduce the computational burden with an intrinsically parallel implementation on the Extremely Large Telescopes (ELTs).
Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan
2015-01-01
We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to −2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation. PMID:25985165
Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan
2015-05-13
We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to -2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.
Tsanas, Athanasios; Zañartu, Matías; Little, Max A; Fox, Cynthia; Ramig, Lorraine O; Clifford, Gari D
2014-05-01
There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F(0)) of speech signals. This study examines ten F(0) estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F(0) in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F(0) estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F(0) estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F(0) estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F(0) estimation is required.
Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao
2016-07-12
In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.
Tsanas, Athanasios; Zañartu, Matías; Little, Max A.; Fox, Cynthia; Ramig, Lorraine O.; Clifford, Gari D.
2014-01-01
There has been consistent interest among speech signal processing researchers in the accurate estimation of the fundamental frequency (F0) of speech signals. This study examines ten F0 estimation algorithms (some well-established and some proposed more recently) to determine which of these algorithms is, on average, better able to estimate F0 in the sustained vowel /a/. Moreover, a robust method for adaptively weighting the estimates of individual F0 estimation algorithms based on quality and performance measures is proposed, using an adaptive Kalman filter (KF) framework. The accuracy of the algorithms is validated using (a) a database of 117 synthetic realistic phonations obtained using a sophisticated physiological model of speech production and (b) a database of 65 recordings of human phonations where the glottal cycles are calculated from electroglottograph signals. On average, the sawtooth waveform inspired pitch estimator and the nearly defect-free algorithms provided the best individual F0 estimates, and the proposed KF approach resulted in a ∼16% improvement in accuracy over the best single F0 estimation algorithm. These findings may be useful in speech signal processing applications where sustained vowels are used to assess vocal quality, when very accurate F0 estimation is required. PMID:24815269
Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao
2016-01-01
In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved. PMID:27420062
NASA Astrophysics Data System (ADS)
Wang, Yiwei; Binaud, Nicolas; Gogu, Christian; Bes, Christian; Fu, Jian
2016-12-01
Prediction of fatigue crack length in aircraft fuselage panels is one of the key issues for aircraft structural safety since it helps prevent catastrophic failures. Accurate estimation of crack length propagation is also meaningful for helping develop aircraft maintenance strategies. Paris' law is often used to capture the dynamics of fatigue crack propagation in metallic material. However, uncertainties are often present in the crack growth model, measured crack size and pressure differential in each flight and need to be accounted for accurate prediction. The aim of this paper is to estimate the two unknown Paris' law constants m and C as well as the crack length evolution by taking into account these uncertainties. Due to the nonlinear nature of the Paris' law, we propose here an on-line estimation algorithm based on two widespread nonlinear filtering techniques, Extended Kalman filter (EKF) and Unscented Kalman filter (UKF). The numerical experiments indicate that both EKF and UKF estimated the crack length well and accurately identified the unknown parameters. Although UKF is theoretical superior to EKF, in this Paris' law application EKF is comparable in accuracy to UKF and requires less computational expense.
NASA Astrophysics Data System (ADS)
Lv, Chen; Zhang, Junzhi; Li, Yutong
2014-11-01
Because of the damping and elastic properties of an electrified powertrain, the regenerative brake of an electric vehicle (EV) is very different from a conventional friction brake with respect to the system dynamics. The flexibility of an electric drivetrain would have a negative effect on the blended brake control performance. In this study, models of the powertrain system of an electric car equipped with an axle motor are developed. Based on these models, the transfer characteristics of the motor torque in the driveline and its effect on blended braking control performance are analysed. To further enhance a vehicle's brake performance and energy efficiency, blended braking control algorithms with compensation for the powertrain flexibility are proposed using an extended Kalman filter. These algorithms are simulated under normal deceleration braking. The results show that the brake performance and blended braking control accuracy of the vehicle are significantly enhanced by the newly proposed algorithms.
NASA Technical Reports Server (NTRS)
Deutschmann, Julie; Bar-Itzhack, Itzhack
1997-01-01
Traditionally satellite attitude and trajectory have been estimated with completely separate systems, using different measurement data. The estimation of both trajectory and attitude for low earth orbit satellites has been successfully demonstrated in ground software using magnetometer and gyroscope data. Since the earth's magnetic field is a function of time and position, and since time is known quite precisely, the differences between the computed and measured magnetic field components, as measured by the magnetometers throughout the entire spacecraft orbit, are a function of both the spacecraft trajectory and attitude errors. Therefore, these errors can be used to estimate both trajectory and attitude. This work further tests the single augmented Extended Kalman Filter (EKF) which simultaneously and autonomously estimates spacecraft trajectory and attitude with data from the Rossi X-Ray Timing Explorer (RXTE) magnetometer and gyro-measured body rates. In addition, gyro biases are added to the state and the filter's ability to estimate them is presented.
Luo, Yong; Wu, Wenqi; Babu, Ravindra; Tang, Kanghua; Luo, Bing
2012-01-01
COMPASS is an indigenously developed Chinese global navigation satellite system and will share many features in common with GPS (Global Positioning System). Since the ultra-tight GPS/INS (Inertial Navigation System) integration shows its advantage over independent GPS receivers in many scenarios, the federated ultra-tight COMPASS/INS integration has been investigated in this paper, particularly, by proposing a simplified prefilter model. Compared with a traditional prefilter model, the state space of this simplified system contains only carrier phase, carrier frequency and carrier frequency rate tracking errors. A two-quadrant arctangent discriminator output is used as a measurement. Since the code tracking error related parameters were excluded from the state space of traditional prefilter models, the code/carrier divergence would destroy the carrier tracking process, and therefore an adaptive Kalman filter algorithm tuning process noise covariance matrix based on state correction sequence was incorporated to compensate for the divergence. The federated ultra-tight COMPASS/INS integration was implemented with a hardware COMPASS intermediate frequency (IF), and INS's accelerometers and gyroscopes signal sampling system. Field and simulation test results showed almost similar tracking and navigation performances for both the traditional prefilter model and the proposed system; however, the latter largely decreased the computational load.
An extended framework for adaptive playback-based video summarization
NASA Astrophysics Data System (ADS)
Peker, Kadir A.; Divakaran, Ajay
2003-11-01
In our previous work, we described an adaptive fast playback framework for video summarization where we changed the playback rate using the motion activity feature so as to maintain a constant "pace." This method provides an effective way of skimming through video, especially when the motion is not too complex and the background is mostly still, such as in surveillance video. In this paper, we present an extended summarization framework that, in addition to motion activity, uses semantic cues such as face or skin color appearance, speech and music detection, or other domain dependent semantically significant events to control the playback rate. The semantic features we use are computationally inexpensive and can be computed in compressed domain, yet are robust, reliable, and have a wide range of applicability across different content types. The presented framework also allows for adaptive summaries based on preference, for example, to include more dramatic vs. action elements, or vice versa. The user can switch at any time between the skimming and the normal playback modes. The continuity of the video is preserved, and complete omission of segments that may be important to the user is avoided by using adaptive fast playback instead of skipping over long segments. The rule-set and the input parameters can be further modified to fit a certain domain or application. Our framework can be used by itself, or as a subsequent presentation stage for a summary produced by any other summarization technique that relies on generating a sub-set of the content.
Han, Houzeng; Xu, Tianhe; Wang, Jian
2016-07-08
Precise Point Positioning (PPP) makes use of the undifferenced pseudorange and carrier phase measurements with ionospheric-free (IF) combinations to achieve centimeter-level positioning accuracy. Conventionally, the IF ambiguities are estimated as float values. To improve the PPP positioning accuracy and shorten the convergence time, the integer phase clock model with between-satellites single-difference (BSSD) operation is used to recover the integer property. However, the continuity and availability of stand-alone PPP is largely restricted by the observation environment. The positioning performance will be significantly degraded when GPS operates under challenging environments, if less than five satellites are present. A commonly used approach is integrating a low cost inertial sensor to improve the positioning performance and robustness. In this study, a tightly coupled (TC) algorithm is implemented by integrating PPP with inertial navigation system (INS) using an Extended Kalman filter (EKF). The navigation states, inertial sensor errors and GPS error states are estimated together. The troposphere constrained approach, which utilizes external tropospheric delay as virtual observation, is applied to further improve the ambiguity-fixed height positioning accuracy, and an improved adaptive filtering strategy is implemented to improve the covariance modelling considering the realistic noise effect. A field vehicular test with a geodetic GPS receiver and a low cost inertial sensor was conducted to validate the improvement on positioning performance with the proposed approach. The results show that the positioning accuracy has been improved with inertial aiding. Centimeter-level positioning accuracy is achievable during the test, and the PPP/INS TC integration achieves a fast re-convergence after signal outages. For troposphere constrained solutions, a significant improvement for the height component has been obtained. The overall positioning accuracies of the height
Han, Houzeng; Xu, Tianhe; Wang, Jian
2016-01-01
Precise Point Positioning (PPP) makes use of the undifferenced pseudorange and carrier phase measurements with ionospheric-free (IF) combinations to achieve centimeter-level positioning accuracy. Conventionally, the IF ambiguities are estimated as float values. To improve the PPP positioning accuracy and shorten the convergence time, the integer phase clock model with between-satellites single-difference (BSSD) operation is used to recover the integer property. However, the continuity and availability of stand-alone PPP is largely restricted by the observation environment. The positioning performance will be significantly degraded when GPS operates under challenging environments, if less than five satellites are present. A commonly used approach is integrating a low cost inertial sensor to improve the positioning performance and robustness. In this study, a tightly coupled (TC) algorithm is implemented by integrating PPP with inertial navigation system (INS) using an Extended Kalman filter (EKF). The navigation states, inertial sensor errors and GPS error states are estimated together. The troposphere constrained approach, which utilizes external tropospheric delay as virtual observation, is applied to further improve the ambiguity-fixed height positioning accuracy, and an improved adaptive filtering strategy is implemented to improve the covariance modelling considering the realistic noise effect. A field vehicular test with a geodetic GPS receiver and a low cost inertial sensor was conducted to validate the improvement on positioning performance with the proposed approach. The results show that the positioning accuracy has been improved with inertial aiding. Centimeter-level positioning accuracy is achievable during the test, and the PPP/INS TC integration achieves a fast re-convergence after signal outages. For troposphere constrained solutions, a significant improvement for the height component has been obtained. The overall positioning accuracies of the height
NASA Technical Reports Server (NTRS)
Gourdeau, L.; Verron, J.; Murtugudde, R.; Busalacchi, A. J.
1997-01-01
A new implementation of the extended Kaman filter is developed for the purpose of assimilating altimetric observations into a primitive equation model of the tropical Pacific. Its specificity consists in defining the errors into a reduced basis that evolves in time with the model dynamic. Validation by twin experiments is conducted and the method is shown to be efficient in quasi real conditions. Data from the first 2 years of the Topex/Poseidon mission are assimilated into the Gent & Cane [1989] model. Assimilation results are evaluated against independent in situ data, namely TAO mooring observations.
NASA Astrophysics Data System (ADS)
Laborie, Vanessya; Hissel, François; Sergent, Philippe; Fayet, Laurence; de Bruyn, Bertrand; Le Pelletier, Thierry
2014-05-01
Operational flood forecasting nowadays usually makes use of hydraulic numerical models fed by hydrological (rainfall-runoff) models. Those latter, which allow to obtain discharges to inject at the input of hydraulic numerical models, are very often calibrated once and for all only for specific events. In other hydrological situations, their results significantly differ from measurements at hydrometric stations of the watershed. The use of data assimilation methods may then be useful to "incorporate" available observations during an event as they become available and therefore to "readjust" the forecasts of the model by bringing them back as close as possible to the reality. A numerical flood forecasting model has been developed for the Service in charge of flood forecasting for Oise and Aisne watersheds. It is divided into two models : the first model is a hydrological numerical model which provides discharges at each station of the watersheds of the Oise and Aisne rivers. It is based on the software HYDRABV-SCS-PQ developed by HYDRATEC and feeds a numerical hydraulic model with the input discharges at different points (Hirson for the Oise river, Verrières and Amblaincourt on the Aisne river), as well as the discharges of the main tributaries.CEREMA has developed an extended Kalman filter based on the hydrological model SCS-PQ which aim is to assimilate available discharges data at several hydrometric stations of the watershed and, by this way, improve flood forecasts. While this method often relies on assumptions about the differentiability of the underlying model, special techniques were applied here to account for the numerous thresholds where the hydrological model is not differentiable. The object of the presentation is to explain the methodology used and describe several results obtained for two periods (2007-2008 and 2009-2010) for the station of Hirson on the Oise river. For these two periods, Nash coefficients have been significantly improved using Kalman
NASA Astrophysics Data System (ADS)
Gurung, H.; Banerjee, A.
2016-02-01
This report presents the development of an extended Kalman filter (EKF) to harness the self-sensing capability of a shape memory alloy (SMA) wire, actuating a linear spring. The stress and temperature of the SMA wire, constituting the state of the system, are estimated using the EKF, from the measured change in electrical resistance (ER) of the SMA. The estimated stress is used to compute the change in length of the spring, eliminating the need for a displacement sensor. The system model used in the EKF comprises the heat balance equation and the constitutive relation of the SMA wire coupled with the force-displacement behavior of a spring. Both explicit and implicit approaches are adopted to evaluate the system model at each time-update step of the EKF. Next, in the measurement-update step, estimated states are updated based on the measured electrical resistance. It has been observed that for the same time step, the implicit approach consumes less computational time than the explicit method. To verify the implementation, EKF estimated states of the system are compared with those of an established model for different inputs to the SMA wire. An experimental setup is developed to measure the actual spring displacement and ER of the SMA, for any time-varying voltage applied to it. The process noise covariance is decided using a heuristic approach, whereas the measurement noise covariance is obtained experimentally. Finally, the EKF is used to estimate the spring displacement for a given input and the corresponding experimentally obtained ER of the SMA. The qualitative agreement between the EKF estimated displacement with that obtained experimentally reveals the true potential of this approach to harness the self-sensing capability of the SMA.
Bukhari, W; Hong, S-M
2016-03-07
The prediction as well as the gating of respiratory motion have received much attention over the last two decades for reducing the targeting error of the radiation treatment beam due to respiratory motion. In this article, we present a real-time algorithm for predicting respiratory motion in 3D space and realizing a gating function without pre-specifying a particular phase of the patient's breathing cycle. The algorithm, named EKF-GPRN(+) , first employs an extended Kalman filter (EKF) independently along each coordinate to predict the respiratory motion and then uses a Gaussian process regression network (GPRN) to correct the prediction error of the EKF in 3D space. The GPRN is a nonparametric Bayesian algorithm for modeling input-dependent correlations between the output variables in multi-output regression. Inference in GPRN is intractable and we employ variational inference with mean field approximation to compute an approximate predictive mean and predictive covariance matrix. The approximate predictive mean is used to correct the prediction error of the EKF. The trace of the approximate predictive covariance matrix is utilized to capture the uncertainty in EKF-GPRN(+) prediction error and systematically identify breathing points with a higher probability of large prediction error in advance. This identification enables us to pause the treatment beam over such instances. EKF-GPRN(+) implements a gating function by using simple calculations based on the trace of the predictive covariance matrix. Extensive numerical experiments are performed based on a large database of 304 respiratory motion traces to evaluate EKF-GPRN(+) . The experimental results show that the EKF-GPRN(+) algorithm reduces the patient-wise prediction error to 38%, 40% and 40% in root-mean-square, compared to no prediction, at lookahead lengths of 192 ms, 384 ms and 576 ms, respectively. The EKF-GPRN(+) algorithm can further reduce the prediction error by employing the gating
Kalman filtering approach to blind equalization
NASA Astrophysics Data System (ADS)
Kutlu, Mehmet
1993-12-01
Digital communication systems suffer from the channel distortion problem which introduces errors due to intersymbol interference. The solution to this problem is provided by equalizers which use a training sequence to adapt to the channel. However in many cases in which a training sequence is unfeasible, the channel must be adapted blindly. Most of the blind equalization algorithms known so far have problems of convergence to local minima. Our intention is to offer an alternative approach by using extended Kalman filtering and hidden Markov models. They seem to yield more efficient algorithms which take the statistics of the transmitted sequence into consideration. The theoretical development of these new algorithms is discussed in this thesis. Also these algorithms have been simulated under different conditions. The results of simulations and comparisons with existing systems are provided. The models for simulations are presented as MATLAB codes.
Attitude Error Representations for Kalman Filtering
NASA Technical Reports Server (NTRS)
Markley, F. Landis; Bauer, Frank H. (Technical Monitor)
2002-01-01
The quaternion has the lowest dimensionality possible for a globally nonsingular attitude representation. The quaternion must obey a unit norm constraint, though, which has led to the development of an extended Kalman filter using a quaternion for the global attitude estimate and a three-component representation for attitude errors. We consider various attitude error representations for this Multiplicative Extended Kalman Filter and its second-order extension.
Attitude Representations for Kalman Filtering
NASA Technical Reports Server (NTRS)
Markley, F. Landis; Bauer, Frank H. (Technical Monitor)
2001-01-01
The four-component quaternion has the lowest dimensionality possible for a globally nonsingular attitude representation, it represents the attitude matrix as a homogeneous quadratic function, and its dynamic propagation equation is bilinear in the quaternion and the angular velocity. The quaternion is required to obey a unit norm constraint, though, so Kalman filters often employ a quaternion for the global attitude estimate and a three-component representation for small errors about the estimate. We consider these mixed attitude representations for both a first-order Extended Kalman filter and a second-order filter, as well for quaternion-norm-preserving attitude propagation.
Reduced-order Kalman filtering with incomplete observability
NASA Technical Reports Server (NTRS)
Yonezawa, K.
1980-01-01
Kalman filtering is considered with reference to linear stochastic dynamic systems without complete observability. It is shown that the canonical decomposition theorem can be extended to the stochastic case and the matrix Riccati equation of the Kalman filter is order-reducible if some states are not observable. The inclusion of unobservable states in Kalman filtering makes the unobservable states 'asymptotically' observable in the filter if these unobservable states are dynamically connected to observable states and asymptotically stable. The reduced-order Kalman filter saves computation time when compared to the conventional Kalman filter.
Flexible Generation of Kalman Filter Code
NASA Technical Reports Server (NTRS)
Richardson, Julian; Wilson, Edward
2006-01-01
Domain-specific program synthesis can automatically generate high quality code in complex domains from succinct specifications, but the range of programs which can be generated by a given synthesis system is typically narrow. Obtaining code which falls outside this narrow scope necessitates either 1) extension of the code generator, which is usually very expensive, or 2) manual modification of the generated code, which is often difficult and which must be redone whenever changes are made to the program specification. In this paper, we describe adaptations and extensions of the AUTOFILTER Kalman filter synthesis system which greatly extend the range of programs which can be generated. Users augment the input specification with a specification of code fragments and how those fragments should interleave with or replace parts of the synthesized filter. This allows users to generate a much wider range of programs without their needing to modify the synthesis system or edit generated code. We demonstrate the usefulness of the approach by applying it to the synthesis of a complex state estimator which combines code from several Kalman filters with user-specified code. The work described in this paper allows the complex design decisions necessary for real-world applications to be reflected in the synthesized code. When executed on simulated input data, the generated state estimator was found to produce comparable estimates to those produced by a handcoded estimator
Elsayed, Alaaeldin M.; Hunter, Lisa L.; Keefe, Douglas H.; Feeney, M. Patrick; Brown, David K.; Meinzen-Derr, Jareen K.; Baroch, Kelly; Sullivan-Mahoney, Maureen; Francis, Kara; Schaid, Leigh G.
2015-01-01
Objective To study normative thresholds and latencies for click and tone-burst auditory brainstem response (TB-ABR) for air and bone conduction in normal infants and those discharged from neonatal intensive care units (NICU), who passed newborn hearing screening and follow-up DPOAE. An evoked potential system (Vivosonic Integrity™) that incorporates Bluetooth electrical isolation and Kalman-weighted adaptive processing to improve signal to noise ratios was employed for this study. Results were compared with other published data. Research Design One hundred forty-five infants who passed two-stage hearing screening with transient-evoked otoacoustic emission (OAE) or automated ABR were assessed with clicks at 70 dB nHL and threshold TB-ABR. Tone-bursts at frequencies between 500 to 4000 Hz were employed for air and bone conduction ABR testing using a specified staircase threshold search to establish threshold levels and Wave V peak latencies. Results Median air conduction hearing thresholds using TB-ABR ranged from 0-20 dB nHL, depending on stimulus frequency. Median bone conduction thresholds were 10 dB nHL across all frequencies, and median air-bone gaps were 0 dB across all frequencies. There was no significant threshold difference between left and right ears and no significant relationship between thresholds and hearing loss risk factors, ethnicity or gender. Older age was related to decreased latency for air conduction. Compared to previous studies, mean air conduction thresholds were found at slightly lower (better) levels, while bone conduction levels were better at 2000 Hz and higher at 500 Hz. Latency values were longer at 500 Hz than previous studies using other instrumentation. Sleep state did not affect air or bone conduction thresholds. Conclusions This study demonstrated slightly better Wave V thresholds for air conduction than previous infant studies. The differences found in the current study, while statistically significant, were within the test
Atypicalities in perceptual adaptation in autism do not extend to perceptual causality.
Karaminis, Themelis; Turi, Marco; Neil, Louise; Badcock, Nicholas A; Burr, David; Pellicano, Elizabeth
2015-01-01
A recent study showed that adaptation to causal events (collisions) in adults caused subsequent events to be less likely perceived as causal. In this study, we examined if a similar negative adaptation effect for perceptual causality occurs in children, both typically developing and with autism. Previous studies have reported diminished adaptation for face identity, facial configuration and gaze direction in children with autism. To test whether diminished adaptive coding extends beyond high-level social stimuli (such as faces) and could be a general property of autistic perception, we developed a child-friendly paradigm for adaptation of perceptual causality. We compared the performance of 22 children with autism with 22 typically developing children, individually matched on age and ability (IQ scores). We found significant and equally robust adaptation aftereffects for perceptual causality in both groups. There were also no differences between the two groups in their attention, as revealed by reaction times and accuracy in a change-detection task. These findings suggest that adaptation to perceptual causality in autism is largely similar to typical development and, further, that diminished adaptive coding might not be a general characteristic of autism at low levels of the perceptual hierarchy, constraining existing theories of adaptation in autism.
Post-mission adaptation after extended isolation: is there a fifth quarter?
NASA Astrophysics Data System (ADS)
Solignac, Amaury; Angerer, Oliver; Rosnet, Elisabeth; Bachelard, Claude
The adaptation processes of crews in isolated and confined environments have long been studied. However, few studies have surveyed the adaptation of personnel upon return, when the mission has come to an end. This post-mission perspective has implications in psychological screening (selection), and may help to improve the performance and well-being of crews during - and after - extended missions in unusual environments (polar missions, space simulators and stations, etc.) Preliminary results of a retrospective study among French and German polar winterers (150 subjects) will be presented.
Dependence of Adaptive Cross-correlation Algorithm Performance on the Extended Scene Image Quality
NASA Technical Reports Server (NTRS)
Sidick, Erkin
2008-01-01
Recently, we reported an adaptive cross-correlation (ACC) algorithm to estimate with high accuracy the shift as large as several pixels between two extended-scene sub-images captured by a Shack-Hartmann wavefront sensor. It determines the positions of all extended-scene image cells relative to a reference cell in the same frame using an FFT-based iterative image-shifting algorithm. It works with both point-source spot images as well as extended scene images. We have demonstrated previously based on some measured images that the ACC algorithm can determine image shifts with as high an accuracy as 0.01 pixel for shifts as large 3 pixels, and yield similar results for both point source spot images and extended scene images. The shift estimate accuracy of the ACC algorithm depends on illumination level, background, and scene content in addition to the amount of the shift between two image cells. In this paper we investigate how the performance of the ACC algorithm depends on the quality and the frequency content of extended scene images captured by a Shack-Hatmann camera. We also compare the performance of the ACC algorithm with those of several other approaches, and introduce a failsafe criterion for the ACC algorithm-based extended scene Shack-Hatmann sensors.
Multirate and event-driven Kalman filters for helicopter flight
NASA Technical Reports Server (NTRS)
Sridhar, Banavar; Smith, Phillip; Suorsa, Raymond E.; Hussien, Bassam
1993-01-01
A vision-based obstacle detection system that provides information about objects as a function of azimuth and elevation is discussed. The range map is computed using a sequence of images from a passive sensor, and an extended Kalman filter is used to estimate range to obstacles. The magnitude of the optical flow that provides measurements for each Kalman filter varies significantly over the image depending on the helicopter motion and object location. In a standard Kalman filter, the measurement update takes place at fixed intervals. It may be necessary to use a different measurement update rate in different parts of the image in order to maintain the same signal to noise ratio in the optical flow calculations. A range estimation scheme that accepts the measurement only under certain conditions is presented. The estimation results from the standard Kalman filter are compared with results from a multirate Kalman filter and an event-driven Kalman filter for a sequence of helicopter flight images.
Development of Real-Time Error Ellipses as an Indicator of Kalman Filter Performance.
1984-03-01
S q often than 3 to 5 seconds. However, before the HP-86 can e considered feasible for real-time Kalman filtr procssinz, more investigaz ion i: needi...Subtitle) S. TYPE OF REPORT & PERIOD COVERED Development of Real-Time Error Master’s Thesis; Ellipses as an Indicator of Kalman March 1984 Filter...SUPP.LEETARY NOTES 19. KEY WORDS (Cmntine on reveo ole, It ndeeaey md Identil by block number) Error Ellipsoids; Kalman Filter; Extended Kalman Filter
Effects of extended lay-off periods on performance and operator trust under adaptable automation.
Chavaillaz, Alain; Wastell, David; Sauer, Jürgen
2016-03-01
Little is known about the long-term effects of system reliability when operators do not use a system during an extended lay-off period. To examine threats to skill maintenance, 28 participants operated twice a simulation of a complex process control system for 2.5 h, with an 8-month retention interval between sessions. Operators were provided with an adaptable support system, which operated at one of the following reliability levels: 60%, 80% or 100%. Results showed that performance, workload, and trust remained stable at the second testing session, but operators lost self-confidence in their system management abilities. Finally, the effects of system reliability observed at the first testing session were largely found again at the second session. The findings overall suggest that adaptable automation may be a promising means to support operators in maintaining their performance at the second testing session.
Toward a systems-oriented approach to the role of the extended amygdala in adaptive responding.
Waraczynski, Meg
2016-09-01
Research into the structure and function of the basal forebrain macrostructure called the extended amygdala (EA) has recently seen considerable growth. This paper reviews that work, with the objectives of identifying underlying themes and developing a common goal towards which investigators of EA function might work. The paper begins with a brief review of the structure and the ontological and phylogenetic origins of the EA. It continues with a review of research into the role of the EA in both aversive and appetitive states, noting that these two seemingly disparate avenues of research converge on the concept of reinforcement - either negative or positive - of adaptive responding. These reviews lead to a proposal as to where the EA may fit in the organization of the basal forebrain, and an invitation to investigators to place their findings in a unifying conceptual framework of the EA as a collection of neural ensembles that mediate adaptive responding.
Extended depth of focus adaptive optics spectral domain optical coherence tomography
Sasaki, Kazuhiro; Kurokawa, Kazuhiro; Makita, Shuichi; Yasuno, Yoshiaki
2012-01-01
We present an adaptive optics spectral domain optical coherence tomography (AO-SDOCT) with a long focal range by active phase modulation of the pupil. A long focal range is achieved by introducing AO-controlled third-order spherical aberration (SA). The property of SA and its effects on focal range are investigated in detail using the Huygens-Fresnel principle, beam profile measurement and OCT imaging of a phantom. The results indicate that the focal range is extended by applying SA, and the direction of extension can be controlled by the sign of applied SA. Finally, we demonstrated in vivo human retinal imaging by altering the applied SA. PMID:23082278
Adaptive variable selection for extended Nijboer-Zernike aberration retrieval via lasso
NASA Astrophysics Data System (ADS)
Wang, Bin; Diao, Huai-An; Guo, Jianhua; Liu, Xiyang; Wu, Yuanhao
2017-02-01
In this paper, we propose extended Nijboer-Zernike (ENZ) method for aberration retrieval by incorporating lasso variable selection method which can improve the accuracy of aberration retrieval. The proposed model is computed by the state-of-art algorithm of the Bregman iterative algorithm (Bregman, 1967 [1]; Cai et al., 2008 [2]; Yin et al., 2008 [3]) for L1 minimization problem with adaptive regularized parameter choice based on the strategy (Ito et al., 2011 [4]). Numerical simulations for real world and simulated phase data validate the effectiveness of the proposed ENZ AR via lasso.
Lee, Hyung-Min; Ghovanloo, Maysam
2014-01-01
We present an adaptive reconfigurable active voltage doubler (VD)/rectifier (REC) (VD/REC) in standard CMOS, which can adaptively change its topology to either a VD or a REC by sensing the output voltage, leading to more robust inductive power transmission over an extended range. Both active VD and REC modes provide much lower dropout voltage and far better power conversion efficiency (PCE) compared to their passive counterparts by adopting offset-controlled high-speed comparators that drive the rectifying switches at proper times in the high-frequency band. We have fabricated the active VD/REC in a 0.5-µm 3-metal 2-poly CMOS process, occupying 0.585 mm2 of chip area. In an exemplar setup, VD/REC extended the power transmission range by 33% (from 6 to 8 cm) in relative coil distance and 41.5% (from 53° to 75°) in relative coil orientation compared to using the REC alone. While providing 3.1-V dc output across a 500-Ω load from 2.15- (VD) and 3.7-V (REC) peak ac inputs at 13.56 MHz, VD/REC achieved measured PCEs of 70% and 77%, respectively. PMID:24633369
Lesage, Adrien; Lelièvre, Tony; Stoltz, Gabriel; Hénin, Jérôme
2016-12-27
We report a theoretical description and numerical tests of the extended-system adaptive biasing force method (eABF), together with an unbiased estimator of the free energy surface from eABF dynamics. Whereas the original ABF approach uses its running estimate of the free energy gradient as the adaptive biasing force, eABF is built on the idea that the exact free energy gradient is not necessary for efficient exploration, and that it is still possible to recover the exact free energy separately with an appropriate estimator. eABF does not directly bias the collective coordinates of interest, but rather fictitious variables that are harmonically coupled to them; therefore is does not require second derivative estimates, making it easily applicable to a wider range of problems than ABF. Furthermore, the extended variables present a smoother, coarse-grain-like sampling problem on a mollified free energy surface, leading to faster exploration and convergence. We also introduce CZAR, a simple, unbiased free energy estimator from eABF trajectories. eABF/CZAR converges to the physical free energy surface faster than standard ABF for a wide range of parameters.
Adaptive subspace detection of extended target in white Gaussian noise using sinc basis
NASA Astrophysics Data System (ADS)
Zhang, Xiao-Wei; Li, Ming; Qu, Jian-She; Yang, Hui
2016-01-01
For the high resolution radar (HRR), the problem of detecting the extended target is considered in this paper. Based on a single observation, a new two-step detection based on sparse representation (TSDSR) method is proposed to detect the extended target in the presence of Gaussian noise with unknown covariance. In the new method, the Sinc dictionary is introduced to sparsely represent the high resolution range profile (HRRP). Meanwhile, adaptive subspace pursuit (ASP) is presented to recover the HRRP embedded in the Gaussian noise and estimate the noise covariance matrix. Based on the Sinc dictionary and the estimated noise covariance matrix, one step subspace detector (OSSD) for the first-order Gaussian (FOG) model without secondary data is adopted to realise the extended target detection. Finally, the proposed TSDSR method is applied to raw HRR data. Experimental results demonstrate that HRRPs of different targets can be sparsely represented very well with the Sinc dictionary. Moreover, the new method can estimate the noise power with tiny errors and have a good detection performance.
Kawano, Yoshihiro; Higgins, Christopher; Yamamoto, Yasuhito; Nyhus, Julie; Bernard, Amy; Dong, Hong-Wei; Karten, Harvey J; Schilling, Tobias
2013-01-01
We present a new method for whole slide darkfield imaging. Whole Slide Imaging (WSI), also sometimes called virtual slide or virtual microscopy technology, produces images that simultaneously provide high resolution and a wide field of observation that can encompass the entire section, extending far beyond any single field of view. For example, a brain slice can be imaged so that both overall morphology and individual neuronal detail can be seen. We extended the capabilities of traditional whole slide systems and developed a prototype system for darkfield internal reflection illumination (DIRI). Our darkfield system uses an ultra-thin light-emitting diode (LED) light source to illuminate slide specimens from the edge of the slide. We used a new type of side illumination, a variation on the internal reflection method, to illuminate the specimen and create a darkfield image. This system has four main advantages over traditional darkfield: (1) no oil condenser is required for high resolution imaging (2) there is less scatter from dust and dirt on the slide specimen (3) there is less halo, providing a more natural darkfield contrast image, and (4) the motorized system produces darkfield, brightfield and fluorescence images. The WSI method sometimes allows us to image using fewer stains. For instance, diaminobenzidine (DAB) and fluorescent staining are helpful tools for observing protein localization and volume in tissues. However, these methods usually require counter-staining in order to visualize tissue structure, limiting the accuracy of localization of labeled cells within the complex multiple regions of typical neurohistological preparations. Darkfield imaging works on the basis of light scattering from refractive index mismatches in the sample. It is a label-free method of producing contrast in a sample. We propose that adapting darkfield imaging to WSI is very useful, particularly when researchers require additional structural information without the use of
Optical Kalman filtering for missile guidance
NASA Technical Reports Server (NTRS)
Casasent, D.; Neuman, C. P.; Lycas, J.
1984-01-01
Optical systolic array processors constitute a powerful and general-purpose set of optical architectures with high computational rates. In this paper, Kalman filtering, a novel application for these architectures, is investigated. All required operations are detailed; their realization by optical and special-purpose analog electronics are specified; and the processing time of the system is quantified. The specific Kalman filter application chosen is for an air-to-air missile guidance controller. The architecture realized in this paper meets the design goal of a fully adaptive Kalman filter which processes a measurement every 1 msec. The vital issue of flow and pipelining of data and operations in a systolic array processor is addressed. The approach is sufficiently general and can be realized on an optical or digital systolic array processor.
Zhan, Yang; Guo, Shuixia; Kendrick, Keith M; Feng, Jianfeng
2009-04-01
Novel approaches to effectively reduce noise in data recorded from multi-trial physiology experiments have been investigated using two-dimensional filtering methods, adaptive Wiener filtering and reduced update Kalman filtering. Test data based on signal and noise model consisting of different conditions of signal components mixed with noise have been considered with filtering effects evaluated using analysis of frequency coherence and of time-dependent coherence. Various situations that may affect the filtering results have been explored and reveal that Wiener and Kalman filtering can considerably improve the coherence values between two channels of multi-trial data and suppress uncorrelated components. We have extended our approach to experimental data: multi-electrode array (MEA) local field potential (LFPs) recordings from the inferotemporal cortex of sheep and LFP vs. electromyogram (LFP-EMG) recording data during resting tremor in Parkinson's disease patients. Finally general procedures for implementation of these filtering techniques are described.
Extended observer based on adaptive second order sliding mode control for a fixed wing UAV.
Castañeda, Herman; Salas-Peña, Oscar S; León-Morales, Jesús de
2017-01-01
This paper addresses the design of attitude and airspeed controllers for a fixed wing unmanned aerial vehicle. An adaptive second order sliding mode control is proposed for improving performance under different operating conditions and is robust in presence of external disturbances. Moreover, this control does not require the knowledge of disturbance bounds and avoids overestimation of the control gains. Furthermore, in order to implement this controller, an extended observer is designed to estimate unmeasurable states as well as external disturbances. Additionally, sufficient conditions are given to guarantee the closed-loop stability of the observer based control. Finally, using a full 6 degree of freedom model, simulation results are obtained where the performance of the proposed method is compared against active disturbance rejection based on sliding mode control.
A class of quaternion Kalman filters.
Jahanchahi, Cyrus; Mandic, Danilo P
2014-03-01
The existing Kalman filters for quaternion-valued signals do not operate fully in the quaternion domain, and are combined with the real Kalman filter to enable the tracking in 3-D spaces. Using the recently introduced HR-calculus, we develop the fully quaternion-valued Kalman filter (QKF) and quaternion-extended Kalman filter (QEKF), allowing for the tracking of 3-D and 4-D signals directly in the quaternion domain. To consider the second-order noncircularity of signals, we employ the recently developed augmented quaternion statistics to derive the widely linear QKF (WL-QKF) and widely linear QEKF (WL-QEKF). To reduce computational requirements of the widely linear algorithms, their efficient implementation are proposed and it is shown that the quaternion widely linear model can be simplified when processing 3-D data, further reducing the computational requirements. Simulations using both synthetic and real-world circular and noncircular signals illustrate the advantages offered by widely linear over strictly linear quaternion Kalman filters.
NASA Astrophysics Data System (ADS)
Stubberud, Allen R.
2017-01-01
When considering problems of linear sequential estimation, two versions of the Kalman filter, the continuous-time version and the discrete-time version, are often used. (A hybrid filter also exists.) In many applications in which the Kalman filter is used, the system to which the filter is applied is a linear continuous-time system, but the Kalman filter is implemented on a digital computer, a discrete-time device. The two general approaches for developing a discrete-time filter for implementation on a digital computer are: (1) approximate the continuous-time system by a discrete-time system (called discretization of the continuous-time system) and develop a filter for the discrete-time approximation; and (2) develop a continuous-time filter for the system and then discretize the continuous-time filter. Generally, the two discrete-time filters will be different, that is, it can be said that discretization and filter generation are not, in general, commutative operations. As a result, any relationship between the discrete-time and continuous-time versions of the filter for the same continuous-time system is often obfuscated. This is particularly true when an attempt is made to generate the continuous-time version of the Kalman filter through a simple limiting process (the sample period going to zero) applied to the discrete-time version. The correct result is, generally, not obtained. In a 1961 research report, Kalman showed that the continuous-time Kalman filter can be obtained from the discrete-time Kalman filter by taking limits as the sample period goes to zero if the white noise process for the continuous-time version is appropriately defined. Using this basic concept, a discrete-time Kalman filter can be developed for a continuous-time system as follows: (1) discretize the continuous-time system using Kalman's technique; and (2) develop a discrete-time Kalman filter for that discrete-time system. Kalman's results show that the discrete-time filter generated in
NASA Astrophysics Data System (ADS)
Singh, Ravinder; Price, Stanton R.; Anderson, Derek T.
2015-05-01
A big challenge with forward looking (FL), versus downward looking, sensors mounted on a ground vehicle for explosive hazard detection (EHD) is they "see everything", on and off road. Even if a technology such as road detection is used, we still have to find and subsequently discriminate targets versus clutter on the road and often road side. When designing an automatic detection system for FL-EHD, we typically make use of a prescreener to identify regions of interest (ROI) instead of searching for targets in an inefficient brute force fashion by extracting complicated features and running expensive classifiers at every possible translation, rotation and scale. In this article, we explore the role of genetic algorithms (GAs), specifically with respect to a new adaptive mutation operator, for learning the parameters of a FL-EHD prescreener in FL infrared (FLIR) imagery. The proposed extended adaptive mutation (eAM) algorithm is driven by fitness similarities in the chromosome population. Currently, our prescreener consists of many free parameters that are empirically chosen by a researcher. The parameters are learned herein using the proposed optimization technique and the performance of the system is measured using receiver operating characteristic (ROC) curves on data obtained from a U.S. Army test site that includes a variety of target types buried at varying depths and from different times of day. The proposed technique is also applied to numerous synthetic fitness landscapes to further assess the effectiveness of the eAM algorithm. Results show that the new adaptive mutation technique converges faster to a better solution than a GA with fixed mutation.
VLBI real-time analysis by Kalman Filtering
NASA Astrophysics Data System (ADS)
Karbon, Maria; Soja, Benedikt; Nilson, Tobias; Heinkelmann, Robert; Liu, Li; Lu, Ciuxian; Xu, Minghui; Raposo-Pulido, Virginia; Mora-Diaz, Julian; Schuh, Harald
2014-05-01
Geodetic Very Long Baseline Interferometry (VLBI) is one of the primary space geodetic techniques. It provides the full set of Earth Orientation Parameter (EOP) and is unique for observing long term Universal Time (UT1) and precession/nutation. Currently the VLBI products are delivered with a delay of about two weeks from the moment of the observation. However, the need for near-real time estimates of the parameters is increasing, e.g. for satellite based navigation and positioning or for enabling precise tracking of interplanetary spacecraft. The goal is thus to reduce the time span between observation and the final result to less than one day. This can be archived by replacing the classical least squares method with an adaptive Kalman filter. We have developed a Kalman filter for VLBI data analysis. This method has the advantage that it is simultaneously possible to estimate stationary parameters, e.g. station positions, and to model the highly variable stochastic behavior of non-stationary parameters like clocks or atmospheric parameters. The filter is able to perform without any human interaction, making it a completely autonomous tool. In this work we describe the filter and discuss its application for EOP determination and prediction. We discuss the implementation of the stochastic models to statistically account for unpredictable changes in EOP. Furthermore, additional data like results from other techniques can be included to improve the performance. For example, atmospheric angular momentum calculated from numerical weather models can be introduced to supplement the short-term prediction of UT1 and polar motion. This Kalman filter will be extended and embedded in the newly developed Vienna VLBI Software (VieVS) as a completely autonomous tool enabling the VLBI analysis in near real-time and providing all the parameters of interest with the highest possible accuracy.
Use of an Extended Kalman Filter
1990-09-01
navigation radars available anywhere in the market. Those belonging to the FURUNO company are the most popular . This radar will be used on the present...Smugglers", Popular Communications, June 1990 4. Kourkoulis, D., Bearings Only Target Tracking-Maneuvering Target, Master’s Thesis, Naval Postgraduate...CA. 93943-5002 3. Jefatura de Educacion 1 Comandancia General de la Armada Av. Wollmer, San Bernardino CP.1011 Caracas, Venezuela. 4. Escuela Superior
Chen, Ying-ping; Chen, Chao-Hong
2010-01-01
An adaptive discretization method, called split-on-demand (SoD), enables estimation of distribution algorithms (EDAs) for discrete variables to solve continuous optimization problems. SoD randomly splits a continuous interval if the number of search points within the interval exceeds a threshold, which is decreased at every iteration. After the split operation, the nonempty intervals are assigned integer codes, and the search points are discretized accordingly. As an example of using SoD with EDAs, the integration of SoD and the extended compact genetic algorithm (ECGA) is presented and numerically examined. In this integration, we adopt a local search mechanism as an optional component of our back end optimization engine. As a result, the proposed framework can be considered as a memetic algorithm, and SoD can potentially be applied to other memetic algorithms. The numerical experiments consist of two parts: (1) a set of benchmark functions on which ECGA with SoD and ECGA with two well-known discretization methods: the fixed-height histogram (FHH) and the fixed-width histogram (FWH) are compared; (2) a real-world application, the economic dispatch problem, on which ECGA with SoD is compared to other methods. The experimental results indicate that SoD is a better discretization method to work with ECGA. Moreover, ECGA with SoD works quite well on the economic dispatch problem and delivers solutions better than the best known results obtained by other methods in existence.
Model-based wavefront sensorless adaptive optics system for large aberrations and extended objects.
Yang, Huizhen; Soloviev, Oleg; Verhaegen, Michel
2015-09-21
A model-based wavefront sensorless (WFSless) adaptive optics (AO) system with a 61-element deformable mirror is simulated to correct the imaging of a turbulence-degraded extended object. A fast closed-loop control algorithm, which is based on the linear relation between the mean square of the aberration gradients and the second moment of the image intensity distribution, is used to generate the control signals for the actuators of the deformable mirror (DM). The restoration capability and the convergence rate of the AO system are investigated with different turbulence strength wave-front aberrations. Simulation results show the model-based WFSless AO system can restore those images degraded by different turbulence strengths successfully and obtain the correction very close to the achievable capability of the given DM. Compared with the ideal correction of 61-element DM, the averaged relative error of RMS value is 6%. The convergence rate of AO system is independent of the turbulence strength and only depends on the number of actuators of DM.
NASA Astrophysics Data System (ADS)
Conley, Rebecca; Delaney, Tristan J.; Jiao, Xiangmin
2016-11-01
The finite element methods (FEM) are important techniques in engineering for solving partial differential equations, but they depend heavily on element shape quality for stability and good performance. In this paper, we introduce the Adaptive Extended Stencil Finite Element Method (AES-FEM) as a means for overcoming this dependence on element shape quality. Our method replaces the traditional basis functions with a set of generalized Lagrange polynomial (GLP) basis functions, which we construct using local weighted least-squares approximations. The method preserves the theoretical framework of FEM, and allows imposing essential boundary conditions and integrating the stiffness matrix in the same way as the classical FEM. In addition, AES-FEM can use higher-degree polynomial basis functions than the classical FEM, while virtually preserving the sparsity pattern of the stiffness matrix. We describe the formulation and implementation of AES-FEM, and analyze its consistency and stability. We present numerical experiments in both 2D and 3D for the Poison equation and a time-independent convection-diffusion equation. The numerical results demonstrate that AES-FEM is more accurate than linear FEM, is also more efficient than linear FEM in terms of error versus runtime, and enables much better stability and faster convergence of iterative solvers than linear FEM over poor-quality meshes
Multiharmonic tracking using sigma-point Kalman filter.
Kim, Sunghan; Paul, Anindya S; Wan, Eric A; McNames, James
2008-01-01
Several groups have proposed the state-space approach to track time-varying frequencies ofmulti-harmonic quasi-periodic signals contaminated with white Gaussian noise. We compared the extended Kalman filter (EKF) and sigma-point Kalman filter (SPKF) algorithms on this problem. On average, the SPKF outperformed the EKF and more accurately tracked the instantaneous frequency over a wide range of signal-to-noise (SNR) ratios.
NASA Technical Reports Server (NTRS)
Brown, R. G.
1984-01-01
The formulation of appropriate state-space models for Kalman filtering applications is studied. The so-called model is completely specified by four matrix parameters and the initial conditions of the recursive equations. Once these are determined, the die is cast, and the way in which the measurements are weighted is determined foreverafter. Thus, finding a model that fits the physical situation at hand is all important. Also, it is often the most difficult aspect of designing a Kalman filter. Formulation of discrete state models from the spectral density and ARMA random process descriptions is discussed. Finally, it is pointed out that many common processes encountered in applied work (such as band-limited white noise) simply do not lend themselves very well to Kalman filter modeling.
NASA Technical Reports Server (NTRS)
Skliar, M.; Ramirez, W. F.
1997-01-01
For an implicitly defined discrete system, a new algorithm for Kalman filtering is developed and an efficient numerical implementation scheme is proposed. Unlike the traditional explicit approach, the implicit filter can be readily applied to ill-conditioned systems and allows for generalization to descriptor systems. The implementation of the implicit filter depends on the solution of the congruence matrix equation (A1)(Px)(AT1) = Py. We develop a general iterative method for the solution of this equation, and prove necessary and sufficient conditions for convergence. It is shown that when the system matrices of an implicit system are sparse, the implicit Kalman filter requires significantly less computer time and storage to implement as compared to the traditional explicit Kalman filter. Simulation results are presented to illustrate and substantiate the theoretical developments.
NASA Technical Reports Server (NTRS)
Sonnabend, David
1995-01-01
In a paper here last year, an idea was put forward that much greater performance could be obtained from an observer, relative to a Kalman filter if more general performance indices were adopted, and the full power spectra of all the noises were employed. The considerable progress since then is reported here. Included are an extension of the theory to regulators, direct calculation of the theory's fundamental quantities - the noise effect integrals - for several theoretical spectra, and direct derivations of the Riccati equations of LQG (Linear-Quadratic-Gaussian) and Kalman theory yielding new insights.
A Two-Stage Kalman Filter Approach for Robust and Real-Time Power System State Estimation
Zhang, Jinghe; Welch, Greg; Bishop, Gary; Huang, Zhenyu
2014-04-01
As electricity demand continues to grow and renewable energy increases its penetration in the power grid, realtime state estimation becomes essential for system monitoring and control. Recent development in phasor technology makes it possible with high-speed time-synchronized data provided by Phasor Measurement Units (PMU). In this paper we present a two-stage Kalman filter approach to estimate the static state of voltage magnitudes and phase angles, as well as the dynamic state of generator rotor angles and speeds. Kalman filters achieve optimal performance only when the system noise characteristics have known statistical properties (zero-mean, Gaussian, and spectrally white). However in practice the process and measurement noise models are usually difficult to obtain. Thus we have developed the Adaptive Kalman Filter with Inflatable Noise Variances (AKF with InNoVa), an algorithm that can efficiently identify and reduce the impact of incorrect system modeling and/or erroneous measurements. In stage one, we estimate the static state from raw PMU measurements using the AKF with InNoVa; then in stage two, the estimated static state is fed into an extended Kalman filter to estimate the dynamic state. Simulations demonstrate its robustness to sudden changes of system dynamics and erroneous measurements.
Unscented Kalman filters for polarization state tracking and phase noise mitigation.
Jignesh, Jokhakar; Corcoran, Bill; Zhu, Chen; Lowery, Arthur
2016-09-19
Simultaneous polarization and phase noise tracking and compensation is proposed based on an unscented Kalman filter (UKF). We experimentally demonstrate the tracking under noise-loading and after 800-km single-mode fiber transmission with 20-Gbaud QPSK and 16-QAM signals. These experiments show that the proposed UKF outperforms both conventional blind tracing algorithms and a previously proposed extended Kalman filter, at the cost of higher complexity. Additionally, we propose and test modified Kalman filter algorithms to reduce computational complexity.
Initial flight results of the TRMM Kalman filter
NASA Technical Reports Server (NTRS)
Andrews, Stephen F.; Morgenstern, Wendy M.
1998-01-01
The Tropical Rainfall Measuring Mission (TRMM) spacecraft is a nadir pointing spacecraft that nominally controls attitude based on the Earth Sensor Assembly (ESA) output. After a potential single point failure in the ESA was identified, the contingency attitude determination method chosen to backup the ESA-based system was a sixth-order extended Kalman filter that uses magnetometer and digital sun sensor measurements. A brief description of the TRMM Kalman filter will be given, including some implementation issues and algorithm heritage. Operational aspects of the Kalman filter and some failure detection and correction will be described. The Kalman filter was tested in a sun pointing attitude and in a nadir pointing attitude during the in-orbit checkout period, and results from those tests will be presented. This paper will describe some lessons learned from the experience of the TRMM team.
Initial Flight Results of the TRMM Kalman Filter
NASA Technical Reports Server (NTRS)
Andrews, Stephen F.; Morgenstern, Wendy M.
1998-01-01
The Tropical Rainfall Measuring Mission (TRMM) spacecraft is a nadir pointing spacecraft that nominally controls attitude based on the Earth Sensor Assembly (ESA) output. After a potential single point failure in the ESA was identified, the contingency attitude determination method chosen to backup the ESA-based system was a sixth-order extended Kalman filter that uses magnetometer and digital sun sensor measurements. A brief description of the TRMM Kalman filter will be given, including some implementation issues and algorithm heritage. Operational aspects of the Kalman filter and some failure detection and correction will be described. The Kalman filter was tested in a sun pointing attitude and in a nadir pointing attitude during the in-orbit checkout period, and results from those tests will be presented. This paper will describe some lessons learned from the experience of the TRMM team.
From dinosaurs to modern bird diversity: extending the time scale of adaptive radiation.
Moen, Daniel; Morlon, Hélène
2014-05-01
What explains why some groups of organisms, like birds, are so species rich? And what explains their extraordinary ecological diversity, ranging from large, flightless birds to small migratory species that fly thousand of kilometers every year? These and similar questions have spurred great interest in adaptive radiation, the diversification of ecological traits in a rapidly speciating group of organisms. Although the initial formulation of modern concepts of adaptive radiation arose from consideration of the fossil record, rigorous attempts to identify adaptive radiation in the fossil record are still uncommon. Moreover, most studies of adaptive radiation concern groups that are less than 50 million years old. Thus, it is unclear how important adaptive radiation is over temporal scales that span much larger portions of the history of life. In this issue, Benson et al. test the idea of a "deep-time" adaptive radiation in dinosaurs, compiling and using one of the most comprehensive phylogenetic and body-size datasets for fossils. Using recent phylogenetic statistical methods, they find that in most clades of dinosaurs there is a strong signal of an "early burst" in body-size evolution, a predicted pattern of adaptive radiation in which rapid trait evolution happens early in a group's history and then slows down. They also find that body-size evolution did not slow down in the lineage leading to birds, hinting at why birds survived to the present day and diversified. This paper represents one of the most convincing attempts at understanding deep-time adaptive radiations.
An improved conscan algorithm based on a Kalman filter
NASA Technical Reports Server (NTRS)
Eldred, D. B.
1994-01-01
Conscan is commonly used by DSN antennas to allow adaptive tracking of a target whose position is not precisely known. This article describes an algorithm that is based on a Kalman filter and is proposed to replace the existing fast Fourier transform based (FFT-based) algorithm for conscan. Advantages of this algorithm include better pointing accuracy, continuous update information, and accommodation of missing data. Additionally, a strategy for adaptive selection of the conscan radius is proposed. The performance of the algorithm is illustrated through computer simulations and compared to the FFT algorithm. The results show that the Kalman filter algorithm is consistently superior.
NASA Astrophysics Data System (ADS)
ul Amin, Rooh; Aijun, Li; Khan, Muhammad Umer; Shamshirband, Shahaboddin; Kamsin, Amirrudin
2017-01-01
In this paper, an adaptive trajectory tracking controller based on extended normalized radial basis function network (ENRBFN) is proposed for 3-degree-of-freedom four rotor hover vehicle subjected to external disturbance i.e. wind turbulence. Mathematical model of four rotor hover system is developed using equations of motions and a new computational intelligence based technique ENRBFN is introduced to approximate the unmodeled dynamics of the hover vehicle. The adaptive controller based on the Lyapunov stability approach is designed to achieve tracking of the desired attitude angles of four rotor hover vehicle in the presence of wind turbulence. The adaptive weight update based on the Levenberg-Marquardt algorithm is used to avoid weight drift in case the system is exposed to external disturbances. The closed-loop system stability is also analyzed using Lyapunov stability theory. Simulations and experimental results are included to validate the effectiveness of the proposed control scheme.
A new class of nonlinear Rauch-Tung-Striebel cubature Kalman smoothers.
Jia, Bin; Xin, Ming
2015-03-01
In this paper, a new Rauch-Tung-Striebel type of nonlinear smoothing method is proposed based on a class of high-degree cubature integration rules. This new class of cubature Kalman smoothers generalizes the conventional third-degree cubature Kalman smoother using the combination of Genz׳s or Mysovskikh׳s high-degree spherical rule with the moment matching based arbitrary-degree radial rule, which considerably improves the estimation accuracy. A target tracking problem is utilized to demonstrate the performance of this new smoother and to compare it with other Gaussian approximation smoothers. It will be shown that this new cubature Kalman smoother enhances the filtering accuracy and outperforms the extended Kalman smoother, the unscented Kalman smoother, and the conventional third-degree cubature Kalman smoother. It also maintains close performance to the Gauss-Hermite quadrature smoother with much less computational cost.
NASA Astrophysics Data System (ADS)
Börger, Klaus; Schmidt, Michael; Dettmering, Denise; Limberger, Marco; Erdogan, Eren; Seitz, Florian; Brandert, Sylvia; Görres, Barbara; Kersten, Wilhelm; Bothmer, Volker; Hinrichs, Johannes; Venzmer, Malte; Mrotzek, Niclas
2016-04-01
Today, the observations of space geodetic techniques are usually available with a rather low latency which applies to space missions observing the solar terrestrial environment, too. Therefore, we can use all these measurements in near real-time to compute and to provide ionosphere information, e.g. the vertical total electron content (VTEC). GSSAC and BGIC support a project aiming at a service for providing ionosphere information. This project is called OPTIMAP, meaning "Operational Tool for Ionosphere Mapping and Prediction"; the scientific work is mainly done by the German Geodetic Research Institute of the Technical University Munich (DGFI-TUM) and the Institute for Astrophysics of the University of Goettingen (IAG). The OPTIMAP strategy for providing ionosphere target quantities of high quality, such as VTEC or the electron density, includes mathematical approaches and tools allowing for the model adaptation to the real observational scenario as a significant improvement w.r.t. the traditional well-established methods. For example, OPTIMAP combines different observation types such as GNSS (GPS, GLONASS), Satellite Altimetry (Jason-2), DORIS as well as radio-occultation measurements (FORMOSAT#3/COSMIC). All these observations run into a Kalman-filter to compute global ionosphere maps, i.e. VTEC, for the current instant of time and as a forecast for a couple of subsequent days. Mathematically, the global VTEC is set up as a series expansion in terms of two-dimensional basis functions defined as tensor products of trigonometric B-splines for longitude and polynomial B-splines for latitude. Compared to the classical spherical harmonics, B-splines have a localizing character and, therefore, can handle an inhomogeneous data distribution properly. Finally, B-splines enable a so-called multi-resolution-representation (MRR) enabling the combination of global and regional modelling approaches. In addition to the geodetic measurements, Sun observations are pre
2014-12-01
wideband waveform. 14. SUBJECT TERMS waveform design, eigen waveform, ambiguity function, target identification , target detection , range Doppler map...are also interested in identification of extended targets . And finally, the third objective (which utilizes the results of the first two) is to...design an integrated scheme for the combined problem of range-Doppler location/ detection with extended target type identification with the use of a
Absar, Syeda Mariya; Preston, Benjamin L.
2015-05-25
The exploration of alternative socioeconomic futures is an important aspect of understanding the potential consequences of climate change. While socioeconomic scenarios are common and, at times essential, tools for the impact, adaptation and vulnerability and integrated assessment modeling research communities, their approaches to scenario development have historically been quite distinct. However, increasing convergence of impact, adaptation and vulnerability and integrated assessment modeling research in terms of scales of analysis suggests there may be value in the development of a common framework for socioeconomic scenarios. The Shared Socioeconomic Pathways represents an opportunity for the development of such a common framework. However, the scales at which these global storylines have been developed are largely incommensurate with the sub-national scales at which impact, adaptation and vulnerability, and increasingly integrated assessment modeling, studies are conducted. Our objective for this study was to develop sub-national and sectoral extensions of the global SSP storylines in order to identify future socioeconomic challenges for adaptation for the U.S. Southeast. A set of nested qualitative socioeconomic storyline elements, integrated storylines, and accompanying quantitative indicators were developed through an application of the Factor-Actor-Sector framework. Finally, in addition to revealing challenges and opportunities associated with the use of the SSPs as a basis for more refined scenario development, this study generated sub-national storyline elements and storylines that can subsequently be used to explore the implications of alternative subnational socioeconomic futures for the assessment of climate change impacts and adaptation.
Generating code adapted for interlinking legacy scalar code and extended vector code
Gschwind, Michael K
2013-06-04
Mechanisms for intermixing code are provided. Source code is received for compilation using an extended Application Binary Interface (ABI) that extends a legacy ABI and uses a different register configuration than the legacy ABI. First compiled code is generated based on the source code, the first compiled code comprising code for accommodating the difference in register configurations used by the extended ABI and the legacy ABI. The first compiled code and second compiled code are intermixed to generate intermixed code, the second compiled code being compiled code that uses the legacy ABI. The intermixed code comprises at least one call instruction that is one of a call from the first compiled code to the second compiled code or a call from the second compiled code to the first compiled code. The code for accommodating the difference in register configurations is associated with the at least one call instruction.
NASA Astrophysics Data System (ADS)
Luo, Shaohua; Sun, Quanping; Cheng, Wei
2016-04-01
This paper addresses chaos control of the micro-electro- mechanical resonator by using adaptive dynamic surface technology with extended state observer. To reveal the mechanism of the micro- electro-mechanical resonator, the phase diagrams and corresponding time histories are given to research the nonlinear dynamics and chaotic behavior, and Homoclinic and heteroclinic chaos which relate closely with the appearance of chaos are presented based on the potential function. To eliminate the effect of chaos, an adaptive dynamic surface control scheme with extended state observer is designed to convert random motion into regular motion without precise system model parameters and measured variables. Putting tracking differentiator into chaos controller solves the `explosion of complexity' of backstepping and poor precision of the first-order filters. Meanwhile, to obtain high performance, a neural network with adaptive law is employed to approximate unknown nonlinear function in the process of controller design. The boundedness of all the signals of the closed-loop system is proved in theoretical analysis. Finally, numerical simulations are executed and extensive results illustrate effectiveness and robustness of the proposed scheme.
Absar, Syeda Mariya; Preston, Benjamin L.
2015-05-25
The exploration of alternative socioeconomic futures is an important aspect of understanding the potential consequences of climate change. While socioeconomic scenarios are common and, at times essential, tools for the impact, adaptation and vulnerability and integrated assessment modeling research communities, their approaches to scenario development have historically been quite distinct. However, increasing convergence of impact, adaptation and vulnerability and integrated assessment modeling research in terms of scales of analysis suggests there may be value in the development of a common framework for socioeconomic scenarios. The Shared Socioeconomic Pathways represents an opportunity for the development of such a common framework. However,more » the scales at which these global storylines have been developed are largely incommensurate with the sub-national scales at which impact, adaptation and vulnerability, and increasingly integrated assessment modeling, studies are conducted. Our objective for this study was to develop sub-national and sectoral extensions of the global SSP storylines in order to identify future socioeconomic challenges for adaptation for the U.S. Southeast. A set of nested qualitative socioeconomic storyline elements, integrated storylines, and accompanying quantitative indicators were developed through an application of the Factor-Actor-Sector framework. Finally, in addition to revealing challenges and opportunities associated with the use of the SSPs as a basis for more refined scenario development, this study generated sub-national storyline elements and storylines that can subsequently be used to explore the implications of alternative subnational socioeconomic futures for the assessment of climate change impacts and adaptation.« less
Discovery of the Kalman filter as a practical tool for aerospace and industry
NASA Technical Reports Server (NTRS)
Mcgee, L. A.; Schmidt, S. F.
1985-01-01
The sequence of events which led the researchers at Ames Research Center to the early discovery of the Kalman filter shortly after its introduction into the literature is recounted. The scientific breakthroughs and reformulations that were necessary to transform Kalman's work into a useful tool for a specific aerospace application are described. The resulting extended Kalman filter, as it is now known, is often still referred to simply as the Kalman filter. As the filter's use gained in popularity in the scientific community, the problems of implementation on small spaceborne and airborne computers led to a square-root formulation of the filter to overcome numerical difficulties associated with computer word length. The work that led to this new formulation is also discussed, including the first airborne computer implementation and flight test. Since then the applications of the extended and square-root formulations of the Kalman filter have grown rapidly throughout the aerospace industry.
Fast Kalman filtering on quasilinear dendritic trees.
Paninski, Liam
2010-04-01
Optimal filtering of noisy voltage signals on dendritic trees is a key problem in computational cellular neuroscience. However, the state variable in this problem-the vector of voltages at every compartment-is very high-dimensional: realistic multicompartmental models often have on the order of N = 10(4) compartments. Standard implementations of the Kalman filter require O(N (3)) time and O(N (2)) space, and are therefore impractical. Here we take advantage of three special features of the dendritic filtering problem to construct an efficient filter: (1) dendritic dynamics are governed by a cable equation on a tree, which may be solved using sparse matrix methods in O(N) time; and current methods for observing dendritic voltage (2) provide low SNR observations and (3) only image a relatively small number of compartments at a time. The idea is to approximate the Kalman equations in terms of a low-rank perturbation of the steady-state (zero-SNR) solution, which may be obtained in O(N) time using methods that exploit the sparse tree structure of dendritic dynamics. The resulting methods give a very good approximation to the exact Kalman solution, but only require O(N) time and space. We illustrate the method with applications to real and simulated dendritic branching structures, and describe how to extend the techniques to incorporate spatially subsampled, temporally filtered, and nonlinearly transformed observations.
Robust Kriged Kalman Filtering
Baingana, Brian; Dall'Anese, Emiliano; Mateos, Gonzalo; Giannakis, Georgios B.
2015-11-11
Although the kriged Kalman filter (KKF) has well-documented merits for prediction of spatial-temporal processes, its performance degrades in the presence of outliers due to anomalous events, or measurement equipment failures. This paper proposes a robust KKF model that explicitly accounts for presence of measurement outliers. Exploiting outlier sparsity, a novel l1-regularized estimator that jointly predicts the spatial-temporal process at unmonitored locations, while identifying measurement outliers is put forth. Numerical tests are conducted on a synthetic Internet protocol (IP) network, and real transformer load data. Test results corroborate the effectiveness of the novel estimator in joint spatial prediction and outlier identification.
Multilevel ensemble Kalman filtering
Hoel, Hakon; Law, Kody J. H.; Tempone, Raul
2016-06-14
This study embeds a multilevel Monte Carlo sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF) in the setting of finite dimensional signal evolution and noisy discrete-time observations. The signal dynamics is assumed to be governed by a stochastic differential equation (SDE), and a hierarchy of time grids is introduced for multilevel numerical integration of that SDE. Finally, the resulting multilevel EnKF is proved to asymptotically outperform EnKF in terms of computational cost versus approximation accuracy. The theoretical results are illustrated numerically.
Human physiological adaptation to extended Space Flight and its implications for Space Station
NASA Technical Reports Server (NTRS)
Kutyna, F. A.; Shumate, W. H.
1985-01-01
Current work evaluating short-term space flight physiological data on the homeostatic changes due to weightlessness is presented as a means of anticipating Space Station long-term effects. An integrated systems analysis of current data shows a vestibulo-sensory adaptation within days; a loss of body mass, fluids, and electrolytes, stabilizing in a month; and a loss in red cell mass over a month. But bone demineralization which did not level off is seen as the biggest concern. Computer algorithms have been developed to simulate the human adaptation to weightlessness. So far these paradigms have been backed up by flight data and it is hoped that they will provide valuable information for future Space Station design. A series of explanatory schematics is attached.
FALCON: a concept to extend adaptive optics corrections to cosmological fields
NASA Astrophysics Data System (ADS)
Hammer, Francois; Puech, Mathieu; Assemat, Francois F.; Gendron, Eric; Sayede, Frederic; Laporte, Philippe; Marteaud, Michel; Liotard, Arnaud; Zamkotsian, Frederic
2004-07-01
FALCON is an original concept for a next generation spectrograph at ESO VLT or at future ELTs. It is a spectrograph including multiple small integral field units (IFUs) which can be deployed within a large field of view such as that of VLT/GIRAFFE. In FALCON, each IFU features an adaptive optics correction using off-axis natural reference stars in order to combine, in the 0.8 - 1.8 μm wavelength range, spatial and spectral resolutions (0.1 - 0.15 arcsec and R = 1000 +/- 5000). These conditions are ideally suited for distant galaxy studies, which should be done within fields of view larger than the galaxy clustering scales (4 - 9 Mpc), i.e. foV > 100 arcmin. Instead of compensating the whole field, the adaptive correction will be performed locally on each IFU. This implies to use small miniaturized devices both for adaptive optics correction and wavefront sensing. Applications to high latitude fields imply to use atmospheric tomography because the stars required for wavefront sensing will be in most of the cases far outside the isoplanatic patch.
Hardware verification of distributed/adaptive control
NASA Technical Reports Server (NTRS)
Eldred, D. B.; Schaechter, D. B.
1983-01-01
Adaptive control techniques are studied for their future application to the control of large space structures, where uncertain or changing parameters may destabilize standard control system designs. The approach used is to examine an extended Kalman filter estimator, in which the state vector is augmented with the unknown parameters. The associated Riccatti equation is linearized about the case of exact knowledge of the parameters. By assuming that parameter variations occur slowly, the filter complexity is reduced further yet. Simulations on a two degree-of-freedom oscillator demonstrate the parameter-tracking capability of the filter, and an implementation on the JPL Flexible Beam Facility using an incorrect model shows the adaptive filter/optimal control to be stable where a standard Kalman filter/optimal control design is unstable.
Deterministic Mean-Field Ensemble Kalman Filtering
Law, Kody J. H.; Tembine, Hamidou; Tempone, Raul
2016-05-03
The proof of convergence of the standard ensemble Kalman filter (EnKF) from Le Gland, Monbet, and Tran [Large sample asymptotics for the ensemble Kalman filter, in The Oxford Handbook of Nonlinear Filtering, Oxford University Press, Oxford, UK, 2011, pp. 598--631] is extended to non-Gaussian state-space models. In this paper, a density-based deterministic approximation of the mean-field limit EnKF (DMFEnKF) is proposed, consisting of a PDE solver and a quadrature rule. Given a certain minimal order of convergence κ between the two, this extends to the deterministic filter approximation, which is therefore asymptotically superior to standard EnKF for dimension d < 2κ. The fidelity of approximation of the true distribution is also established using an extension of the total variation metric to random measures. Lastly, this is limited by a Gaussian bias term arising from nonlinearity/non-Gaussianity of the model, which arises in both deterministic and standard EnKF. Numerical results support and extend the theory.
Deterministic Mean-Field Ensemble Kalman Filtering
Law, Kody J. H.; Tembine, Hamidou; Tempone, Raul
2016-05-03
The proof of convergence of the standard ensemble Kalman filter (EnKF) from Le Gland, Monbet, and Tran [Large sample asymptotics for the ensemble Kalman filter, in The Oxford Handbook of Nonlinear Filtering, Oxford University Press, Oxford, UK, 2011, pp. 598--631] is extended to non-Gaussian state-space models. In this paper, a density-based deterministic approximation of the mean-field limit EnKF (DMFEnKF) is proposed, consisting of a PDE solver and a quadrature rule. Given a certain minimal order of convergence κ between the two, this extends to the deterministic filter approximation, which is therefore asymptotically superior to standard EnKF for dimension d
Systolic VLSI for Kalman filters
NASA Technical Reports Server (NTRS)
Yeh, H.-G.; Chang, J. J.
1986-01-01
A novel two-dimensional parallel computing method for real-time Kalman filtering is presented. The mathematical formulation of a Kalman filter algorithm is rearranged to be the type of Faddeev algorithm for generalizing signal processing. The data flow mapping from the Faddeev algorithm to a two-dimensional concurrent computing structure is developed. The architecture of the resulting processor cells is regular, simple, expandable, and therefore naturally suitable for VLSI chip implementation. The computing methodology and the two-dimensional systolic arrays are useful for Kalman filter applications as well as other matrix/vector based algebraic computations.
A robust and self-tuning Kalman filter for autonomous spacecraft navigation
NASA Astrophysics Data System (ADS)
Truong, Son Hong
Most navigation systems currently operated by the National Aeronautics and Space Administration (NASA) and other major space agencies (e.g., European Space Agency (ESA)) are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman filter and the Global Positioning System (GPS) or GPS-like data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for spacecraft navigation. Current techniques of Kalman filtering, however, still rely on manual tuning from analysts, and cannot help in optimizing navigation autonomy without compromising accuracy and performance. The re-tuning process is more complex when dealing with geosynchronous and high-eccentricity orbits. This dissertation presents an approach to produce a high accuracy onboard navigation system that can recover from estimation errors in real time. It proposes a sophisticated application of neuro-fuzzy techniques to perform the self-tuning capability. It also demonstrates the feasibility and efficiency of a self-tuning component built from this concept to augment to a Kalman filter, which performs the state estimation. The core requirement is a method of state estimation that handles uncertainties robustly, is capable of identifying estimation problems, flexible enough to make decisions and adjustments to recover from these problems, and compact enough to run on flight software. The scope of the dissertation research has both theoretical and experimental dimensions. In the direction of theory, performance limits of Kalman filter and related major adaptive techniques, and new technologies popular in the areas of system identification and automatic controls are studied, with special emphasis on mathematical issues leading to the optimization of spacecraft navigation autonomy. In the experimental direction, a prototype self-tuning system is designed, developed, and tested. Filtered data from
Circadian adaptation of airline pilots during extended duration operations between the USA and Asia.
Gander, Philippa; van den Berg, Margo; Mulrine, Hannah; Signal, Leigh; Mangie, Jim
2013-10-01
This study tracked circadian adaptation among airline pilots before, during, and after trips where they flew from Seattle (SEA) or Los Angeles (LAX) to Asia (7--9 time zones westward), spent 7--12 d in Asia, and then flew back to the USA. In Asia, pilots' exposures to local time cues and sleep opportunities were constrained by duty (short-haul flights crossing ≤ 1 time zone/24 h). Fourteen captains and 16 first officers participated (median age = 56 versus 48 yrs, p.U) < 0.001). Their sleep was monitored (actigraphy, duty/sleep diaries) from 3 d pre-trip to 5 d post-trip. For every flight, Karolinska Sleepiness and Samn-Perelli Fatigue scales and 5-min psychomotor vigilance task (PVT) tests were completed pre-flight and at top of descent (TOD). Participants had ≥ 3 d free of duty prior to outbound flight(s). From 72--24 h prior to departure (baseline sleep), mean total sleep/24 h (TST) = 7.00 h (SD = 1.18 h) and mean sleep efficiency = 87% (SD = 4.9%). Most pilots (23/30) flew direct to and from Asia, but 7 LAX-based pilots flew via a 1-d layover in Honolulu (HNL). On flights with ≥ 2 pilots, mean total in-flight sleep varied from 0.40 to 2.09 h outbound and from 0.74 to 1.88 h inbound. Duty patterns in Asia were variable, with ≤ 2 flights/d (mean flight duration = 3.53 h, SD = 0.53 h). TST on days 17 in Asia did not differ from baseline (p.F) = 0.2031). However, mean sleep efficiency was significantly lower than baseline on days 5--7 (p.F) = 0.0041). More pilots were on duty between 20:00 and 24:00 h on days 57 (mean = 21%) than on days 24 (mean = 14%). Sleep propensity distribution phase markers and chi-square periodogram analyses suggest that adaptation to local time was complete by day 4 in Asia. On pre-flight PVT tests in Asia, the slowest 10% of responses improved for flights departing 14:00--19:59 h (p.F) = 0.0484). At TOD, the slowest 10% of responses improved across days for flights arriving 14:00--19:59 h (p.F) = 0.0349) and 20:00--01:59 h (p
Kalman filtering for real-time navigator processing.
Spincemaille, Pascal; Nguyen, Thanh D; Prince, Martin R; Wang, Yi
2008-07-01
Navigator echoes are used in high-resolution cardiac MRI for tracking physiological motion to suppress motion artifacts. Alternatives to the conventional diaphragm navigator such as the cardiac fat navigator and the k-space center signal (self-navigator) were developed to monitor heart motion directly. These navigator data can be noisy or may contain undesirable frequency components. Real-time filtering of navigator data without delay, as opposed to the previously used retrospective frequency band filtering, is required for effective prospective navigator gating. One of the commonly used real-time filtering techniques is the Kalman filter, which adaptively estimates motion and suppresses measurement noise by using Bayesian statistics and a motion model. The Kalman filter is investigated in this work to filter noise and distinguish cardiac and respiratory components in navigator data. Preliminary imaging data demonstrate the feasibility of real-time Kalman filtering for prospective respiratory self-gating in CINE cardiac MRI.
Recursive three-dimensional model reconstruction based on Kalman filtering.
Yu, Ying Kin; Wong, Kin Hong; Chang, Michael Ming Yuen
2005-06-01
A recursive two-step method to recover structure and motion from image sequences based on Kalman filtering is described in this paper. The algorithm consists of two major steps. The first step is an extended Kalman filter (EKF) for the estimation of the object's pose. The second step is a set of EKFs, one for each model point, for the refinement of the positions of the model features in the three-dimensional (3-D) space. These two steps alternate from frame to frame. The initial model converges to the final structure as the image sequence is scanned sequentially. The performance of the algorithm is demonstrated with both synthetic data and real-world objects. Analytical and empirical comparisons are made among our approach, the interleaved bundle adjustment method, and the Kalman filtering-based recursive algorithm by Azarbayejani and Pentland. Our approach outperformed the other two algorithms in terms of computation speed without loss in the quality of model reconstruction.
Nonlinear dynamical system identification using unscented Kalman filter
NASA Astrophysics Data System (ADS)
Rehman, M. Javvad ur; Dass, Sarat Chandra; Asirvadam, Vijanth Sagayan
2016-11-01
Kalman Filter is the most suitable choice for linear state space and Gaussian error distribution from decades. In general practical systems are not linear and Gaussian so these assumptions give inconsistent results. System Identification for nonlinear dynamical systems is a difficult task to perform. Usually, Extended Kalman Filter (EKF) is used to deal with non-linearity in which Jacobian method is used for linearizing the system dynamics, But it has been observed that in highly non-linear environment performance of EKF is poor. Unscented Kalman Filter (UKF) is proposed here as a better option because instead of analytical linearization of state space, UKF performs statistical linearization by using sigma point calculated from deterministic samples. Formation of the posterior distribution is based on the propagation of mean and covariance through sigma points.
NASA Astrophysics Data System (ADS)
Yang, Hongjiu; You, Xiu; Liu, Zhixin; Sun, Fuchun
2015-10-01
This paper studies the problem of synchronisation to a desired trajectory for non-linear multi-agent systems. By introducing extended state observer approach, decentralised adaptive controllers are designed for distributed systems which have non-identical unknown non-linear dynamics. The non-identical unknown non-linear dynamics allows for a tracked command dynamics which is also non-linear and unknown. State variables of agents can be obtained only in the case where leader agent and the network communication topology for multi-agent systems is strongly connected digraph network structures. A Lyapunov-function-based approach is given to show that the tracking error is ultimately bounded. Some simulation results are given to demonstrate the effectiveness of the developed techniques in this paper.
NASA Astrophysics Data System (ADS)
Chen, Yan; Oliver, Dean S.
2012-04-01
Dynamic data for parameter estimation in groundwater hydrology are often strongly sensitive to averages of porosity and permeability over extended regions, but only weakly sensitive to the properties of individual gridblocks. Consequently, updates of gridblock parameters from the ensemble Kalman filter (EnKF) will be subject to substantial error due to spurious correlations when the size of the ensemble is small. It has been shown that adaptive localization methods can reduce the magnitude of spurious correlations and regularize updates from the EnKF. In this paper we show that when data are sensitive to an average of parameters over extended regions, adaptive localization methods may eliminate true, but weak, correlations. For this type of data, there appears to be a relationship between the spatial scale of the model variables and the sensitivity of observations to the variables. By introducing a multiscale expansion with adaptive localization, we are able to increase the sensitivity to variables corresponding to large spatial scales and more accurately compute their magnitudes, while eliminating updates to variables corresponding to small spatial scales with weak correlations. Three examples with distinct features are used to compare the new method of combining adaptive localization with wavelet parameterization to the case of no localization and adaptive localization with standard gridblock parameterization. We show that the Kalman gain estimated using the wavelet parameterization with adaptive localization is less biased and has smaller root-mean-square error for the problems in which spatial averaging is important and the Kalman gain has large-scale features.
Estimation of noise parameters in dynamical system identification with Kalman filters.
Kwasniok, Frank
2012-09-01
A method is proposed for determining dynamical and observational noise parameters in state and parameter identification from time series using Kalman filters. The noise covariances are estimated in a secondary optimization by maximizing the predictive likelihood of the data. The approach is based on internal consistency; for the correct noise parameters, the uncertainty projected by the Kalman filter matches the actual predictive uncertainty. The method is able to disentangle dynamical and observational noise. The algorithm is demonstrated for the linear, extended, and unscented Kalman filters using an Ornstein-Uhlenbeck process, the noise-driven Lorenz system, and van der Pol oscillator as well as a paleoclimatic ice-core record as examples. The approach is also applicable to the ensemble Kalman filter and can be readily extended to non-Gaussian estimation frameworks such as Gaussian-sum filters and particle filters.
The discrete-time compensated Kalman filter
NASA Technical Reports Server (NTRS)
Lee, W. H.; Athans, M.
1978-01-01
A suboptimal dynamic compensator to be used in conjunction with the ordinary discrete time Kalman filter was derived. The resultant compensated Kalman Filter has the property that steady state bias estimation errors, resulting from modelling errors, were eliminated.
Choi, Young Joon; Jorshari, Razzi Movassaghi; Djilali, Ned
2015-03-10
Direct numerical simulations of the flow-nanoparticle interaction in a colloidal suspension are presented using an extended finite element method (XFEM) in which the dynamics of the nanoparticles is solved in a fully-coupled manner with the flow. The method is capable of accurately describing solid-fluid interfaces without the need of boundary-fitted meshes to investigate the dynamics of particles in complex flows. In order to accurately compute the high interparticle shear stresses and pressures while minimizing computing costs, an adaptive meshing technique is incorporated with the fluid-structure interaction algorithm. The particle-particle interaction at the microscopic level is modeled using the Lennard-Jones (LJ) potential and the corresponding potential parameters are determined by a scaling procedure. The study is relevant to the preparation of inks used in the fabrication of catalyst layers for fuel cells. In this paper, we are particularly interested in investigating agglomeration of the nanoparticles under external shear flow in a sliding bi-periodic Lees-Edwards frame. The results indicate that the external shear has a crucial impact on the structure formation of colloidal particles in a suspension.
Recent Flight Results of the TRMM Kalman Filter
NASA Technical Reports Server (NTRS)
Andrews, Stephen F.; Bilanow, Stephen; Bauer, Frank (Technical Monitor)
2002-01-01
The Tropical Rainfall Measuring Mission (TRMM) spacecraft is a nadir pointing spacecraft that nominally controls the roll and pitch attitude based on the Earth Sensor Assembly (ESA) output. TRMM's nominal orbit altitude was 350 km, until raised to 402 km to prolong mission life. During the boost, the ESA experienced a decreasing signal to noise ratio, until sun interference at 393 km altitude made the ESA data unreliable for attitude determination. At that point, the backup attitude determination algorithm, an extended Kalman filter, was enabled. After the boost finished, TRMM reacquired its nadir-pointing attitude, and continued its mission. This paper will briefly discuss the boost and the decision to turn on the backup attitude determination algorithm. A description of the extended Kalman filter algorithm will be given. In addition, flight results from analyzing attitude data and the results of software changes made onboard TRMM will be discussed. Some lessons learned are presented.
Real-time shipboard orbit determination using Kalman filtering techniques
NASA Technical Reports Server (NTRS)
Brammer, R. F.
1974-01-01
The real-time tracking and orbit determination program used on board the NASA tracking ship, the USNS Vanguard, is described in this paper. The computer program uses a variety of filtering algorithms, including an extended Kalman filter, to derive real-time orbit determinations (position-velocity state vectors) from shipboard tracking and navigation data. Results from Apollo missions are given to show that orbital parameters can be estimated quickly and accurately using these methods.
Optimizing aircraft performance with adaptive, integrated flight/propulsion control
NASA Technical Reports Server (NTRS)
Smith, R. H.; Chisholm, J. D.; Stewart, J. F.
1991-01-01
The Performance-Seeking Control (PSC) integrated flight/propulsion adaptive control algorithm presented was developed in order to optimize total aircraft performance during steady-state engine operation. The PSC multimode algorithm minimizes fuel consumption at cruise conditions, while maximizing excess thrust during aircraft accelerations, climbs, and dashes, and simultaneously extending engine service life through reduction of fan-driving turbine inlet temperature upon engagement of the extended-life mode. The engine models incorporated by the PSC are continually upgraded, using a Kalman filter to detect anomalous operations. The PSC algorithm will be flight-demonstrated by an F-15 at NASA-Dryden.
VLBI TRF determination via Kalman filtering
NASA Astrophysics Data System (ADS)
Soja, Benedikt; Karbon, Maria; Nilsson, Tobias; Glaser, Susanne; Balidakis, Kyriakos; Heinkelmann, Robert; Schuh, Harald
2015-04-01
The determination of station positions is one of the primary tasks for space geodetic techniques. Station coordinate offsets are usually determined with respect to a linear coordinate model after removing elastic displacements caused by mass redistributions within the Earth's system. In operational VLBI analysis, the coordinate offsets are estimated in a least-squares adjustment as a constant over the duration of a 24-hour VLBI experiment. Terrestrial reference frames (TRF) are usually derived by adjusting the normal equations that contain the 24-hour constant offsets in order to estimate a linear model, possibly including breaks, for the station positions. We have created a VLBI TRF solution without the assumption of negligible subdaily motion and of linear behavior on longer time scales by applying a Kalman filter. As a preparation for the upcoming VLBI Global Observing System (VGOS), which aims for continuous observations that are available in real-time, a Kalman filter has been implemented into the VLBI software VieVS@GFZ. In addition to the real-time capability, the filter offers the possibility of stochastically modeling the parameters of interest. For station coordinates, changes in a subdaily time frame occur, for instance, from un- or mismodeled geophysical effects. The models for tidal and non-tidal ocean, atmosphere, and hydrology loading are known to have deficiencies and inconsistencies which propagate into the estimated station coordinates. The stochastic model of the Kalman filter can be adapted to take these subdaily effects into account. Comparing the resulting station coordinate time series with daily values from a least squares fit, we have investigated to what extent and in which regions the loading models currently have deficiencies. Due to the high correlation between station height and tropospheric delays, it is possible that errors in one group of parameters are partly absorbed by the other group. To detect problems with correlations and to
Kalman Filters in Improving the Signal to Noise Ratio of Full Tensor Gravity Gradiometry Data
NASA Astrophysics Data System (ADS)
Sepehrmanesh, M.; Ravat, D.
2014-12-01
We have applied several extensions and optimal smoothing approaches of the Kalman filter, one of the best known recursive data processing techniques, on the Full Tensor Gradiometry (FTG) data acquired by Bell Geospace over the Vinton salt dome located in southwest Louisiana. We used the filter to improve the signal-to-noise ratio of gravity gradiometry components. We tested the standard Kalman filter and Fading memory and Constrained Kalman filter extensions with Fixed-lag and Forward-Backward smoothing methods to maintain symmetry. Our most meaningful results were obtained through the Kalman filter with the constraint of Laplace's equation combined with the Forward-Backward filter operations. Laplace's equation constraint was incorporated using two separate strategies: Model reduction and Perfect constraint (or Perfect measurement). In general, Kalman filter processed data have greater dynamic range than previously filtered data and also have the ability to extract signal from noisy data without having to remove a band of wavenumbers. In addition, our constrained Kalman filter also has the ability to force the Laplace's equation constraint. These characteristics enable the Kalman filter to investigate short wavelength signals associated with near-surface lateral density variations. In analyzing two dimensional maps for geologic variations, our workflow includes leveling and decorrugation, both procedures necessary for data processed along profiles. Several previously mapped near-subsurface geologic features like faults and their continuity in the Vinton dome area are more readily apparent in our Kalman filter processed components. Since the processed data generally agree with the previously mapped and interpreted structures, the interpretation could be extended to previously unmapped areas. The use of Kalman filtering in combination with Laplace's equation in applications such as gravity and magnetic gradiometry could be useful in determining more precisely the
Adaptive Filtering Using Recurrent Neural Networks
NASA Technical Reports Server (NTRS)
Parlos, Alexander G.; Menon, Sunil K.; Atiya, Amir F.
2005-01-01
A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.
Fu, Haohao; Shao, Xueguang; Chipot, Christophe; Cai, Wensheng
2016-08-09
Proper use of the adaptive biasing force (ABF) algorithm in free-energy calculations needs certain prerequisites to be met, namely, that the Jacobian for the metric transformation and its first derivative be available and the coarse variables be independent and fully decoupled from any holonomic constraint or geometric restraint, thereby limiting singularly the field of application of the approach. The extended ABF (eABF) algorithm circumvents these intrinsic limitations by applying the time-dependent bias onto a fictitious particle coupled to the coarse variable of interest by means of a stiff spring. However, with the current implementation of eABF in the popular molecular dynamics engine NAMD, a trajectory-based post-treatment is necessary to derive the underlying free-energy change. Usually, such a posthoc analysis leads to a decrease in the reliability of the free-energy estimates due to the inevitable loss of information, as well as to a drop in efficiency, which stems from substantial read-write accesses to file systems. We have developed a user-friendly, on-the-fly code for performing eABF simulations within NAMD. In the present contribution, this code is probed in eight illustrative examples. The performance of the algorithm is compared with traditional ABF, on the one hand, and the original eABF implementation combined with a posthoc analysis, on the other hand. Our results indicate that the on-the-fly eABF algorithm (i) supplies the correct free-energy landscape in those critical cases where the coarse variables at play are coupled to either each other or to geometric restraints or holonomic constraints, (ii) greatly improves the reliability of the free-energy change, compared to the outcome of a posthoc analysis, and (iii) represents a negligible additional computational effort compared to regular ABF. Moreover, in the proposed implementation, guidelines for choosing two parameters of the eABF algorithm, namely the stiffness of the spring and the mass
NASA Astrophysics Data System (ADS)
Kocaoglu, Omer P.; Lee, Sangyeol; Jonnal, Ravi S.; Wang, Qiang; Herde, Ashley E.; Besecker, Jason; Gao, Weihua; Miller, Donald T.
2011-03-01
Optical coherence tomography with adaptive optics (AO-OCT) is a highly sensitive, noninvasive method for 3D imaging of the microscopic retina. The purpose of this study is to advance AO-OCT technology by enabling repeated imaging of cone photoreceptors over extended periods of time (days). This sort of longitudinal imaging permits monitoring of 3D cone dynamics in both normal and diseased eyes, in particular the physiological processes of disc renewal and phagocytosis, which are disrupted by retinal diseases such as age related macular degeneration and retinitis pigmentosa. For this study, the existing AO-OCT system at Indiana underwent several major hardware and software improvements to optimize system performance for 4D cone imaging. First, ultrahigh speed imaging was realized using a Basler Sprint camera. Second, a light source with adjustable spectrum was realized by integration of an Integral laser (Femto Lasers, λc=800nm, ▵λ=160nm) and spectral filters in the source arm. For cone imaging, we used a bandpass filter with λc=809nm and ▵λ=81nm (2.6 μm nominal axial resolution in tissue, and 167 KHz A-line rate using 1,408 px), which reduced the impact of eye motion compared to previous AO-OCT implementations. Third, eye motion artifacts were further reduced by custom ImageJ plugins that registered (axially and laterally) the volume videos. In two subjects, cone photoreceptors were imaged and tracked over a ten day period and their reflectance and outer segment (OS) lengths measured. High-speed imaging and image registration/dewarping were found to reduce eye motion to a fraction of a cone width (1 μm root mean square). The pattern of reflections in the cones was found to change dramatically and occurred on a spatial scale well below the resolution of clinical instruments. Normalized reflectance of connecting cilia (CC) and OS posterior tip (PT) of an exemplary cone was 54+/-4, 47+/-4, 48+/-6, 50+/-5, 56+/-1% and 46+/-4, 53+/-4, 52+/-6, 50+/-5, 44
On the evaluation of uncertainties for state estimation with the Kalman filter
NASA Astrophysics Data System (ADS)
Eichstädt, S.; Makarava, N.; Elster, C.
2016-12-01
The Kalman filter is an established tool for the analysis of dynamic systems with normally distributed noise, and it has been successfully applied in numerous areas. It provides sequentially calculated estimates of the system states along with a corresponding covariance matrix. For nonlinear systems, the extended Kalman filter is often used. This is derived from the Kalman filter by linearization around the current estimate. A key issue in metrology is the evaluation of the uncertainty associated with the Kalman filter state estimates. The ‘Guide to the Expression of Uncertainty in Measurement’ (GUM) and its supplements serve as the de facto standard for uncertainty evaluation in metrology. We explore the relationship between the covariance matrix produced by the Kalman filter and a GUM-compliant uncertainty analysis. In addition, the results of a Bayesian analysis are considered. For the case of linear systems with known system matrices, we show that all three approaches are compatible. When the system matrices are not precisely known, however, or when the system is nonlinear, this equivalence breaks down and different results can then be reached. For precisely known nonlinear systems, though, the result of the extended Kalman filter still corresponds to the linearized uncertainty propagation of the GUM. The extended Kalman filter can suffer from linearization and convergence errors. These disadvantages can be avoided to some extent by applying Monte Carlo procedures, and we propose such a method which is GUM-compliant and can also be applied online during the estimation. We illustrate all procedures in terms of a 2D dynamic system and compare the results with those obtained by particle filtering, which has been proposed for the approximate calculation of a Bayesian solution. Finally, we give some recommendations based on our findings.
Application of Kalman filters to robot calibration
NASA Technical Reports Server (NTRS)
Whitney, D. E.; Junkel, E. F.
1983-01-01
This report explores new uses of Kalman filter theory in manufacturing systems (robots in particular). The Kalman filter allows the robot to read its sensors plus external sensors and learn from its experience. In effect, the robot is given primitive intelligence. The study, which is applicable to any type of powered kinematic linkage, focuses on the calibration of a manipulator.
A Localized Ensemble Kalman Smoother
NASA Technical Reports Server (NTRS)
Butala, Mark D.
2012-01-01
Numerous geophysical inverse problems prove difficult because the available measurements are indirectly related to the underlying unknown dynamic state and the physics governing the system may involve imperfect models or unobserved parameters. Data assimilation addresses these difficulties by combining the measurements and physical knowledge. The main challenge in such problems usually involves their high dimensionality and the standard statistical methods prove computationally intractable. This paper develops and addresses the theoretical convergence of a new high-dimensional Monte-Carlo approach called the localized ensemble Kalman smoother.
All Source Sensor Integration Using an Extended Kalman Filter
2012-03-22
Electrical and Computer Engineering Graduate School of Engineering and Management Air Force Institute of Technology Air University Air Education and...but was called home too early to make it. I miss you more than you could ever know. Timothy R. Penn v Contents Page Abstract...filter equations. The discrete time measurements take on the form: zk = Hkxk + vk (2.42) Rk = E[(vk)(vk) T ] (2.43) where each element of (2.42) is the
Multiplicative Quaternion Extended Kalman Filtering for Nonspinning Guided Projectiles
2013-07-01
The magnetometer is not an inertial sensor , and its error dynamics are uncoupled with the inertial navigation states, but a bias term is still... Sensors and Flight Dynamics ; ARL-TR-5994; U.S. Army Research Laboratory: Aberdeen Proving Ground, MD, 2012. 8. Titterton, D. H.; Weston, J. L...24 Figure 5. Direct-fire gyro -bias errors
Projecting Ice-Affected Streamflow by Extended Kalman Filtering,
1997-12-01
temperature and the relation between stage 87.2% of the time and within 15% of published values and flow during open- water conditions. The form 96.6% of... Water Resources Division, Lansing, Michigan, and Dr. Mohinder S. Grewal, Professor of Electrical Engineering, California State Univer- sity at Fullerton...record streamflow-gaging stations nationwide (Condes de la Torre 1994). Direct measurements of flow and stage ( water -surface elevation in the waterway) are
Extended Kalman Filter framework for forecasting shoreline evolution
Long, Joseph; Plant, Nathaniel G.
2012-01-01
A shoreline change model incorporating both long- and short-term evolution is integrated into a data assimilation framework that uses sparse observations to generate an updated forecast of shoreline position and to estimate unobserved geophysical variables and model parameters. Application of the assimilation algorithm provides quantitative statistical estimates of combined model-data forecast uncertainty which is crucial for developing hazard vulnerability assessments, evaluation of prediction skill, and identifying future data collection needs. Significant attention is given to the estimation of four non-observable parameter values and separating two scales of shoreline evolution using only one observable morphological quantity (i.e. shoreline position).
Broom, Donald M
2006-01-01
The term adaptation is used in biology in three different ways. It may refer to changes which occur at the cell and organ level, or at the individual level, or at the level of gene action and evolutionary processes. Adaptation by cells, especially nerve cells helps in: communication within the body, the distinguishing of stimuli, the avoidance of overload and the conservation of energy. The time course and complexity of these mechanisms varies. Adaptive characters of organisms, including adaptive behaviours, increase fitness so this adaptation is evolutionary. The major part of this paper concerns adaptation by individuals and its relationships to welfare. In complex animals, feed forward control is widely used. Individuals predict problems and adapt by acting before the environmental effect is substantial. Much of adaptation involves brain control and animals have a set of needs, located in the brain and acting largely via motivational mechanisms, to regulate life. Needs may be for resources but are also for actions and stimuli which are part of the mechanism which has evolved to obtain the resources. Hence pigs do not just need food but need to be able to carry out actions like rooting in earth or manipulating materials which are part of foraging behaviour. The welfare of an individual is its state as regards its attempts to cope with its environment. This state includes various adaptive mechanisms including feelings and those which cope with disease. The part of welfare which is concerned with coping with pathology is health. Disease, which implies some significant effect of pathology, always results in poor welfare. Welfare varies over a range from very good, when adaptation is effective and there are feelings of pleasure or contentment, to very poor. A key point concerning the concept of individual adaptation in relation to welfare is that welfare may be good or poor while adaptation is occurring. Some adaptation is very easy and energetically cheap and
Lei, Xusheng; Li, Jingjing
2012-01-01
This paper presents an adaptive information fusion method to improve the accuracy and reliability of the altitude measurement information for small unmanned aerial rotorcraft during the landing process. Focusing on the low measurement performance of sensors mounted on small unmanned aerial rotorcraft, a wavelet filter is applied as a pre-filter to attenuate the high frequency noises in the sensor output. Furthermore, to improve altitude information, an adaptive extended Kalman filter based on a maximum a posteriori criterion is proposed to estimate measurement noise covariance matrix in real time. Finally, the effectiveness of the proposed method is proved by static tests, hovering flight and autonomous landing flight tests. PMID:23201993
An Extension to the Kalman Filter for an Improved Detection of Unknown Behavior
NASA Technical Reports Server (NTRS)
Benazera, Emmanuel; Narasimhan, Sriram
2005-01-01
The use of Kalman filter (KF) interferes with fault detection algorithms based on the residual between estimated and measured variables, since the measured values are used to update the estimates. This feedback results in the estimates being pulled closer to the measured values, influencing the residuals in the process. Here we present a fault detection scheme for systems that are being tracked by a KF. Our approach combines an open-loop prediction over an adaptive window and an information-based measure of the deviation of the Kalman estimate from the prediction to improve fault detection.
Adaptive processing for LANDSAT data
NASA Technical Reports Server (NTRS)
Crane, R. B.; Reyer, J. F.
1975-01-01
Analytical and test results on the use of adaptive processing on LANDSAT data are presented. The Kalman filter was used as a framework to contain different adapting techniques. When LANDSAT MSS data were used all of the modifications made to the Kalman filter performed the functions for which they were designed. It was found that adaptive processing could provide compensation for incorrect signature means, within limits. However, if the data were such that poor classification accuracy would be obtained when the correct means were used, then adaptive processing would not improve the accuracy and might well lower it even further.
Goldberg, Emily L; Romero-Aleshire, Melissa J; Renkema, Kristin R; Ventevogel, Melissa S; Chew, Wade M; Uhrlaub, Jennifer L; Smithey, Megan J; Limesand, Kirsten H; Sempowski, Gregory D; Brooks, Heddwen L; Nikolich-Žugich, Janko
2015-01-01
Aging of the world population and a concomitant increase in age-related diseases and disabilities mandates the search for strategies to increase healthspan, the length of time an individual lives healthy and productively. Due to the age-related decline of the immune system, infectious diseases remain among the top 5–10 causes of mortality and morbidity in the elderly, and improving immune function during aging remains an important aspect of healthspan extension. Calorie restriction (CR) and more recently rapamycin (rapa) feeding have both been used to extend lifespan in mice. Preciously few studies have actually investigated the impact of each of these interventions upon in vivo immune defense against relevant microbial challenge in old organisms. We tested how rapa and CR each impacted the immune system in adult and old mice. We report that each intervention differentially altered T-cell development in the thymus, peripheral T-cell maintenance, T-cell function and host survival after West Nile virus infection, inducing distinct but deleterious consequences to the aging immune system. We conclude that neither rapa feeding nor CR, in the current form/administration regimen, may be optimal strategies for extending healthy immune function and, with it, lifespan. PMID:25424641
A Self-Tuning Kalman Filter for Autonomous Spacecraft Navigation
NASA Technical Reports Server (NTRS)
Truong, Son H.
1998-01-01
Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman Filter and Global Positioning System (GPS) data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. Current techniques of Kalman filtering, however, still rely on manual tuning from analysts, and cannot help in optimizing autonomy without compromising accuracy and performance. This paper presents an approach to produce a high accuracy autonomous navigation system fully integrated with the flight system. The resulting system performs real-time state estimation by using an Extended Kalman Filter (EKF) implemented with high-fidelity state dynamics model, as does the GPS Enhanced Orbit Determination Experiment (GEODE) system developed by the NASA Goddard Space Flight Center. Augmented to the EKF is a sophisticated neural-fuzzy system, which combines the explicit knowledge representation of fuzzy logic with the learning power of neural networks. The fuzzy-neural system performs most of the self-tuning capability and helps the navigation system recover from estimation errors. The core requirement is a method of state estimation that handles uncertainties robustly, capable of identifying estimation problems, flexible enough to make decisions and adjustments to recover from these problems, and compact enough to run on flight hardware. The resulting system can be extended to support geosynchronous spacecraft and high-eccentricity orbits. Mathematical methodology, systems and operations concepts, and implementation of a system prototype are presented in this paper. Results from the use of the prototype to evaluate optimal control algorithms implemented are discussed. Test data and major control issues (e.g., how to define specific roles for fuzzy logic to support the self-learning capability) are also
Gauterin, Eckhard; Kammerer, Philipp; Kühn, Martin; Schulte, Horst
2016-05-01
Advanced model-based control of wind turbines requires knowledge of the states and the wind speed. This paper benchmarks a nonlinear Takagi-Sugeno observer for wind speed estimation with enhanced Kalman Filter techniques: The performance and robustness towards model-structure uncertainties of the Takagi-Sugeno observer, a Linear, Extended and Unscented Kalman Filter are assessed. Hence the Takagi-Sugeno observer and enhanced Kalman Filter techniques are compared based on reduced-order models of a reference wind turbine with different modelling details. The objective is the systematic comparison with different design assumptions and requirements and the numerical evaluation of the reconstruction quality of the wind speed. Exemplified by a feedforward loop employing the reconstructed wind speed, the benefit of wind speed estimation within wind turbine control is illustrated.
Highway traffic estimation of improved precision using the derivative-free nonlinear Kalman Filter
NASA Astrophysics Data System (ADS)
Rigatos, Gerasimos; Siano, Pierluigi; Zervos, Nikolaos; Melkikh, Alexey
2015-12-01
The paper proves that the PDE dynamic model of the highway traffic is a differentially flat one and by applying spatial discretization its shows that the model's transformation into an equivalent linear canonical state-space form is possible. For the latter representation of the traffic's dynamics, state estimation is performed with the use of the Derivative-free nonlinear Kalman Filter. The proposed filter consists of the Kalman Filter recursion applied on the transformed state-space model of the highway traffic. Moreover, it makes use of an inverse transformation, based again on differential flatness theory which enables to obtain estimates of the state variables of the initial nonlinear PDE model. By avoiding approximate linearizations and the truncation of nonlinear terms from the PDE model of the traffic's dynamics the proposed filtering methods outperforms, in terms of accuracy, other nonlinear estimators such as the Extended Kalman Filter. The article's theoretical findings are confirmed through simulation experiments.
Fujita, Masaki; Yamasaki, Shinji; Katagiri, Chiaki; Oshiro, Itsuro; Sano, Katsuhiro; Kurozumi, Taiji; Sugawara, Hiroshi; Kunikita, Dai; Matsuzaki, Hiroyuki; Kano, Akihiro; Okumura, Tomoyo; Sone, Tomomi; Fujita, Hikaru; Kobayashi, Satoshi; Naruse, Toru; Kondo, Megumi; Matsu’ura, Shuji; Suwa, Gen; Kaifu, Yousuke
2016-01-01
Maritime adaptation was one of the essential factors that enabled modern humans to disperse all over the world. However, geographic distribution of early maritime technology during the Late Pleistocene remains unclear. At this time, the Indonesian Archipelago and eastern New Guinea stand as the sole, well-recognized area for secure Pleistocene evidence of repeated ocean crossings and advanced fishing technology. The incomplete archeological records also make it difficult to know whether modern humans could sustain their life on a resource-poor, small oceanic island for extended periods with Paleolithic technology. We here report evidence from a limestone cave site on Okinawa Island, Japan, of successive occupation that extends back to 35,000−30,000 y ago. Well-stratified strata at the Sakitari Cave site yielded a rich assemblage of seashell artifacts, including formally shaped tools, beads, and the world’s oldest fishhooks. These are accompanied by seasonally exploited food residue. The persistent occupation on this relatively small, geographically isolated island, as well as the appearance of Paleolithic sites on nearby islands by 30,000 y ago, suggest wider distribution of successful maritime adaptations than previously recognized, spanning the lower to midlatitude areas in the western Pacific coastal region. PMID:27638208
The Role of Scale and Model Bias in ADAPT's Photospheric Eatimation
Godinez Vazquez, Humberto C.; Hickmann, Kyle Scott; Arge, Charles Nicholas; Henney, Carl
2015-05-20
The Air Force Assimilative Photospheric flux Transport model (ADAPT), is a magnetic flux propagation based on Worden-Harvey (WH) model. ADAPT would be used to provide a global photospheric map of the Earth. A data assimilation method based on the Ensemble Kalman Filter (EnKF), a method of Monte Carlo approximation tied with Kalman filtering, is used in calculating the ADAPT models.
Theory and application of Kalman filtering
NASA Technical Reports Server (NTRS)
Teng, L.
1970-01-01
As a unified extension of a group of related mathematical procedures, Kalman filtering is of assistance in the design of aircraft- and ground-based guidance and navigation data reduction and display systems.
Sauer, Juergen; Chavaillaz, Alain
2017-01-01
This experiment aimed to examine how skill lay-off and system reliability would affect operator behaviour in a simulated work environment under wide-range and large-choice adaptable automation comprising six different levels. Twenty-four participants were tested twice during a 2-hr testing session, with the second session taking place 8 months after the first. In the middle of the second testing session, system reliability changed. The results showed that after the retention interval trust increased and self-confidence decreased. Complacency was unaffected by the lay-off period. Diagnostic speed slowed down after the retention interval but diagnostic accuracy was maintained. No difference between experimental conditions was found for automation management behaviour (i.e. level of automation chosen and frequency of switching between levels). There were few effects of system reliability. Overall, the findings showed that subjective measures were more sensitive to the impact of skill lay-off than objective behavioural measures.
NASA Astrophysics Data System (ADS)
Bargatze, L. F.
2015-12-01
Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted
Reduced Kalman Filters for Clock Ensembles
NASA Technical Reports Server (NTRS)
Greenhall, Charles A.
2011-01-01
This paper summarizes the author's work ontimescales based on Kalman filters that act upon the clock comparisons. The natural Kalman timescale algorithm tends to optimize long-term timescale stability at the expense of short-term stability. By subjecting each post-measurement error covariance matrix to a non-transparent reduction operation, one obtains corrected clocks with improved short-term stability and little sacrifice of long-term stability.
Barsov, Eugene V.; Payne, William S.; Hughes, Stephen H.
2001-01-01
We have designed and characterized two new replication-competent avian sarcoma/leukosis virus-based retroviral vectors with amphotropic and ecotropic host ranges. The amphotropic vector RCASBP-M2C(797-8), was obtained by passaging the chimeric retroviral vector RCASBP-M2C(4070A) (6) in chicken embryos. The ecotropic vector, RCASBP(Eco), was created by replacing the env-coding region in the retroviral vector RCASBP(A) with the env region from an ecotropic murine leukemia virus. It replicates efficiently in avian DFJ8 cells that express murine ecotropic receptor. For both vectors, permanent cell lines that produce viral stocks with titers of about 5 × 106 CFU/ml on mammalian cells can be easily established by passaging transfected avian cells. Some chimeric viruses, for example, RCASBP(Eco), replicate efficiently without modifications. For those chimeric viruses that do require modification, adaptation by passage in vitro or in vivo is a general strategy. This strategy has been used to prepare vectors with altered host range and could potentially be used to develop vectors that would be useful for targeted gene delivery. PMID:11333876
Barsov, E V; Payne, W S; Hughes, S H
2001-06-01
We have designed and characterized two new replication-competent avian sarcoma/leukosis virus-based retroviral vectors with amphotropic and ecotropic host ranges. The amphotropic vector RCASBP-M2C(797-8), was obtained by passaging the chimeric retroviral vector RCASBP-M2C(4070A) (6) in chicken embryos. The ecotropic vector, RCASBP(Eco), was created by replacing the env-coding region in the retroviral vector RCASBP(A) with the env region from an ecotropic murine leukemia virus. It replicates efficiently in avian DFJ8 cells that express murine ecotropic receptor. For both vectors, permanent cell lines that produce viral stocks with titers of about 5 x 10(6) CFU/ml on mammalian cells can be easily established by passaging transfected avian cells. Some chimeric viruses, for example, RCASBP(Eco), replicate efficiently without modifications. For those chimeric viruses that do require modification, adaptation by passage in vitro or in vivo is a general strategy. This strategy has been used to prepare vectors with altered host range and could potentially be used to develop vectors that would be useful for targeted gene delivery.
Owolabi, Kolade M; Patidar, Kailash C
2016-01-01
In this paper, we consider the numerical simulations of an extended nonlinear form of Kierstead-Slobodkin reaction-transport system in one and two dimensions. We employ the popular fourth-order exponential time differencing Runge-Kutta (ETDRK4) schemes proposed by Cox and Matthew (J Comput Phys 176:430-455, 2002), that was modified by Kassam and Trefethen (SIAM J Sci Comput 26:1214-1233, 2005), for the time integration of spatially discretized partial differential equations. We demonstrate the supremacy of ETDRK4 over the existing exponential time differencing integrators that are of standard approaches and provide timings and error comparison. Numerical results obtained in this paper have granted further insight to the question 'What is the minimal size of the spatial domain so that the population persists?' posed by Kierstead and Slobodkin (J Mar Res 12:141-147, 1953), with a conclusive remark that the population size increases with the size of the domain. In attempt to examine the biological wave phenomena of the solutions, we present the numerical results in both one- and two-dimensional space, which have interesting ecological implications. Initial data and parameter values were chosen to mimic some existing patterns.
Tractography from HARDI using an intrinsic unscented Kalman filter.
Cheng, Guang; Salehian, Hesamoddin; Forder, John R; Vemuri, Baba C
2015-01-01
A novel adaptation of the unscented Kalman filter (UKF) was recently introduced in literature for simultaneous multitensor estimation and fiber tractography from diffusion MRI. This technique has the advantage over other tractography methods in terms of computational efficiency, due to the fact that the UKF simultaneously estimates the diffusion tensors and propagates the most consistent direction to track along. This UKF and its variants reported later in literature however are not intrinsic to the space of diffusion tensors. Lack of this key property can possibly lead to inaccuracies in the multitensor estimation as well as in the tractography. In this paper, we propose a novel intrinsic unscented Kalman filter (IUKF) in the space of diffusion tensors which are symmetric positive definite matrices, that can be used for simultaneous recursive estimation of multitensors and propagation of directional information for use in fiber tractography from diffusion weighted MR data. In addition to being more accurate, IUKF retains all the advantages of UKF mentioned above. We demonstrate the accuracy and effectiveness of the proposed method via experiments publicly available phantom data from the fiber cup-challenge (MICCAI 2009) and diffusion weighted MR scans acquired from human brains and rat spinal cords.
Applying Kalman filtering to investigate tropospheric effects in VLBI
NASA Astrophysics Data System (ADS)
Soja, Benedikt; Nilsson, Tobias; Karbon, Maria; Heinkelmann, Robert; Liu, Li; Lu, Cuixian; Andres Mora-Diaz, Julian; Raposo-Pulido, Virginia; Xu, Minghui; Schuh, Harald
2014-05-01
Very Long Baseline Interferometry (VLBI) currently provides results, e.g., estimates of the tropospheric delays, with a delay of more than two weeks. In the future, with the coming VLBI2010 Global Observing System (VGOS) and increased usage of electronic data transfer, it is planned that the time between observations and results is decreased. This may, for instance, allow the integration of VLBI-derived tropospheric delays into numerical weather prediction models. Therefore, future VLBI analysis software packages need to be able to process the observational data autonomously in near real-time. For this purpose, we have extended the Vienna VLBI Software (VieVS) by a Kalman filter module. This presentation describes the filter and discusses its application for tropospheric studies. Instead of estimating zenith wet delays as piece-wise linear functions in a least-squares adjustment, the Kalman filter allows for more sophisticated stochastic modeling. We start with a random walk process to model the time-dependent behavior of the zenith wet delays. Other possible approaches include the stochastic model described by turbulence theory, e.g. the model by Treuhaft and Lanyi (1987). Different variance-covariance matrices of the prediction error, depending on the time of the year and the geographic latitude, have been tested. In winter and closer to the poles, lower variances and covariances are appropriate. The horizontal variations in tropospheric delays have been investigated by comparing three different strategies: assumption of a horizontally stratified troposphere, using north and south gradients modeled, e.g., as Gauss-Markov processes, and applying a turbulence model assuming correlations between observations in different azimuths. By conducting Monte-Carlo simulations of current standard VLBI networks and of future VGOS networks, the different tropospheric modeling strategies are investigated. For this purpose, we use the simulator module of VieVS which takes into
Ensemble Kalman filtering with residual nudging: some recent progress
NASA Astrophysics Data System (ADS)
Luo, Xiaodong; Hoteit, Ibrahim
2014-05-01
The ensemble Kalman filter (EnKF) is an efficient algorithm for many data assimilation problems. In certain circumstances, however, divergence of the EnKF might be spotted. In previous studies (Luo and Hoteit, 2012, 2013b,a), the authors proposed an observation-space based strategy, called residual nudging, in order to improve the stability of the EnKF when dealing with linear observation operators. The main idea behind residual nudging is to monitor, and if necessary, adjust the distances (misfits) between the real observations and the simulated ones of the state estimates, in the hope that by doing so one may be able to obtain better estimation accuracy. In a more present study (Luo and Hoteit, 2014), residual nudging is extended and modified in order to handle nonlinear observation operators. Such extension and modification result in an iterative filtering framework that is able to achieve the objective of residual nudging in the presence of nonlinear observation operators, under suitable conditions. The 40 dimensional Lorenz 96 model is used to illustrate the performance of the iterative filter. Numerical results show that, while a normal EnKF may diverge in the presence of nonlinear observation operators, the proposed iterative filter performs very stably and leads to reasonable estimation accuracies under various experiment settings. REFERENCES Luo, X. and H. Hoteit, 2014: Ensemble kalman filtering with residual nudging: an extension to the state estimation problems with nonlinear observation operators. Submitted. Luo, X. and I. Hoteit, 2012: Ensemble Kalman filtering with residual nudging. Tellus A, 64, 17130, open access, doi:10.3402/tellusa.v64i0.17130. Luo, X. and I. Hoteit, 2013a: Covariance inflation in the ensemble Kalman filter: a residual nudging perspective and some implications. Mon. Wea. Rev., 141, 3360-3368, doi:10.1175/MWR-D-13-00067.1. Luo, X. and I. Hoteit, 2013b: Efficient particle filtering through residual nudging. Quart. J. Roy. Meteor
Kalman Orbit Optimized Loop Tracking
NASA Technical Reports Server (NTRS)
Young, Lawrence E.; Meehan, Thomas K.
2011-01-01
Under certain conditions of low signal power and/or high noise, there is insufficient signal to noise ratio (SNR) to close tracking loops with individual signals on orbiting Global Navigation Satellite System (GNSS) receivers. In addition, the processing power available from flight computers is not great enough to implement a conventional ultra-tight coupling tracking loop. This work provides a method to track GNSS signals at very low SNR without the penalty of requiring very high processor throughput to calculate the loop parameters. The Kalman Orbit-Optimized Loop (KOOL) tracking approach constitutes a filter with a dynamic model and using the aggregate of information from all tracked GNSS signals to close the tracking loop for each signal. For applications where there is not a good dynamic model, such as very low orbits where atmospheric drag models may not be adequate to achieve the required accuracy, aiding from an IMU (inertial measurement unit) or other sensor will be added. The KOOL approach is based on research JPL has done to allow signal recovery from weak and scintillating signals observed during the use of GPS signals for limb sounding of the Earth s atmosphere. That approach uses the onboard PVT (position, velocity, time) solution to generate predictions for the range, range rate, and acceleration of the low-SNR signal. The low- SNR signal data are captured by a directed open loop. KOOL builds on the previous open loop tracking by including feedback and observable generation from the weak-signal channels so that the MSR receiver will continue to track and provide PVT, range, and Doppler data, even when all channels have low SNR.
Kalman Filtering USNO's GPS Observations for Improved Time Transfer Predictions
NASA Technical Reports Server (NTRS)
Hutsell, Steven T.
1996-01-01
The Global Positioning System (GPS) Master Control Station (MCS) performs the Coordinated Universal Time (UTC) time transfer mission by uploading and broadcasting predictions of the GPS-UTC offset in subframe 4 of the GS navigation message. These predictions are based on only two successive daily data points obtained from the US Naval Observatory (USNO). USNO produces these daily smoothed data points by performing a least-squares fit on roughly 38 hours worth of data from roughly 160 successive 13-minute tracks of GPS satellites. Though sufficient for helping to maintain a time transfer error specification of 28 ns (1 Sigma), the MCS's prediction algorithm does not make the best use of the available data from from USNO, and produces data that can degrade quickly over extended prediction spans. This paper investigates how, by applying Kalman filtering to the same available tracking data, the MCS could improve its estimate of GPS-UTC, and in particular, the GPS-UTC A(sub 1) term. By refining the A(sub 1) (frequency) estimate for GPS-UTC predictions, error in GPS time transfer could drop significantly. Additional, the risk of future spikes in GPS's time transfer error could similarly be minimized, by employing robust Kalman filtering for GPS-UTC predictions.
Hettiarachchi, Imali T; Mohamed, Shady; Nyhof, Luke; Nahavandi, Saeid
2013-01-01
Recently effective connectivity studies have gained significant attention among the neuroscience community as Electroencephalography (EEG) data with a high time resolution can give us a wider understanding of the information flow within the brain. Among other tools used in effective connectivity analysis Granger Causality (GC) has found a prominent place. The GC analysis, based on strictly causal multivariate autoregressive (MVAR) models does not account for the instantaneous interactions among the sources. If instantaneous interactions are present, GC based on strictly causal MVAR will lead to erroneous conclusions on the underlying information flow. Thus, the work presented in this paper applies an extended MVAR (eMVAR) model that accounts for the zero lag interactions. We propose a constrained adaptive Kalman filter (CAKF) approach for the eMVAR model identification and demonstrate that this approach performs better than the short time windowing-based adaptive estimation when applied to information flow analysis.
Air-to-Air Missile Enhanced Scoring with Kalman Smoothing
2012-03-01
Paulo Henriques Iscold Andrade de Oliveira, and Luis Antonio Aguirre . “Flight Path Reconstruc- tion Using the Unscented Kalman Filter Algorithm...Reconstruction Using The Unscented Kalman Filter Algorithm. Teixeira, Torres, Andrade de Oliveira, and Aguirre propose the use of an Un- scented Kalman
Fuzzy adaptive interacting multiple model nonlinear filter for integrated navigation sensor fusion.
Tseng, Chien-Hao; Chang, Chih-Wen; Jwo, Dah-Jing
2011-01-01
In this paper, the application of the fuzzy interacting multiple model unscented Kalman filter (FUZZY-IMMUKF) approach to integrated navigation processing for the maneuvering vehicle is presented. The unscented Kalman filter (UKF) employs a set of sigma points through deterministic sampling, such that a linearization process is not necessary, and therefore the errors caused by linearization as in the traditional extended Kalman filter (EKF) can be avoided. The nonlinear filters naturally suffer, to some extent, the same problem as the EKF for which the uncertainty of the process noise and measurement noise will degrade the performance. As a structural adaptation (model switching) mechanism, the interacting multiple model (IMM), which describes a set of switching models, can be utilized for determining the adequate value of process noise covariance. The fuzzy logic adaptive system (FLAS) is employed to determine the lower and upper bounds of the system noise through the fuzzy inference system (FIS). The resulting sensor fusion strategy can efficiently deal with the nonlinear problem for the vehicle navigation. The proposed FUZZY-IMMUKF algorithm shows remarkable improvement in the navigation estimation accuracy as compared to the relatively conventional approaches such as the UKF and IMMUKF.
Likelihood Methods for Adaptive Filtering and Smoothing. Technical Report #455.
ERIC Educational Resources Information Center
Butler, Ronald W.
The dynamic linear model or Kalman filtering model provides a useful methodology for predicting the past, present, and future states of a dynamic system, such as an object in motion or an economic or social indicator that is changing systematically with time. Recursive likelihood methods for adaptive Kalman filtering and smoothing are developed.…
Kalman filter estimation model in flood forecasting
NASA Astrophysics Data System (ADS)
Husain, Tahir
Elementary precipitation and runoff estimation problems associated with hydrologic data collection networks are formulated in conjunction with the Kalman Filter Estimation Model. Examples involve the estimation of runoff using data from a single precipitation station and also from a number of precipitation stations. The formulations demonstrate the role of state-space, measurement, and estimation equations of the Kalman Filter Model in flood forecasting. To facilitate the formulation, the unit hydrograph concept and antecedent precipitation index is adopted in the estimation model. The methodology is then applied to estimate various flood events in the Carnation Creek of British Columbia.
The use of the Kalman filter in the automated segmentation of EIT lung images.
Zifan, A; Liatsis, P; Chapman, B E
2013-06-01
In this paper, we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using electrical impedance tomography (EIT). EIT is an emerging, promising, non-invasive imaging modality that produces real-time, low spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a nonlinear ill-posed inverse problem, therefore the problem is usually linearized, which produces impedance-change images, rather than static impedance ones. Such images are highly blurry and fuzzy along object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed with augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle. The proposed method has been validated by using performance statistics such as misclassified area, and false positive rate, and compared to previous approaches. The results show that the proposed automated method can be a fast and reliable segmentation tool for EIT imaging.
The Unscented Kalman Filter estimates the plasma insulin from glucose measurement.
Eberle, Claudia; Ament, Christoph
2011-01-01
Understanding the simultaneous interaction within the glucose and insulin homeostasis in real-time is very important for clinical treatment as well as for research issues. Until now only plasma glucose concentrations can be measured in real-time. To support a secure, effective and rapid treatment e.g. of diabetes a real-time estimation of plasma insulin would be of great value. A novel approach using an Unscented Kalman Filter that provides an estimate of the current plasma insulin concentration is presented, which operates on the measurement of the plasma glucose and Bergman's Minimal Model of the glucose insulin homeostasis. We can prove that process observability is obtained in this case. Hence, a successful estimator design is possible. Since the process is nonlinear we have to consider estimates that are not normally distributed. The symmetric Unscented Kalman Filter (UKF) will perform best compared to other estimator approaches as the Extended Kalman Filter (EKF), the simplex Unscented Kalman Filter (UKF), and the Particle Filter (PF). The symmetric UKF algorithm is applied to the plasma insulin estimation. It shows better results compared to the direct (open loop) estimation that uses a model of the insulin subsystem.
Hanson, Jeremy M.; De Bono, Bryan; Levesque, Jeffrey P.; Mauel, Michael E.; Maurer, David A.; Navratil, Gerald A.; Pedersen, Thomas Sunn; Shiraki, Daisuke; James, Royce W.
2009-05-15
The simulation and experimental optimization of a Kalman filter feedback control algorithm for n=1 tokamak external kink modes are reported. In order to achieve the highest plasma pressure limits in ITER, resistive wall mode stabilization is required [T. C. Hender et al., Nucl. Fusion 47, S128 (2007)] and feedback algorithms will need to distinguish the mode from noise due to other magnetohydrodynamic activity. The Kalman filter contains an internal model that captures the dynamics of a rotating, growing n=1 mode. This model is actively compared with real-time measurements to produce an optimal estimate for the mode's amplitude and phase. On the High Beta Tokamak-Extended Pulse experiment [T. H. Ivers et al., Phys. Plasmas 3, 1926 (1996)], the Kalman filter algorithm is implemented using a set of digital, field-programmable gate array controllers with 10 {mu}s latencies. Signals from an array of 20 poloidal sensor coils are used to measure the n=1 mode, and the feedback control is applied using 40 poloidally and toroidally localized control coils. The feedback system with the Kalman filter is able to suppress the external kink mode over a broad range of phase angles between the sensed mode and applied control field. Scans of filter parameters show good agreement between simulation and experiment, and feedback suppression and excitation of the kink mode are enhanced in experiments when a filter made using optimal parameters from the scans is used.
Nonlinear Kalman Filtering for acoustic emission source localization in anisotropic panels.
Dehghan Niri, E; Farhidzadeh, A; Salamone, S
2014-02-01
Nonlinear Kalman Filtering is an established field in applied probability and control systems, which plays an important role in many practical applications from target tracking to weather and climate prediction. However, its application for acoustic emission (AE) source localization has been very limited. In this paper, two well-known nonlinear Kalman Filtering algorithms are presented to estimate the location of AE sources in anisotropic panels: the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). These algorithms are applied to two cases: velocity profile known (CASE I) and velocity profile unknown (CASE II). The algorithms are compared with a more traditional nonlinear least squares method. Experimental tests are carried out on a carbon-fiber reinforced polymer (CFRP) composite panel instrumented with a sparse array of piezoelectric transducers to validate the proposed approaches. AE sources are simulated using an instrumented miniature impulse hammer. In order to evaluate the performance of the algorithms, two metrics are used: (1) accuracy of the AE source localization and (2) computational cost. Furthermore, it is shown that both EKF and UKF can provide a confidence interval of the estimated AE source location and can account for uncertainty in time of flight measurements.
Correlation-based tracking using tunable training and Kalman prediction
NASA Astrophysics Data System (ADS)
Ontiveros-Gallardo, Sergio E.; Kober, Vitaly
2016-09-01
Tracking solves the problem of detecting and estimating the future target state in an input video sequence. In this work, an adaptive tracking algorithm by means of multiple object detections in reduced frame areas with a tunable bank of correlation filters is proposed. Prediction of the target state is carried out with the Kalman filtering. It helps us to estimate the target state, to reduce the search area in the next frame, and to solve the occlusion problem. The bank of composite filters is updated frame by frame with tolerance to different recent viewpoint and scale changes of the target. The performance of the proposed algorithm with the help of computer simulation is evaluated in terms of detection and location errors.
Adaptive Filtering for Large Space Structures: A Closed-Form Solution
NASA Technical Reports Server (NTRS)
Rauch, H. E.; Schaechter, D. B.
1985-01-01
In a previous paper Schaechter proposes using an extended Kalman filter to estimate adaptively the (slowly varying) frequencies and damping ratios of a large space structure. The time varying gains for estimating the frequencies and damping ratios can be determined in closed form so it is not necessary to integrate the matrix Riccati equations. After certain approximations, the time varying adaptive gain can be written as the product of a constant matrix times a matrix derived from the components of the estimated state vector. This is an important savings of computer resources and allows the adaptive filter to be implemented with approximately the same effort as the nonadaptive filter. The success of this new approach for adaptive filtering was demonstrated using synthetic data from a two mode system.
Discrete-time adaptive backstepping nonlinear control via high-order neural networks.
Alanis, Alma Y; Sanchez, Edgar N; Loukianov, Alexander G
2007-07-01
This paper deals with adaptive tracking for discrete-time multiple-input-multiple-output (MIMO) nonlinear systems in presence of bounded disturbances. In this paper, a high-order neural network (HONN) structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF). This paper also includes the respective stability analysis, on the basis of the Lyapunov approach, for the whole controlled system, including the extended Kalman filter (EKF)-based NN learning algorithm. Applicability of the scheme is illustrated via simulation for a discrete-time nonlinear model of an electric induction motor.
Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman
2017-03-01
A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies.
Kalman Filter for Mass Property and Thrust Identification (MMS)
NASA Technical Reports Server (NTRS)
Queen, Steven
2015-01-01
The Magnetospheric Multiscale (MMS) mission consists of four identically instrumented, spin-stabilized observatories, elliptically orbiting the Earth in a tetrahedron formation. For the operational success of the mission, on-board systems must be able to deliver high-precision orbital adjustment maneuvers. On MMS, this is accomplished using feedback from on-board star sensors in tandem with accelerometers whose measurements are dynamically corrected for errors associated with a spinning platform. In order to determine the required corrections to the measured acceleration, precise estimates of attitude, rate, and mass-properties is necessary. To this end, both an on-board and ground-based Multiplicative Extended Kalman Filter (MEKF) were formulated and implemented in order to estimate the dynamic and quasi-static properties of the spacecraft.
Adaptive control of Space Station with control moment gyros
NASA Technical Reports Server (NTRS)
Bishop, Robert H.; Paynter, Scott J.; Sunkel, John W.
1992-01-01
An adaptive approach to Space Station attitude control is investigated. The main components of the controller are the parameter identification scheme, the control gain calculation, and the control law. The control law is a full-state feedback space station baseline control law. The control gain calculation is based on linear-quadratic regulator theory with eigenvalues placement in a vertical strip. The parameter identification scheme is a recursive extended Kalman filter that estimates the inertias and also provides an estimate of the unmodeled disturbances due to the aerodynamic torques and to the nonlinear effects. An analysis of the inertia estimation problem suggests that it is possible to estimate Space Station inertias accurately during nominal control moment gyro operations. The closed-loop adaptive control law is shown to be capable of stabilizing the Space Station after large inertia changes. Results are presented for the pitch axis.
Adaptive Wavefront Calibration and Control for the Gemini Planet Imager
Poyneer, L A; Veran, J
2007-02-02
Quasi-static errors in the science leg and internal AO flexure will be corrected. Wavefront control will adapt to current atmospheric conditions through Fourier modal gain optimization, or the prediction of atmospheric layers with Kalman filtering.
A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation.
Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao
2016-12-19
The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms.
Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks.
Puskorius, G V; Feldkamp, L A
1994-01-01
Although the potential of the powerful mapping and representational capabilities of recurrent network architectures is generally recognized by the neural network research community, recurrent neural networks have not been widely used for the control of nonlinear dynamical systems, possibly due to the relative ineffectiveness of simple gradient descent training algorithms. Developments in the use of parameter-based extended Kalman filter algorithms for training recurrent networks may provide a mechanism by which these architectures will prove to be of practical value. This paper presents a decoupled extended Kalman filter (DEKF) algorithm for training of recurrent networks with special emphasis on application to control problems. We demonstrate in simulation the application of the DEKF algorithm to a series of example control problems ranging from the well-known cart-pole and bioreactor benchmark problems to an automotive subsystem, engine idle speed control. These simulations suggest that recurrent controller networks trained by Kalman filter methods can combine the traditional features of state-space controllers and observers in a homogeneous architecture for nonlinear dynamical systems, while simultaneously exhibiting less sensitivity than do purely feedforward controller networks to changes in plant parameters and measurement noise.
Kalman filtering for spacecraft attitude estimation
NASA Technical Reports Server (NTRS)
Lefferts, E. J.; Markley, F. L.; Shuster, M. D.
1982-01-01
Several schemes in current use for sequential estimation of spacecraft attitude using Kalman filters are examined. These differ according to their treatment of the attitude error, namely: using the complete four-component quaternion; using a truncated quaternion in which one of the components has been eliminated; or using a quaternion referred to approximate body-fixed axes. These schemes are examined for the case of a spacecraft carrying line-of-sight attitude sensors and three-axis gyros whose measurements are corrupted by noise on both the drift rate and the drift-rate ramp. The analysis of the covariance is carried out in detail. The historical development of Kalman filtering of attitude is reviewed.
An algorithmic approach to adaptive state filtering using recurrent neural networks.
Parlos, A G; Menon, S K; Atiya, A
2001-01-01
Practical algorithms are presented for adaptive state filtering in nonlinear dynamic systems when the state equations are unknown. The state equations are constructively approximated using neural networks. The algorithms presented are based on the two-step prediction-update approach of the Kalman filter. The proposed algorithms make minimal assumptions regarding the underlying nonlinear dynamics and their noise statistics. Non-adaptive and adaptive state filtering algorithms are presented with both off-line and online learning stages. The algorithms are implemented using feedforward and recurrent neural network and comparisons are presented. Furthermore, extended Kalman filters (EKFs) are developed and compared to the filter algorithms proposed. For one of the case studies, the EKF converges but results in higher state estimation errors that the equivalent neural filters. For another, more complex case study with unknown system dynamics and noise statistics, the developed EKFs do not converge. The off-line trained neural state filters converge quite rapidly and exhibit acceptable performance. Online training further enhances the estimation accuracy of the developed adaptive filters, effectively decoupling the eventual filter accuracy from the accuracy of the process model.
Aircraft Turbofan Engine Health Estimation Using Constrained Kalman Filtering
NASA Technical Reports Server (NTRS)
Simon, Dan; Simon, Donald L.
2003-01-01
Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This paper develops an analytic method of incorporating state variable inequality constraints in the Kalman filter. The resultant filter is a combination of a standard Kalman filter and a quadratic programming problem. The incorporation of state variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is proven theoretically and shown via simulation results obtained from application to a turbofan engine model. This model contains 16 state variables, 12 measurements, and 8 component health parameters. It is shown that the new algorithms provide improved performance in this example over unconstrained Kalman filtering.
Applications of Kalman filtering to real-time trace gas concentration measurements
NASA Technical Reports Server (NTRS)
Leleux, D. P.; Claps, R.; Chen, W.; Tittel, F. K.; Harman, T. L.
2002-01-01
A Kalman filtering technique is applied to the simultaneous detection of NH3 and CO2 with a diode-laser-based sensor operating at 1.53 micrometers. This technique is developed for improving the sensitivity and precision of trace gas concentration levels based on direct overtone laser absorption spectroscopy in the presence of various sensor noise sources. Filter performance is demonstrated to be adaptive to real-time noise and data statistics. Additionally, filter operation is successfully performed with dynamic ranges differing by three orders of magnitude. Details of Kalman filter theory applied to the acquired spectroscopic data are discussed. The effectiveness of this technique is evaluated by performing NH3 and CO2 concentration measurements and utilizing it to monitor varying ammonia and carbon dioxide levels in a bioreactor for water reprocessing, located at the NASA-Johnson Space Center. Results indicate a sensitivity enhancement of six times, in terms of improved minimum detectable absorption by the gas sensor.
Feedback Robust Cubature Kalman Filter for Target Tracking Using an Angle Sensor.
Wu, Hao; Chen, Shuxin; Yang, Binfeng; Chen, Kun
2016-05-09
The direction of arrival (DOA) tracking problem based on an angle sensor is an important topic in many fields. In this paper, a nonlinear filter named the feedback M-estimation based robust cubature Kalman filter (FMR-CKF) is proposed to deal with measurement outliers from the angle sensor. The filter designs a new equivalent weight function with the Mahalanobis distance to combine the cubature Kalman filter (CKF) with the M-estimation method. Moreover, by embedding a feedback strategy which consists of a splitting and merging procedure, the proper sub-filter (the standard CKF or the robust CKF) can be chosen in each time index. Hence, the probability of the outliers' misjudgment can be reduced. Numerical experiments show that the FMR-CKF performs better than the CKF and conventional robust filters in terms of accuracy and robustness with good computational efficiency. Additionally, the filter can be extended to the nonlinear applications using other types of sensors.
Three-axis attitude determination via Kalman filtering of magnetometer data
NASA Technical Reports Server (NTRS)
Martel, Francois; Pal, Parimal K.; Psiaki, Mark L.
1988-01-01
A three-axis Magnetometer/Kalman Filter attitude determination system for a spacecraft in low-altitude Earth orbit is developed, analyzed, and simulation tested. The motivation for developing this system is to achieve light weight and low cost for an attitude determination system. The extended Kalman filter estimates the attitude, attitude rates, and constant disturbance torques. Accuracy near that of the International Geomagnetic Reference Field model is achieved. Covariance computation and simulation testing demonstrate the filter's accuracy. One test case, a gravity-gradient stabilized spacecraft with a pitch momentum wheel and a magnetically-anchored damper, is a real satellite on which this attitude determination system will be used. The application to a nadir pointing satellite and the estimation of disturbance torques represent the significant extensions contributed by this paper. Beyond its usefulness purely for attitude determination, this system could be used as part of a low-cost three-axis attitude stabilization system.
In-situ estimation of MOCVD growth rate via a modified Kalman filter
Woo, W.W.; Svoronos, S.A.; Sankur, H.O.; Bajaj, J.; Irvine, S.J.C.
1996-05-01
In-situ laser reflectance monitoring of metal-organic chemical vapor deposition (MOCVD) is an effective way to monitor growth rate and epitaxial layer thickness of a variety of III-V and II-VI semiconductors. Materials with low optical extinction coefficients, such as ZnTe/GaAs and AlAs/GaAs for a 6,328 {angstrom} HeNe laser, are ideal for such an application. An extended Kalman filter modified to include a variable forgetting factor was applied to the MOCVD systems. The filter was able to accurately estimate thickness and growth rate while filtering out process noise and cope with sudden changes in growth rate, reflectance drift, and bias. Due to the forgetting factor, the Kalman filter was successful, even when based on very simple process models.
NASA Technical Reports Server (NTRS)
Womble, M. E.; Potter, J. E.
1975-01-01
A prefiltering version of the Kalman filter is derived for both discrete and continuous measurements. The derivation consists of determining a single discrete measurement that is equivalent to either a time segment of continuous measurements or a set of discrete measurements. This prefiltering version of the Kalman filter easily handles numerical problems associated with rapid transients and ill-conditioned Riccati matrices. Therefore, the derived technique for extrapolating the Riccati matrix from one time to the next constitutes a new set of integration formulas which alleviate ill-conditioning problems associated with continuous Riccati equations. Furthermore, since a time segment of continuous measurements is converted into a single discrete measurement, Potter's square root formulas can be used to update the state estimate and its error covariance matrix. Therefore, if having the state estimate and its error covariance matrix at discrete times is acceptable, the prefilter extends square root filtering with all its advantages, to continuous measurement problems.
A Kalman Filtering Perspective for Multiatlas Segmentation.
Gao, Yi; Zhu, Liangjia; Cates, Joshua; MacLeod, Rob S; Bouix, Sylvain; Tannenbaum, Allen
In multiatlas segmentation, one typically registers several atlases to the novel image, and their respective segmented label images are transformed and fused to form the final segmentation. In this work, we provide a new dynamical system perspective for multiatlas segmentation, inspired by the following fact: The transformation that aligns the current atlas to the novel image can be not only computed by direct registration but also inferred from the transformation that aligns the previous atlas to the image together with the transformation between the two atlases. This process is similar to the global positioning system on a vehicle, which gets position by inquiring from the satellite and by employing the previous location and velocity-neither answer in isolation being perfect. To solve this problem, a dynamical system scheme is crucial to combine the two pieces of information; for example, a Kalman filtering scheme is used. Accordingly, in this work, a Kalman multiatlas segmentation is proposed to stabilize the global/affine registration step. The contributions of this work are twofold. First, it provides a new dynamical systematic perspective for standard independent multiatlas registrations, and it is solved by Kalman filtering. Second, with very little extra computation, it can be combined with most existing multiatlas segmentation schemes for better registration/segmentation accuracy.
Application of Kalman Filter on Multisensor Fusion Tracking
1992-12-01
AD-A257 335 NAVAL POSTGRADUATE SCHOOL Monterey, California DTIC ELCT THESIS APPLICATION OF KALMAN FILTER ON MULTISENSOR FUSION TRACKING by Brian...WORK UNIT ELEMENT NO NO NO ACCESSION NO 11 TITLE (Include Security Classification) PPLICATION OF KALMAN FILTER ON MULTISENSOR FUSION TRACKING 12...FIELD GROUP SUB-GROUP fusion, Kalman Filter Multisensor Fusion Tracking 19 ABSTRACT (Continue on reverse if necessary and identify by block number)he
An adaptive additive inflation scheme for Ensemble Kalman Filters
NASA Astrophysics Data System (ADS)
Sommer, Matthias; Janjic, Tijana
2016-04-01
Data assimilation for atmospheric dynamics requires an accurate estimate for the uncertainty of the forecast in order to obtain an optimal combination with available observations. This uncertainty has two components, firstly the uncertainty which originates in the the initial condition of that forecast itself and secondly the error of the numerical model used. While the former can be approximated quite successfully with an ensemble of forecasts (an additional sampling error will occur), little is known about the latter. For ensemble data assimilation, ad-hoc methods to address model error include multiplicative and additive inflation schemes, possibly also flow-dependent. The additive schemes rely on samples for the model error e.g. from short-term forecast tendencies or differences of forecasts with varying resolutions. However since these methods work in ensemble space (i.e. act directly on the ensemble perturbations) the sampling error is fixed and can be expected to affect the skill substiantially. In this contribution we show how inflation can be generalized to take into account more degrees of freedom and what improvements for future operational ensemble data assimilation can be expected from this, also in comparison with other inflation schemes.
An adaptive ensemble Kalman filter for soil moisture data assimilation
Technology Transfer Automated Retrieval System (TEKTRAN)
In a 19-year twin experiment for the Red-Arkansas river basin we assimilate synthetic surface soil moisture retrievals into the NASA Catchment land surface model. We demonstrate how poorly specified model and observation error parameters affect the quality of the assimilation products. In particul...
Adaptive control of Space Station during nominal operations with CMGs. [Control Moment Gyroscopes
NASA Technical Reports Server (NTRS)
Bishop, R. H.; Paynter, S. J.; Sunkel, J. W.
1991-01-01
An adaptive control approach is investigated for the Space Station. The main components of the adaptive controller are the parameter identification scheme, the control gain calculation, and the control law. The control law is the Space Station baseline control law. The control gain calculation is based on linear quadratic regulator theory with eigenvalue placement in a vertical strip. The parameter identification scheme is a real-time recursive extended Kalman filter which estimates the inertias and also provides an estimate of the unmodeled disturbances due to the aerodynamic torques and to the nonlinear effects. An analysis of the inertia estimation problem suggests that it is possible to compute accurate estimates of the Space Station inertias during nominal CMG (control moment gyro) operations. The closed-loop adaptive control law is shown to be capable of stabilizing the Space Station after large inertia changes. Results are presented for the pitch axis.
Longitudinal Factor Score Estimation Using the Kalman Filter.
ERIC Educational Resources Information Center
Oud, Johan H.; And Others
1990-01-01
How longitudinal factor score estimation--the estimation of the evolution of factor scores for individual examinees over time--can profit from the Kalman filter technique is described. The Kalman estimates change more cautiously over time, have lower estimation error variances, and reproduce the LISREL program latent state correlations more…
NASA Astrophysics Data System (ADS)
Soja, Benedikt; Nilsson, Tobias; Balidakis, Kyriakos; Glaser, Susanne; Heinkelmann, Robert; Schuh, Harald
2016-12-01
Terrestrial reference frames (TRF), such as the ITRF2008, are primary products of geodesy. In this paper, we present TRF solutions based on Kalman filtering of very long baseline interferometry (VLBI) data, for which we estimate steady station coordinates over more than 30 years that are updated for every single VLBI session. By applying different levels of process noise, non-linear signals, such as seasonal and seismic effects, are taken into account. The corresponding stochastic model is derived site-dependent from geophysical loading deformation time series and is adapted during periods of post-seismic deformations. Our results demonstrate that the choice of stochastic process has a much smaller impact on the coordinate time series and velocities than the overall noise level. If process noise is applied, tests with and without additionally estimating seasonal signals indicate no difference between the resulting coordinate time series for periods when observational data are available. In a comparison with epoch reference frames, the Kalman filter solutions provide better short-term stability. Furthermore, we find out that the Kalman filter solutions are of similar quality when compared to a consistent least-squares solution, however, with the enhanced attribute of being easier to update as, for instance, in a post-earthquake period.
Incremental activation detection for real-time fMRI series using robust Kalman filter.
Li, Liang; Yan, Bin; Tong, Li; Wang, Linyuan; Li, Jianxin
2014-01-01
Real-time functional magnetic resonance imaging (rt-fMRI) is a technique that enables us to observe human brain activations in real time. However, some unexpected noises that emerged in fMRI data collecting, such as acute swallowing, head moving and human manipulations, will cause much confusion and unrobustness for the activation analysis. In this paper, a new activation detection method for rt-fMRI data is proposed based on robust Kalman filter. The idea is to add a variation to the extended kalman filter to handle the additional sparse measurement noise and a sparse noise term to the measurement update step. Hence, the robust Kalman filter is designed to improve the robustness for the outliers and can be computed separately for each voxel. The algorithm can compute activation maps on each scan within a repetition time, which meets the requirement for real-time analysis. Experimental results show that this new algorithm can bring out high performance in robustness and in real-time activation detection.
A new extension of unscented Kalman filter for structural health assessment with unknown input
NASA Astrophysics Data System (ADS)
Al-Hussein, Abdullah; Haldar, Achintya
2014-03-01
A time-domain nonlinear system identification (SI)-based structural health assessment (SHA) procedure, using Unscented Kalman Filter (UKF) concept, is presented in this paper. It is a two-stage procedure. It integrates an iterative least squares technique and the unscented Kalman filter concept. The authors believe that the integrated procedure significantly improves the basic UKF concept. The procedure can assess the health of a structure using only a limited number of noise-contaminated acceleration time-histories measured only at a small part of a structure and does not need information on input excitation. The structures are represented by finite element models and the location and severity of defect(s) are assessed by tracking the changes in the stiffness properties of individual elements from their expected values. With the help of examples, it is demonstrated that the method is capable of accurately identifying defect-free and defective states of structures. Small and relatively large defects are introduced at different locations in the structure and the capability of the method to detect the health of the structure is examined. It is demonstrated that the accuracy of the method is much better than the other methods currently available for the structural health assessment. It is also superior to the extended Kalman filter. Considering the accuracy and robustness, the procedure can be used as a nondestructive structural health assessment procedure.
Kalman-variant estimators for state of charge in lithium-sulfur batteries
NASA Astrophysics Data System (ADS)
Propp, Karsten; Auger, Daniel J.; Fotouhi, Abbas; Longo, Stefano; Knap, Vaclav
2017-03-01
Lithium-sulfur batteries are now commercially available, offering high specific energy density, low production costs and high safety. However, there is no commercially-available battery management system for them, and there are no published methods for determining state of charge in situ. This paper describes a study to address this gap. The properties and behaviours of lithium-sulfur are briefly introduced, and the applicability of 'standard' lithium-ion state-of-charge estimation methods is explored. Open-circuit voltage methods and 'Coulomb counting' are found to have a poor fit for lithium-sulfur, and model-based methods, particularly recursive Bayesian filters, are identified as showing strong promise. Three recursive Bayesian filters are implemented: an extended Kalman filter (EKF), an unscented Kalman filter (UKF) and a particle filter (PF). These estimators are tested through practical experimentation, considering both a pulse-discharge test and a test based on the New European Driving Cycle (NEDC). Experimentation is carried out at a constant temperature, mirroring the environment expected in the authors' target automotive application. It is shown that the estimators, which are based on a relatively simple equivalent-circuit-network model, can deliver useful results. If the three estimators implemented, the unscented Kalman filter gives the most robust and accurate performance, with an acceptable computational effort.
Schmidt-Kalman Filter with Polynomial Chaos Expansion for Orbit Determination of Space Objects
NASA Astrophysics Data System (ADS)
Yang, Y.; Cai, H.; Zhang, K.
2016-09-01
Parameter errors in orbital models can result in poor orbit determination (OD) using a traditional Kalman filter. One approach to account for these errors is to consider them in the so-called Schmidt-Kalman filter (SKF), by augmenting the state covariance matrix (CM) with additional parameter covariance rather than additively estimating these so-called "consider" parameters. This paper introduces a new SKF algorithm with polynomial chaos expansion (PCE-SKF). The PCE approach has been proved to be more efficient than Monte Carlo method for propagating the input uncertainties onto the system response without experiencing any constraints of linear dynamics, or Gaussian distributions of the uncertainty sources. The state and covariance needed in the orbit prediction step are propagated using PCE. An inclined geosynchronous orbit scenario is set up to test the proposed PCE-SKF based OD algorithm. The satellite orbit is propagated based on numerical integration, with the uncertain coefficient of solar radiation pressure considered. The PCE-SKF solutions are compared with extended Kalman filter (EKF), SKF and PCE-EKF (EKF with PCE) solutions. It is implied that the covariance propagation using PCE leads to more precise OD solutions in comparison with those based on linear propagation of covariance.
Kalman Filtering Approach to Blind Equalization
1993-12-01
Update weighing probabilities (3.11) vi. Compute one step predictions p(j..Nb I X’) S(tlp (D,.,,,IX,) B. ADMISSIBILITY OF THE BLIND EQUALIZATION BY...NAVAL POSTGRADUATE SCHOOL Monterey, California •GR AD13 DTIC 94-07381 AR 0C199 THESIS S 0 LECTE4u KALMAN FILTERING APPROACH TO BLIND EQUALIZATION by...FILTERING APPROACH 5. FUNDING NUMBERS TO BLIND EQUALIZATION S. AUTHOR(S) Mehmet Kutlu 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) S
Fixed Point Implementations of Fast Kalman Algorithms.
1983-11-01
fined point multiply. ve &geete a meatn ’C.- nero. variance N random vector s~t) each time weAfilter is said to be 12 Scaled if udae 8(t+11t0 - 3-1* AS...nl.v by bl ’k rn.b.) 20 AST iA C T ’Cnnin to .- a , o. a ide It .,oco ea ry and Idenuty by block number) In this paper we study scaling rules and round...realized in a -fast form that uses the so-called fast Kalman gain algorithm. The algorithm for the gain is fixed point. Scaling rules and expressions for
Multiscale Systems, Kalman Filters, and Riccati Equations
2006-01-01
Rauch -Tung-Striebel algorithm- consisting of a fine-to-coarse Kalman-filter-like sweep followed by a coarse-to-fine smoothing step- was developed. In...MODELS 5 wv(t) = w(t) - E[w(t)lx(t)] (2.9) E[w(t)ifT(t)] = I- B T(t)P -l(t)B(t) - (t) (2.10) In [1] we derive a generalization of the Rauch -Tung...particular, this framework leads to an extremely efficient and highly parallelizable scale-recursive optimal estimation algorithm generalizing the Rauch
NASA Astrophysics Data System (ADS)
Li, Judith Yue; Kokkinaki, Amalia; Ghorbanidehno, Hojat; Darve, Eric F.; Kitanidis, Peter K.
2015-12-01
Reservoir monitoring aims to provide snapshots of reservoir conditions and their uncertainties to assist operation management and risk analysis. These snapshots may contain millions of state variables, e.g., pressures and saturations, which can be estimated by assimilating data in real time using the Kalman filter (KF). However, the KF has a computational cost that scales quadratically with the number of unknowns, m, due to the cost of computing and storing the covariance and Jacobian matrices, along with their products. The compressed state Kalman filter (CSKF) adapts the KF for solving large-scale monitoring problems. The CSKF uses N preselected orthogonal bases to compute an accurate rank-N approximation of the covariance that is close to the optimal spectral approximation given by SVD. The CSKF has a computational cost that scales linearly in m and uses an efficient matrix-free approach that propagates uncertainties using N + 1 forward model evaluations, where N≪m. Here we present a generalized CSKF algorithm for nonlinear state estimation problems such as CO2 monitoring. For simultaneous estimation of multiple types of state variables, the algorithm allows selecting bases that represent the variability of each state type. Through synthetic numerical experiments of CO2 monitoring, we show that the CSKF can reproduce the Kalman gain accurately even for large compression ratios (m/N). For a given computational cost, the CSKF uses a robust and flexible compression scheme that gives more reliable uncertainty estimates than the ensemble Kalman filter, which may display loss of ensemble variability leading to suboptimal uncertainty estimates.
Kalman-predictive-proportional-integral-derivative (KPPID)
Fluerasu, A.; Sutton, M.
2004-12-17
With third generation synchrotron X-ray sources, it is possible to acquire detailed structural information about the system under study with time resolution orders of magnitude faster than was possible a few years ago. These advances have generated many new challenges for changing and controlling the state of the system on very short time scales, in a uniform and controlled manner. For our particular X-ray experiments on crystallization or order-disorder phase transitions in metallic alloys, we need to change the sample temperature by hundreds of degrees as fast as possible while avoiding over or under shooting. To achieve this, we designed and implemented a computer-controlled temperature tracking system which combines standard Proportional-Integral-Derivative (PID) feedback, thermal modeling and finite difference thermal calculations (feedforward), and Kalman filtering of the temperature readings in order to reduce the noise. The resulting Kalman-Predictive-Proportional-Integral-Derivative (KPPID) algorithm allows us to obtain accurate control, to minimize the response time and to avoid over/under shooting, even in systems with inherently noisy temperature readings and time delays. The KPPID temperature controller was successfully implemented at the Advanced Photon Source at Argonne National Laboratories and was used to perform coherent and time-resolved X-ray diffraction experiments.
Kalman Filter Tracking on Parallel Architectures
NASA Astrophysics Data System (ADS)
Cerati, Giuseppe; Elmer, Peter; Krutelyov, Slava; Lantz, Steven; Lefebvre, Matthieu; McDermott, Kevin; Riley, Daniel; Tadel, Matevž; Wittich, Peter; Würthwein, Frank; Yagil, Avi
2016-11-01
Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors such as GPGPU, ARM and Intel MIC. In order to achieve the theoretical performance gains of these processors, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High-Luminosity Large Hadron Collider (HL-LHC), for example, this will be by far the dominant problem. The need for greater parallelism has driven investigations of very different track finding techniques such as Cellular Automata or Hough Transforms. The most common track finding techniques in use today, however, are those based on a Kalman filter approach. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. They are known to provide high physics performance, are robust, and are in use today at the LHC. Given the utility of the Kalman filter in track finding, we have begun to port these algorithms to parallel architectures, namely Intel Xeon and Xeon Phi. We report here on our progress towards an end-to-end track reconstruction algorithm fully exploiting vectorization and parallelization techniques in a simplified experimental environment.
Kalman Filter Tracking on Parallel Architectures
NASA Astrophysics Data System (ADS)
Cerati, Giuseppe; Elmer, Peter; Lantz, Steven; McDermott, Kevin; Riley, Dan; Tadel, Matevž; Wittich, Peter; Würthwein, Frank; Yagil, Avi
2015-12-01
Power density constraints are limiting the performance improvements of modern CPUs. To address this we have seen the introduction of lower-power, multi-core processors, but the future will be even more exciting. In order to stay within the power density limits but still obtain Moore's Law performance/price gains, it will be necessary to parallelize algorithms to exploit larger numbers of lightweight cores and specialized functions like large vector units. Example technologies today include Intel's Xeon Phi and GPGPUs. Track finding and fitting is one of the most computationally challenging problems for event reconstruction in particle physics. At the High Luminosity LHC, for example, this will be by far the dominant problem. The need for greater parallelism has driven investigations of very different track finding techniques including Cellular Automata or returning to Hough Transform. The most common track finding techniques in use today are however those based on the Kalman Filter [2]. Significant experience has been accumulated with these techniques on real tracking detector systems, both in the trigger and offline. They are known to provide high physics performance, are robust and are exactly those being used today for the design of the tracking system for HL-LHC. Our previous investigations showed that, using optimized data structures, track fitting with Kalman Filter can achieve large speedup both with Intel Xeon and Xeon Phi. We report here our further progress towards an end-to-end track reconstruction algorithm fully exploiting vectorization and parallelization techniques in a realistic simulation setup.
Neumann, M; Cuvillon, L; Breton, E; de Matheli, M
2013-01-01
Recently, a workflow for magnetic resonance (MR) image plane alignment based on tracking in real-time MR images was introduced. The workflow is based on a tracking device composed of 2 resonant micro-coils and a passive marker, and allows for tracking of the passive marker in clinical real-time images and automatic (re-)initialization using the microcoils. As the Kalman filter has proven its benefit as an estimator and predictor, it is well suited for use in tracking applications. In this paper, a Kalman filter is integrated in the previously developed workflow in order to predict position and orientation of the tracking device. Measurement noise covariances of the Kalman filter are dynamically changed in order to take into account that, according to the image plane orientation, only a subset of the 3D pose components is available. The improved tracking performance of the Kalman extended workflow could be quantified in simulation results. Also, a first experiment in the MRI scanner was performed but without quantitative results yet.
NASA Technical Reports Server (NTRS)
Thompson, E. H.; Farrell, J. L.
1976-01-01
Monte Carlo simulation of autonomous orbit determination has validated the use of an 18-bit NASA Standard Spacecraft Computer (NSSC) for the extended Kalman filter. Dimensionally consistent scales are chosen for all variables in the algorithm, such that nearly all of the onboard computation can be performed in single precision without matrix square root formulations. Allowable simplifications in algorithm implementation and practical means of ensuring convergence are verified for accuracies of a few km provided by star/vertical observations
Digital adaptive controllers using second order models with transport lag
NASA Technical Reports Server (NTRS)
Joshi, S.; Kaufman, H.
1975-01-01
Design of a discrete optimal regulator requires the a priori knowledge of a mathematical model for the system of interest. Because a second-order model with transport lag is very amenable to control computations and because this type of model has been used previously to represent certain high order single input-single output processes, an adaptive controller was designed based upon adjustment of controls computed for such a model. An extended Kalman filter was utilized for tracking the model parameters which were subsequently used to update a set of optimal control gains. Favorable results were obtained in applying this procedure to the control of several examples including a ninth order nonlinear process.
Rye, B J; Hardesty, R M
1989-09-15
Recursive estimation of nonlinear functions of the return power in a lidar system entails use of a nonlinear filter. This also permits processing of returns in the presence of multiplicative noise (speckle). The use of the extended Kalman filter is assessed here for estimation of return power, log power, and speckle noise (which is regarded as a system rather than a measurement component), using coherent lidar returns and tested with simulated data. Reiterative processing of data samples using system models comprising a random walk signal together with an uncorrelated speckle term leads to self-consistent estimation of the parameters.
A Kalman Approach to Lunar Surface Navigation using Radiometric and Inertial Measurements
NASA Technical Reports Server (NTRS)
Chelmins, David T.; Welch, Bryan W.; Sands, O. Scott; Nguyen, Binh V.
2009-01-01
Future lunar missions supporting the NASA Vision for Space Exploration will rely on a surface navigation system to determine astronaut position, guide exploration, and return safely to the lunar habitat. In this report, we investigate one potential architecture for surface navigation, using an extended Kalman filter to integrate radiometric and inertial measurements. We present a possible infrastructure to support this technique, and we examine an approach to simulating navigational accuracy based on several different system configurations. The results show that position error can be reduced to 1 m after 5 min of processing, given two satellites, one surface communication terminal, and knowledge of the starting position to within 100 m.
Upper Atmosphere Data Assimilation using an ensemble Kalman filter
NASA Astrophysics Data System (ADS)
Matsuo, T.; Lee, I.; Anderson, J. L.
2013-12-01
We present an application of ensemble Kalman filtering (EnKF) to a general circulation model of the thermosphere and ionosphere. An EnKF assimilation procedure has been constructed using the Data Assimilation Research Testbed and the Thermoshere-Ionosphere Electrodynamics General Circulation Model, two sets of community software offered by NCAR. It is designed to incorporate thermosphere-ionosphere coupling self-consistently both in the analysis and forecast steps of filtering, so that thermospheric parameters can be inferred from ionospheric observations and vice versa. One of the important advantages of the EnKF is that its covariance is consistent with the time-dependent dynamics of the thermosphere and ionosphere. In spite of sampling error issues, cross-covariance between observations and unobserved state variables obtained from the ensemble is effective enough to constrain the thermospheric state from abundant observations of electron density, which in turn improves the overall specification of the ionosphere. We demonstrate these points using specific observations including (i) neutral mass densities obtained from the accelerometer experiment on board the CHAMP satellite, and (ii) electron density profiles obtained from the COSMIC/FORMOSAT-3 mission. Furthermore, we discuss some of the issues specific to upper atmospheric EnKF applications and the roles of auxiliary filtering algorithms, such as adaptive covariance inflation and localization of covariance, to cope with these issues.
Kalman filtering techniques for focal plane electric field estimation.
Groff, Tyler D; Jeremy Kasdin, N
2013-01-01
For a coronagraph to detect faint exoplanets, it will require focal plane wavefront control techniques to continue reaching smaller angular separations and higher contrast levels. These correction algorithms are iterative and the control methods need an estimate of the electric field at the science camera, which requires nearly all of the images taken for the correction. The best way to make such algorithms the least disruptive to science exposures is to reduce the number required to estimate the field. We demonstrate a Kalman filter estimator that uses prior knowledge to create the estimate of the electric field, dramatically reducing the number of exposures required to estimate the image plane electric field while stabilizing the suppression against poor signal-to-noise. In addition to a significant reduction in exposures, we discuss the relative merit of this algorithm to estimation schemes that do not incorporate prior state estimate history, particularly in regard to estimate error and covariance. Ultimately the filter will lead to an adaptive algorithm which can estimate physical parameters in the laboratory for robustness to variance in the optical train.
Towards Extended Vantage Theory
ERIC Educational Resources Information Center
Glaz, Adam
2010-01-01
The applicability of Vantage Theory (VT), a model of (colour) categorization, to linguistic data largely depends on the modifications and adaptations of the model for the purpose. An attempt to do so proposed here, called Extended Vantage Theory (EVT), slightly reformulates the VT conception of vantage by capitalizing on some of the entailments of…
Hierarchical Bayes Ensemble Kalman Filter for geophysical data assimilation
NASA Astrophysics Data System (ADS)
Tsyrulnikov, Michael; Rakitko, Alexander
2016-04-01
In the Ensemble Kalman Filter (EnKF), the forecast error covariance matrix B is estimated from a sample (ensemble), which inevitably implies a degree of uncertainty. This uncertainty is especially large in high dimensions, where the affordable ensemble size is orders of magnitude less than the dimensionality of the system. Common remedies include ad-hoc devices like variance inflation and covariance localization. The goal of this study is to optimize the account for the inherent uncertainty of the B matrix in EnKF. Following the idea by Myrseth and Omre (2010), we explicitly admit that the B matrix is unknown and random and estimate it along with the state (x) in an optimal hierarchical Bayes analysis scheme. We separate forecast errors into predictability errors (i.e. forecast errors due to uncertainties in the initial data) and model errors (forecast errors due to imperfections in the forecast model) and include the two respective components P and Q of the B matrix into the extended control vector (x,P,Q). Similarly, we break the traditional forecast ensemble into the predictability-error related ensemble and model-error related ensemble. The reason for the separation of model errors from predictability errors is the fundamental difference between the two sources of error. Model error are external (i.e. do not depend on the filter's performance) whereas predictability errors are internal to a filter (i.e. are determined by the filter's behavior). At the analysis step, we specify Inverse Wishart based priors for the random matrices P and Q and conditionally Gaussian prior for the state x. Then, we update the prior distribution of (x,P,Q) using both observation and ensemble data, so that ensemble members are used as generalized observations and ordinary observations are allowed to influence the covariances. We show that for linear dynamics and linear observation operators, conditional Gaussianity of the state is preserved in the course of filtering. At the forecast
NASA Technical Reports Server (NTRS)
Canfield, Stephen
1999-01-01
This work will demonstrate the integration of sensor and system dynamic data and their appropriate models using an optimal filter to create a robust, adaptable, easily reconfigurable state (motion) estimation system. This state estimation system will clearly show the application of fundamental modeling and filtering techniques. These techniques are presented at a general, first principles level, that can easily be adapted to specific applications. An example of such an application is demonstrated through the development of an integrated GPS/INS navigation system. This system acquires both global position data and inertial body data, to provide optimal estimates of current position and attitude states. The optimal states are estimated using a Kalman filter. The state estimation system will include appropriate error models for the measurement hardware. The results of this work will lead to the development of a "black-box" state estimation system that supplies current motion information (position and attitude states) that can be used to carry out guidance and control strategies. This black-box state estimation system is developed independent of the vehicle dynamics and therefore is directly applicable to a variety of vehicles. Issues in system modeling and application of Kalman filtering techniques are investigated and presented. These issues include linearized models of equations of state, models of the measurement sensors, and appropriate application and parameter setting (tuning) of the Kalman filter. The general model and subsequent algorithm is developed in Matlab for numerical testing. The results of this system are demonstrated through application to data from the X-33 Michael's 9A8 mission and are presented in plots and simple animations.
Kalman filter data assimilation: targeting observations and parameter estimation.
Bellsky, Thomas; Kostelich, Eric J; Mahalov, Alex
2014-06-01
This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.
Kalman filter data assimilation: Targeting observations and parameter estimation
Bellsky, Thomas Kostelich, Eric J.; Mahalov, Alex
2014-06-15
This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.
ON THE CONVERGENCE OF THE ENSEMBLE KALMAN FILTER
Mandel, Jan; Cobb, Loren; Beezley, Jonathan D.
2013-01-01
Convergence of the ensemble Kalman filter in the limit for large ensembles to the Kalman filter is proved. In each step of the filter, convergence of the ensemble sample covariance follows from a weak law of large numbers for exchangeable random variables, the continuous mapping theorem gives convergence in probability of the ensemble members, and Lp bounds on the ensemble then give Lp convergence. PMID:24843228
Estimation of FBMC/OQAM fading channels using dual Kalman filters.
Aldababseh, Mahmoud; Jamoos, Ali
2014-01-01
We address the problem of estimating time-varying fading channels in filter bank multicarrier (FBMC/OQAM) wireless systems based on pilot symbols. The standard solution to this problem is the least square (LS) estimator or the minimum mean square error (MMSE) estimator with possible adaptive implementation using recursive least square (RLS) algorithm or least mean square (LMS) algorithm. However, these adaptive filters cannot well-exploit fading channel statistics. To take advantage of fading channel statistics, the time evolution of the fading channel is modeled by an autoregressive process and tracked by Kalman filter. Nevertheless, this requires the autoregressive parameters which are usually unknown. Thus, we propose to jointly estimate the FBMC/OQAM fading channels and their autoregressive parameters based on dual optimal Kalman filters. Once the fading channel coefficients at pilot symbol positions are estimated by the proposed method, the fading channel coefficients at data symbol positions are then estimated by using some interpolation methods such as linear, spline, or low-pass interpolation. The comparative simulation study we carried out with existing techniques confirms the effectiveness of the proposed method.
Estimation of FBMC/OQAM Fading Channels Using Dual Kalman Filters
Aldababseh, Mahmoud
2014-01-01
We address the problem of estimating time-varying fading channels in filter bank multicarrier (FBMC/OQAM) wireless systems based on pilot symbols. The standard solution to this problem is the least square (LS) estimator or the minimum mean square error (MMSE) estimator with possible adaptive implementation using recursive least square (RLS) algorithm or least mean square (LMS) algorithm. However, these adaptive filters cannot well-exploit fading channel statistics. To take advantage of fading channel statistics, the time evolution of the fading channel is modeled by an autoregressive process and tracked by Kalman filter. Nevertheless, this requires the autoregressive parameters which are usually unknown. Thus, we propose to jointly estimate the FBMC/OQAM fading channels and their autoregressive parameters based on dual optimal Kalman filters. Once the fading channel coefficients at pilot symbol positions are estimated by the proposed method, the fading channel coefficients at data symbol positions are then estimated by using some interpolation methods such as linear, spline, or low-pass interpolation. The comparative simulation study we carried out with existing techniques confirms the effectiveness of the proposed method. PMID:24701181
Kalman Filtering with Inequality Constraints for Turbofan Engine Health Estimation
NASA Technical Reports Server (NTRS)
Simon, Dan; Simon, Donald L.
2003-01-01
Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This paper develops two analytic methods of incorporating state variable inequality constraints in the Kalman filter. The first method is a general technique of using hard constraints to enforce inequalities on the state variable estimates. The resultant filter is a combination of a standard Kalman filter and a quadratic programming problem. The second method uses soft constraints to estimate state variables that are known to vary slowly with time. (Soft constraints are constraints that are required to be approximately satisfied rather than exactly satisfied.) The incorporation of state variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is proven theoretically and shown via simulation results. The use of the algorithm is demonstrated on a linearized simulation of a turbofan engine to estimate health parameters. The turbofan engine model contains 16 state variables, 12 measurements, and 8 component health parameters. It is shown that the new algorithms provide improved performance in this example over unconstrained Kalman filtering.
Liang, Fan; Yu, Yang; Cui, Shigang; Zhao, Li; Wu, Xingli
2014-05-01
Robot Assisted Coronary Artery Bypass Graft (CABG) allows the heart keep beating in the surgery by actively eliminating the relative motion between point of interest (POI) on the heart surface and surgical tool. The inherited nonlinear and diverse nature of beating heart motion gives a huge obstacle for the robot to meet the demanding tracking control requirements. In this paper, we novelty propose a Master-slave Kalman Filter based on beating heart motion Nonlinear Adaptive Prediction (NAP) algorithm. In the study, we describe the beating heart motion as the combination of nonlinearity relating mathematics part and uncertainty relating non-mathematics part. Specifically, first, we model the nonlinearity of the heart motion via quadratic modulated sinusoids and estimate it by a Master Kalman Filter. Second, we involve the uncertainty heart motion by adaptively change the covariance of the process noise through the slave Kalman Filter. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The results indicate that the new approach reduces prediction errors by at least 30 μm. Moreover, the new approach performs well in robustness test, in which two kinds of arrhythmia datasets from MIT-BIH arrhythmia database are assessed.
Hecht, R.M.; Schomer, D.F.; Oro, J.A.; Bartel, A.H.; Hungerford, E.V. III
1981-01-01
Procedures and instrumentation are described to extend the capability of a cytometry system to record samples that exhibit a wide range of fluorescence such as multicellular systems. The method employs a log amplifier in combination with a set of neutral density filters that reduces the incident light reaching the photomultiplier tube. With any given filter, signals within an intensity range of 200-fold can be measured; different filters can be used to obtain an extended overall range. Polystyrene fluorescent microspheres and a variety of mithramycin stained biological samples ranging from yeast cells to Paramecium were processed by the system. The relative DNA content of individual multicellular embryos was determined for a heterogeneous population of embryonic stages isolated from the nematoda, Caenorhahditis elegans. As part of the evaluation of the procedure, the practical upper limit of range extension was determined. The most intense fluorescent signal was produced when untreated pecan pollen stained with ethidium bromide fluoresced with a factor (8.4 +- 1.3) x 10/sup 4/ more than ethidium bromide stained E. coli cells.
Parnian, Neda; Golnaraghi, Farid
2010-01-01
This paper describes the development of a modified Kalman filter to integrate a multi-camera vision system and strapdown inertial navigation system (SDINS) for tracking a hand-held moving device for slow or nearly static applications over extended periods of time. In this algorithm, the magnitude of the changes in position and velocity are estimated and then added to the previous estimation of the position and velocity, respectively. The experimental results of the hybrid vision/SDINS design show that the position error of the tool tip in all directions is about one millimeter RMS. The proposed Kalman filter removes the effect of the gravitational force in the state-space model. As a result, the resulting error is eliminated and the resulting position is smoother and ripple-free. PMID:22219667
Parnian, Neda; Golnaraghi, Farid
2010-01-01
This paper describes the development of a modified Kalman filter to integrate a multi-camera vision system and strapdown inertial navigation system (SDINS) for tracking a hand-held moving device for slow or nearly static applications over extended periods of time. In this algorithm, the magnitude of the changes in position and velocity are estimated and then added to the previous estimation of the position and velocity, respectively. The experimental results of the hybrid vision/SDINS design show that the position error of the tool tip in all directions is about one millimeter RMS. The proposed Kalman filter removes the effect of the gravitational force in the state-space model. As a result, the resulting error is eliminated and the resulting position is smoother and ripple-free.
Zhu, Eric F.; Gai, Shuning A.; Opel, Cary F.; Kwan, Byron H.; Surana, Rishi; Mihm, Martin C.; Kauke, Monique J.; Moynihan, Kelly D.; Angelini, Alessandro; Williams, Robert T.; Stephan, Matthias T.; Kim, Jacob S.; Yaffe, Michael B.; Irvine, Darrell J.; Weiner, Louis M.; Dranoff, Glenn
2015-01-01
Summary Cancer immunotherapies under development have generally focused on either stimulating T-cell immunity or driving antibody-directed effector functions of the innate immune system such as antibody-dependent cell-mediated cytotoxicity (ADCC). We find that a combination of an anti-tumor antigen antibody and an untargeted IL-2 fusion protein with delayed systemic clearance induces significant tumor control in aggressive isogenic tumor models via a concerted innate and adaptive response involving neutrophils, NK cells, macrophages, and CD8+ T-cells. This combination therapy induces an intratumoral “cytokine storm” and extensive lymphocyte infiltration. Adoptive transfer of anti-tumor T-cells together with this combination therapy leads to robust cures of established tumors and establishment of immunological memory. PMID:25873172
Fast reconstruction and prediction of frozen flow turbulence based on structured Kalman filtering.
Fraanje, Rufus; Rice, Justin; Verhaegen, Michel; Doelman, Niek
2010-11-01
Efficient and optimal prediction of frozen flow turbulence using the complete observation history of the wavefront sensor is an important issue in adaptive optics for large ground-based telescopes. At least for the sake of error budgeting and algorithm performance, the evaluation of an accurate estimate of the optimal performance of a particular adaptive optics configuration is important. However, due to the large number of grid points, high sampling rates, and the non-rationality of the turbulence power spectral density, the computational complexity of the optimal predictor is huge. This paper shows how a structure in the frozen flow propagation can be exploited to obtain a state-space innovation model with a particular sparsity structure. This sparsity structure enables one to efficiently compute a structured Kalman filter. By simulation it is shown that the performance can be improved and the computational complexity can be reduced in comparison with auto-regressive predictors of low order.
Motion estimation using point cluster method and Kalman filter.
Senesh, M; Wolf, A
2009-05-01
The most frequently used method in a three dimensional human gait analysis involves placing markers on the skin of the analyzed segment. This introduces a significant artifact, which strongly influences the bone position and orientation and joint kinematic estimates. In this study, we tested and evaluated the effect of adding a Kalman filter procedure to the previously reported point cluster technique (PCT) in the estimation of a rigid body motion. We demonstrated the procedures by motion analysis of a compound planar pendulum from indirect opto-electronic measurements of markers attached to an elastic appendage that is restrained to slide along the rigid body long axis. The elastic frequency is close to the pendulum frequency, as in the biomechanical problem, where the soft tissue frequency content is similar to the actual movement of the bones. Comparison of the real pendulum angle to that obtained by several estimation procedures--PCT, Kalman filter followed by PCT, and low pass filter followed by PCT--enables evaluation of the accuracy of the procedures. When comparing the maximal amplitude, no effect was noted by adding the Kalman filter; however, a closer look at the signal revealed that the estimated angle based only on the PCT method was very noisy with fluctuation, while the estimated angle based on the Kalman filter followed by the PCT was a smooth signal. It was also noted that the instantaneous frequencies obtained from the estimated angle based on the PCT method is more dispersed than those obtained from the estimated angle based on Kalman filter followed by the PCT method. Addition of a Kalman filter to the PCT method in the estimation procedure of rigid body motion results in a smoother signal that better represents the real motion, with less signal distortion than when using a digital low pass filter. Furthermore, it can be concluded that adding a Kalman filter to the PCT procedure substantially reduces the dispersion of the maximal and minimal
Reduced-Order Kalman Filtering for Processing Relative Measurements
NASA Technical Reports Server (NTRS)
Bayard, David S.
2008-01-01
A study in Kalman-filter theory has led to a method of processing relative measurements to estimate the current state of a physical system, using less computation than has previously been thought necessary. As used here, relative measurements signifies measurements that yield information on the relationship between a later and an earlier state of the system. An important example of relative measurements arises in computer vision: Information on relative motion is extracted by comparing images taken at two different times. Relative measurements do not directly fit into standard Kalman filter theory, in which measurements are restricted to those indicative of only the current state of the system. One approach heretofore followed in utilizing relative measurements in Kalman filtering, denoted state augmentation, involves augmenting the state of the system at the earlier of two time instants and then propagating the state to the later time instant.While state augmentation is conceptually simple, it can also be computationally prohibitive because it doubles the number of states in the Kalman filter. When processing a relative measurement, if one were to follow the state-augmentation approach as practiced heretofore, one would find it necessary to propagate the full augmented state Kalman filter from the earlier time to the later time and then select out the reduced-order components. The main result of the study reported here is proof of a property called reduced-order equivalence (ROE). The main consequence of ROE is that it is not necessary to augment with the full state, but, rather, only the portion of the state that is explicitly used in the partial relative measurement. In other words, it suffices to select the reduced-order components first and then propagate the partial augmented state Kalman filter from the earlier time to the later time; the amount of computation needed to do this can be substantially less than that needed for propagating the full augmented
Kalman Filter for Spinning Spacecraft Attitude Estimation
NASA Technical Reports Server (NTRS)
Markley, F. Landis; Sedlak, Joseph E.
2008-01-01
This paper presents a Kalman filter using a seven-component attitude state vector comprising the angular momentum components in an inertial reference frame, the angular momentum components in the body frame, and a rotation angle. The relatively slow variation of these parameters makes this parameterization advantageous for spinning spacecraft attitude estimation. The filter accounts for the constraint that the magnitude of the angular momentum vector is the same in the inertial and body frames by employing a reduced six-component error state. Four variants of the filter, defined by different choices for the reduced error state, are tested against a quaternion-based filter using simulated data for the THEMIS mission. Three of these variants choose three of the components of the error state to be the infinitesimal attitude error angles, facilitating the computation of measurement sensitivity matrices and causing the usual 3x3 attitude covariance matrix to be a submatrix of the 6x6 covariance of the error state. These variants differ in their choice for the other three components of the error state. The variant employing the infinitesimal attitude error angles and the angular momentum components in an inertial reference frame as the error state shows the best combination of robustness and efficiency in the simulations. Attitude estimation results using THEMIS flight data are also presented.
Multivariate localization methods for ensemble Kalman filtering
NASA Astrophysics Data System (ADS)
Roh, S.; Jun, M.; Szunyogh, I.; Genton, M. G.
2015-05-01
In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (entry-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.
Multivariate localization methods for ensemble Kalman filtering
NASA Astrophysics Data System (ADS)
Roh, S.; Jun, M.; Szunyogh, I.; Genton, M. G.
2015-12-01
In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.
Compressed state Kalman filter for large systems
NASA Astrophysics Data System (ADS)
Kitanidis, Peter K.
2015-02-01
The Kalman filter (KF) is a recursive filter that allows the assimilation of data in real time and has found numerous applications. In earth sciences, the method is applied to systems with very large state vectors obtained from the discretization of functions such as pressure, velocity, solute concentration, and voltage. With state dimension running in the millions, the implementation of the standard or textbook version of KF is very expensive and low-rank approximations have been devised such as EnKF and SEEK. Although widely applied, the error behavior of these methods is not adequately understood. This article focuses on very large linear systems and presents a complete computational method that scales roughly linearly with the dimension of the state vector. The method is suited for problems for which the effective rank of the state covariance matrix is much smaller than its dimension. This method is closest to SEEK but uses a fixed basis that should be selected in accordance with the characteristics of the problem, mainly the transition matrix and the system noise covariance. The method is matrix free, i.e., does not require computation of Jacobian matrices and uses the forward model as a black box. Computational results demonstrate the ability of the method to solve very large, say 106 , state vectors.
REVIEW: Internal models in sensorimotor integration: perspectives from adaptive control theory
NASA Astrophysics Data System (ADS)
Tin, Chung; Poon, Chi-Sang
2005-09-01
Internal models and adaptive controls are empirical and mathematical paradigms that have evolved separately to describe learning control processes in brain systems and engineering systems, respectively. This paper presents a comprehensive appraisal of the correlation between these paradigms with a view to forging a unified theoretical framework that may benefit both disciplines. It is suggested that the classic equilibrium-point theory of impedance control of arm movement is analogous to continuous gain-scheduling or high-gain adaptive control within or across movement trials, respectively, and that the recently proposed inverse internal model is akin to adaptive sliding control originally for robotic manipulator applications. Modular internal models' architecture for multiple motor tasks is a form of multi-model adaptive control. Stochastic methods, such as generalized predictive control, reinforcement learning, Bayesian learning and Hebbian feedback covariance learning, are reviewed and their possible relevance to motor control is discussed. Possible applicability of a Luenberger observer and an extended Kalman filter to state estimation problems—such as sensorimotor prediction or the resolution of vestibular sensory ambiguity—is also discussed. The important role played by vestibular system identification in postural control suggests an indirect adaptive control scheme whereby system states or parameters are explicitly estimated prior to the implementation of control. This interdisciplinary framework should facilitate the experimental elucidation of the mechanisms of internal models in sensorimotor systems and the reverse engineering of such neural mechanisms into novel brain-inspired adaptive control paradigms in future.
On the rejection of vibrations in adaptive optics systems
NASA Astrophysics Data System (ADS)
Muradore, Riccardo; Pettazzi, Lorenzo; Fedrigo, Enrico; Clare, Richard
2012-07-01
In modern adaptive optics systems, lightly damped sinusoidal oscillations resulting from telescope structural vibrations have a significant deleterious impact on the quality of the image collected at the detector plane. Such oscillations are often at frequencies beyond the bandwidth of the wave-front controller that therefore is either incapable of rejecting them or might even amplify their detrimental impact on the overall AO performance. A technique for the rejection of periodic disturbances acting at the output of unknown plants, which has been recently presented in literature, has been adapted to the problem of rejecting vibrations in AO loops. The proposed methodology aims at estimating phase and amplitude of the harmonic disturbance together with the response of the unknown plant at the frequency of vibration. On the basis of such estimates, a control signal is generated to cancel out the periodic perturbation. Additionally, the algorithm can be easily extended to cope with unexpected time variations of the vibrations frequency by adding a frequency tracking module based either on a simple PLL architecture or on a classical extended Kalman filter. Oversampling can be also easily introduced to efficiently correct for vibrations approaching the sampling frequency. The approach presented in this contribution is compared against a different algorithm for vibration rejection available in literature, in order to identify drawbacks and advantages. Finally, the performance of the proposed vibration cancellation technique has been tested in realistic scenarios defined exploiting tip/tilt measurements from MACAO and NACO
Adaptive tracking of narrowband HF channel response
NASA Astrophysics Data System (ADS)
Arikan, F.; Arikan, O.
2003-12-01
Estimation of channel impulse response constitutes a first step in computation of scattering function, channel equalization, elimination of multipath, and optimum detection and identification of transmitted signals through the HF channel. Due to spatial and temporal variations, HF channel impulse response has to be estimated adaptively. Based on developed state-space and measurement models, an adaptive Kalman filter is proposed to track the HF channel variation in time. Robust methods of initialization and adaptively adjusting the noise covariance in the system dynamics are proposed. In simulated examples under good, moderate and poor ionospheric conditions, it is observed that the adaptive Kalman filter based channel estimator provides reliable channel estimates and can track the variation of the channel in time with high accuracy.
A novel extended kernel recursive least squares algorithm.
Zhu, Pingping; Chen, Badong; Príncipe, José C
2012-08-01
In this paper, a novel extended kernel recursive least squares algorithm is proposed combining the kernel recursive least squares algorithm and the Kalman filter or its extensions to estimate or predict signals. Unlike the extended kernel recursive least squares (Ex-KRLS) algorithm proposed by Liu, the state model of our algorithm is still constructed in the original state space and the hidden state is estimated using the Kalman filter. The measurement model used in hidden state estimation is learned by the kernel recursive least squares algorithm (KRLS) in reproducing kernel Hilbert space (RKHS). The novel algorithm has more flexible state and noise models. We apply this algorithm to vehicle tracking and the nonlinear Rayleigh fading channel tracking, and compare the tracking performances with other existing algorithms.
Stable Kalman filters for processing clock measurement data
NASA Technical Reports Server (NTRS)
Clements, P. A.; Gibbs, B. P.; Vandergraft, J. S.
1989-01-01
Kalman filters have been used for some time to process clock measurement data. Due to instabilities in the standard Kalman filter algorithms, the results have been unreliable and difficult to obtain. During the past several years, stable forms of the Kalman filter have been developed, implemented, and used in many diverse applications. These algorithms, while algebraically equivalent to the standard Kalman filter, exhibit excellent numerical properties. Two of these stable algorithms, the Upper triangular-Diagonal (UD) filter and the Square Root Information Filter (SRIF), have been implemented to replace the standard Kalman filter used to process data from the Deep Space Network (DSN) hydrogen maser clocks. The data are time offsets between the clocks in the DSN, the timescale at the National Institute of Standards and Technology (NIST), and two geographically intermediate clocks. The measurements are made by using the GPS navigation satellites in mutual view between clocks. The filter programs allow the user to easily modify the clock models, the GPS satellite dependent biases, and the random noise levels in order to compare different modeling assumptions. The results of this study show the usefulness of such software for processing clock data. The UD filter is indeed a stable, efficient, and flexible method for obtaining optimal estimates of clock offsets, offset rates, and drift rates. A brief overview of the UD filter is also given.
Kalman Filter Constraint Tuning for Turbofan Engine Health Estimation
NASA Technical Reports Server (NTRS)
Simon, Dan; Simon, Donald L.
2005-01-01
Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints are often neglected because they do not fit easily into the structure of the Kalman filter. Recently published work has shown a new method for incorporating state variable inequality constraints in the Kalman filter, which has been shown to generally improve the filter s estimation accuracy. However, the incorporation of inequality constraints poses some risk to the estimation accuracy as the Kalman filter is theoretically optimal. This paper proposes a way to tune the filter constraints so that the state estimates follow the unconstrained (theoretically optimal) filter when the confidence in the unconstrained filter is high. When confidence in the unconstrained filter is not so high, then we use our heuristic knowledge to constrain the state estimates. The confidence measure is based on the agreement of measurement residuals with their theoretical values. The algorithm is demonstrated on a linearized simulation of a turbofan engine to estimate engine health.
Hansson, S
2005-01-01
Extended antipaternalism means the use of antipaternalist arguments to defend activities that harm (consenting) others. As an example, a smoker's right to smoke is often invoked in defence of the activities of tobacco companies. It can, however, be shown that antipaternalism in the proper sense does not imply such extended antipaternalism. We may therefore approve of Mill's antipaternalist principle (namely, that the only reason to interfere with someone's behaviour is to protect others from harm) without accepting activities that harm (consenting) others. This has immediate consequences for the ethics of public health. An antipaternalist need not refrain from interfering with activities such as the marketing of tobacco or heroin, boxing promotion, driving with unbelted passengers, or buying sex from "voluntary" prostitutes. PMID:15681674
Dorn, G.K.; Gilbert, H.A.
1989-02-21
An efficient and cost competitive fuel extender liquid is described for blending with lead-free gasoline as an additive thereto in a maximum amount of up to about 35% thereof with 65% by volume of the gasoline in a blended mixture wherein. The content of the extender in the resultant fuel as proportioned on the basis of its thus representative maximum content consists essentially of: naphtha X as represented by C/sub 4/, C/sub 5/ and C/sub 6/ hydrocarbons having a Reid vapor pressure of about 8.5 to 9.6 per ASTM, D323 test procedure and an initial distillation point of about 101/sup 0/F. and an end point of about 280/sup 0/F. within a range of about 10 to 25% by volume, about 3.8 to 6.0% by volume of anhydrous ethanol, a stabilizing amount of a water repellent of the class consisting of ethyl acetate and methyl isotubyl ketone; and about 4 to 10.5% by volume of aromatics benzene and toluene, of benzene and xylene or of benzene with toluene and xylene; the extender having a specific gravity substantially comparable with that of the lead-free gasoline to which it is to be added and having phase stability in the presence of water when mixed with the gasoline.
A comprehensive comparison of sigma-point Kalman filters applied on a complex maneuvering road
NASA Astrophysics Data System (ADS)
Al Shabi, Mohammad; Hatamleh, Khaled; Al Shaer, Samer; Salameh, Iyad; Gadsden, S. Andrew
2016-05-01
In this paper, a comprehensive comparison is made of the following sigma-point Kalman filters: unscented Kalman filter (UKF), cubature Kalman filter (CKF), and the central difference Kalman filter (CDKF). A simulation based on a complex maneuvering road (an s-path) is used as a benchmark problem. This paper studies the response, stability, robustness, convergence, and computational complexity of the filters. Future work will look at implementing the methods on a robot built for experimentation.
Switching Kalman filter for failure prognostic
NASA Astrophysics Data System (ADS)
Lim, Chi Keong Reuben; Mba, David
2015-02-01
The use of condition monitoring (CM) data to predict remaining useful life have been growing with increasing use of health and usage monitoring systems on aircraft. There are many data-driven methodologies available for the prediction and popular ones include artificial intelligence and statistical based approach. The drawback of such approaches is that they require a lot of failure data for training which can be scarce in practice. In lieu of this, methods using state-space and regression-based models that extract information from the data history itself have been explored. However, such methods have their own limitations as they utilize a single time-invariant model which does not represent changing degradation path well. This causes most degradation modeling studies to focus only on segments of their CM data that behaves close to the assumed model. In this paper, a state-space based method; the Switching Kalman Filter (SKF), is adopted for model estimation and life prediction. The SKF approach however, uses multiple models from which the most probable model is inferred from the CM data using Bayesian estimation before it is applied for prediction. At the same time, the inference of the degradation model itself can provide maintainers with more information for their planning. This SKF approach is demonstrated with a case study on gearbox bearings that were found defective from the Republic of Singapore Air Force AH64D helicopter. The use of in-service CM data allows the approach to be applied in a practical scenario and results showed that the developed SKF approach is a promising tool to support maintenance decision-making.
Interaction of Lyapunov vectors in the formulation of the nonlinear extension of the Kalman filter.
Palatella, Luigi; Trevisan, Anna
2015-04-01
When applied to strongly nonlinear chaotic dynamics the extended Kalman filter (EKF) is prone to divergence due to the difficulty of correctly forecasting the forecast error probability density function. In operational forecasting applications ensemble Kalman filters circumvent this problem with empirical procedures such as covariance inflation. This paper presents an extension of the EKF that includes nonlinear terms in the evolution of the forecast error estimate. This is achieved starting from a particular square-root implementation of the EKF with assimilation confined in the unstable subspace (EKF-AUS), that is, the span of the Lyapunov vectors with non-negative exponents. When the error evolution is nonlinear, the space where it is confined is no more restricted to the unstable and neutral subspace causing filter divergence. The algorithm presented here, denominated EKF-AUS-NL, includes the nonlinear terms in the error dynamics: These result from the nonlinear interaction among the leading Lyapunov vectors and account for all directions where the error growth may take place. Numerical results show that with the nonlinear terms included, filter divergence can be avoided. We test the algorithm on the Lorenz96 model, showing very promising results.
Unscented Kalman filtering for single camera based motion and shape estimation.
Jwo, Dah-Jing; Tseng, Chien-Hao; Liu, Jen-Chu; Lee, Hsien-Der
2011-01-01
Accurate estimation of the motion and shape of a moving object is a challenging task due to great variety of noises present from sources such as electronic components and the influence of the external environment, etc. To alleviate the noise, the filtering/estimation approach can be used to reduce it in streaming video to obtain better estimation accuracy in feature points on the moving objects. To deal with the filtering problem in the appropriate nonlinear system, the extended Kalman filter (EKF), which neglects higher-order derivatives in the linearization process, has been very popular. The unscented Kalman filter (UKF), which uses a deterministic sampling approach to capture the mean and covariance estimates with a minimal set of sample points, is able to achieve at least the second order accuracy without Jacobians' computation involved. In this paper, the UKF is applied to the rigid body motion and shape dynamics to estimate feature points on moving objects. The performance evaluation is carried out through the numerical study. The results show that UKF demonstrates substantial improvement in accuracy estimation for implementing the estimation of motion and planar surface parameters of a single camera.
NASA Astrophysics Data System (ADS)
Podladchikova, T.; Shprits, Y.; Kellerman, A. C.
2015-12-01
The Kalman filter technique combines the strengths of new physical models of the Earth's radiation belts with long-term spacecraft observations of electron fluxes and therefore provide an extremely useful method for the analysis of the state and evolution of the electron radiation belts. However, to get the reliable data assimilation output, the Kalman filter application is confronted with a set of fundamental problems. E.g., satellite measurements are usually limited to a single location in space, which confines the reconstruction of the global evolution of the radiation environment. The uncertainties arise from the imperfect description of the process dynamics and the presence of observation errors, which may cause the failure of data assimilation solution. The development of adaptive Kalman filter that combines the Van Allen Probes data and 3-D VERB code, its accurate customizations in the reconstruction of model describing the phase space density (PSD) evolution, extension of the possibilities to use measurement information, and the model adjustment by developing the identification techniques of model and measurement errors allowed us to reveal hidden and implicit regularities of the PSD dynamics and obtain quantitative and qualitative estimates of radial, energy and pitch angle diffusion characteristics from satellite observations. In this study we propose an approach to estimate radial, energy and pitch angle diffusion rates, as well as the direction of their propagation.
NASA Astrophysics Data System (ADS)
Ni, Jiang Q.; Ho, Ka L.; Tse, Kai W.
1998-08-01
Conventional synthesis filters in subband systems lose their optimality when additive noise (due, for example, to signal quantization) disturbs the subband components. The multichannel representation of subband signals is combined with the statistical model of input signal to derive the multirate state-space model for the filter bank system with additive subband noises. Thus the signal reconstruction problem in subband systems can be formulated as the process of optimal state estimation in the equivalent multirate state-space model. Incorporated with the vector dynamic model, a 2D multirate state-space model suitable for 2D Kalman filtering is developed. The performance of the proposed 2D multirate Kalman filter can be further improved through adaptive segmentation of the object plane. The object plane is partitioned into disjoint regions based on their spatial activity, and different vector dynamical models are used to characterize the nonstationary object- plane distributions. Finally, computer simulations with the proposed 2D multirate Kalman filter give favorable results.
State estimation Kalman filter using optical processings Noise statistics known
NASA Technical Reports Server (NTRS)
Jackson, J.; Casasent, D.
1984-01-01
Reference is made to a study by Casasent et al. (1983), which gave a description of a frequency-multiplexed acoustooptic processor and showed how it was capable of performing all the individual operations required in Kalman filtering. The data flow and organization of all required operations however, were not detailed in that study. Consideration is given here to a simpler Kalman filter state estimation problem. Equally spaced time-sampled intervals (k times T sub s, with k the iterative time index) are assumed. It is further assumed that the system noise vector w and the measurement noise vector v are uncorrelated and Gaussian distributed and that the noise statistics (Q and R) and the system model (Phi, Gamma, H) are known. The error covariance matrix P and the extrapolated error covariance matrix M can thus be precomputed and the Kalman gain matrix K sub k can be precomputed and stored for each input time sample.
Numerical comparison of Kalman filter algorithms - Orbit determination case study
NASA Technical Reports Server (NTRS)
Bierman, G. J.; Thornton, C. L.
1977-01-01
Numerical characteristics of various Kalman filter algorithms are illustrated with a realistic orbit determination study. The case study of this paper highlights the numerical deficiencies of the conventional and stabilized Kalman algorithms. Computational errors associated with these algorithms are found to be so large as to obscure important mismodeling effects and thus cause misleading estimates of filter accuracy. The positive result of this study is that the U-D covariance factorization algorithm has excellent numerical properties and is computationally efficient, having CPU costs that differ negligibly from the conventional Kalman costs. Accuracies of the U-D filter using single precision arithmetic consistently match the double precision reference results. Numerical stability of the U-D filter is further demonstrated by its insensitivity to variations in the a priori statistics.
UDU/T/ covariance factorization for Kalman filtering
NASA Technical Reports Server (NTRS)
Thornton, C. L.; Bierman, G. J.
1980-01-01
There has been strong motivation to produce numerically stable formulations of the Kalman filter algorithms because it has long been known that the original discrete-time Kalman formulas are numerically unreliable. Numerical instability can be avoided by propagating certain factors of the estimate error covariance matrix rather than the covariance matrix itself. This paper documents filter algorithms that correspond to the covariance factorization P = UDU(T), where U is a unit upper triangular matrix and D is diagonal. Emphasis is on computational efficiency and numerical stability, since these properties are of key importance in real-time filter applications. The history of square-root and U-D covariance filters is reviewed. Simple examples are given to illustrate the numerical inadequacy of the Kalman covariance filter algorithms; these examples show how factorization techniques can give improved computational reliability.
The discrete Kalman filtering approach for seismic signals deconvolution
Kurniadi, Rizal; Nurhandoko, Bagus Endar B.
2012-06-20
Seismic signals are a convolution of reflectivity and seismic wavelet. One of the most important stages in seismic data processing is deconvolution process; the process of deconvolution is inverse filters based on Wiener filter theory. This theory is limited by certain modelling assumptions, which may not always valid. The discrete form of the Kalman filter is then used to generate an estimate of the reflectivity function. The main advantage of Kalman filtering is capability of technique to handling continually time varying models and has high resolution capabilities. In this work, we use discrete Kalman filter that it was combined with primitive deconvolution. Filtering process works on reflectivity function, hence the work flow of filtering is started with primitive deconvolution using inverse of wavelet. The seismic signals then are obtained by convoluting of filtered reflectivity function with energy waveform which is referred to as the seismic wavelet. The higher frequency of wavelet gives smaller wave length, the graphs of these results are presented.
NASA Astrophysics Data System (ADS)
Gorsevski, Pece V.; Jankowski, Piotr
2010-08-01
The Kalman recursive algorithm has been very widely used for integrating navigation sensor data to achieve optimal system performances. This paper explores the use of the Kalman filter to extend the aggregation of spatial multi-criteria evaluation (MCE) and to find optimal solutions with respect to a decision strategy space where a possible decision rule falls. The approach was tested in a case study in the Clearwater National Forest in central Idaho, using existing landslide datasets from roaded and roadless areas and terrain attributes. In this approach, fuzzy membership functions were used to standardize terrain attributes and develop criteria, while the aggregation of the criteria was achieved by the use of a Kalman filter. The approach presented here offers advantages over the classical MCE theory because the final solution includes both the aggregated solution and the areas of uncertainty expressed in terms of standard deviation. A comparison of this methodology with similar approaches suggested that this approach is promising for predicting landslide susceptibility and further application as a spatial decision support system.
Parallelized unscented Kalman filters for carrier recovery in coherent optical communication.
Jignesh, Jokhakar; Corcoran, Bill; Lowery, Arthur
2016-07-15
We show that unscented Kalman filters can be used to mitigate local oscillator phase noise and to compensate carrier frequency offset in coherent single-carrier optical communication systems. A parallel processing architecture implementing the unscented Kalman filter is proposed, improving upon a previous parallelized linear Kalman filter (LKF) implementation.
On-line physical parameter identification and adaptive control of a launch vehicle
NASA Astrophysics Data System (ADS)
Keller, Brian Scott
Physical parameter identification is useful in many applications, especially in aerospace where much analysis goes into developing accurate physical system models for control. A number of off-line physical parameter identification methods exist; however, the choice of on-line methods is more limited. On-line identification methods are required for adaptive control. New on-line physical parameter identification methods are developed in this work as motivated by the problem of launch vehicle adaptive control. Launch vehicles vary from launch to launch due to differences in payloads and fuel loading. Based on the known variations, launch vehicle control laws are reanalyzed and modified if necessary; this process is expensive and adds to recurring launch vehicle costs. This reanalysis is performed despite the fact that changes in the launch vehicle are relatively minor. A trustworthy adaptive control system could eliminate this expensive redesign cycle. An adaptive control system could also provide better performance than a controller redesigned off-line. However, adaptive control is still considered too risky to use with unstable systems, primarily due to limitations in the identification methods currently available for use in adaptive control. This problem is addressed with the development of new identification algorithms. A philosophy of identification is described which uses physical parameters for identification. A technique is developed to convert existing on-line methods to a form capable of identifying physical parameters. New methods include physical parameter versions of normalized least mean squares (NLMS), research least squares (RLS), extended least squares (ELS), recursive maximum likelihood (RML), and the extended Kalman filter (EKF). Compared to transfer function identification, physical parameter identification reduces the order of the problem and speeds up convergence. Compared to the extended Kalman filter, the new methods have a faster iteration
Detection of atomic clock frequency jumps with the Kalman filter.
Galleani, Lorenzo; Tavella, Patrizia
2012-03-01
Frequency jumps are common anomalies in atomic clocks aboard navigation system satellites. These anomalous behaviors must be detected quickly and accurately to minimize the impact on user positioning. We develop a detector for frequency jumps based on the Kalman filter. Numerical simulations show that the detector is fast, with high probability of detection and low probability of false alarms. It also has a low computational cost because it takes advantage of the recursive nature of the Kalman filter. Therefore, it can be used in applications in which little computational power is available, such as aboard navigation system satellites.
Mobile indoor localization using Kalman filter and trilateration technique
NASA Astrophysics Data System (ADS)
Wahid, Abdul; Kim, Su Mi; Choi, Jaeho
2015-12-01
In this paper, an indoor localization method based on Kalman filtered RSSI is presented. The indoor communications environment however is rather harsh to the mobiles since there is a substantial number of objects distorting the RSSI signals; fading and interference are main sources of the distortion. In this paper, a Kalman filter is adopted to filter the RSSI signals and the trilateration method is applied to obtain the robust and accurate coordinates of the mobile station. From the indoor experiments using the WiFi stations, we have found that the proposed algorithm can provide a higher accuracy with relatively lower power consumption in comparison to a conventional method.
Recursive least squares estimation and Kalman filtering by systolic arrays
NASA Technical Reports Server (NTRS)
Chen, M. J.; Yao, K.
1988-01-01
One of the most promising new directions for high-throughput-rate problems is that based on systolic arrays. In this paper, using the matrix-decomposition approach, a systolic Kalman filter is formulated as a modified square-root information filter consisting of a whitening filter followed by a simple least-squares operation based on the systolic QR algorithm. By proper skewing of the input data, a fully pipelined time and measurement update systolic Kalman filter can be achieved with O(n squared) processing cells, resulting in a system throughput rate of O (n).
Adapting a truly nonlinear filter to the ocean acoustic inverse problem
NASA Astrophysics Data System (ADS)
Ganse, Andrew A.; Odom, Robert I.
2005-04-01
Nonlinear inverse problems including the ocean acoustic problem have been solved by Monte Carlo, locally-linear, and filter based techniques such as the Extended Kalman Filter (EKF). While these techniques do provide statistical information about the solution (e.g., mean and variance), each suffers from inherent limitations in their approach to nonlinear problems. Monte Carlo techniques are expensive to compute and do not contribute to intuitive interpretation of a problem, and locally-linear techniques (including the EKF) are limited by the multimodal objective landscape of nonlinear problems. A truly nonlinear filter, based on recent work in nonlinear tracking, estimates state information for a nonlinear problem in continual measurement updates and is adapted to solving nonlinear inverse problems. Additional terms derived from the system's state PDF are added to the mean and covariance of the solution to address the nonlinearities of the problem, and overall the technique offers improved performance in nonlinear inversion. [Work supported by ONR.
NASA Technical Reports Server (NTRS)
Choe, C. Y.; Tapley, B. D.
1975-01-01
A method proposed by Potter of applying the Kalman-Bucy filter to the problem of estimating the state of a dynamic system is described, in which the square root of the state error covariance matrix is used to process the observations. A new technique which propagates the covariance square root matrix in lower triangular form is given for the discrete observation case. The technique is faster than previously proposed algorithms and is well-adapted for use with the Carlson square root measurement algorithm.
Fang, Joyce; Savransky, Dmitry
2016-08-01
Automation of alignment tasks can provide improved efficiency and greatly increase the flexibility of an optical system. Current optical systems with automated alignment capabilities are typically designed to include a dedicated wavefront sensor. Here, we demonstrate a self-aligning method for a reconfigurable system using only focal plane images. We define a two lens optical system with 8 degrees of freedom. Images are simulated given misalignment parameters using ZEMAX software. We perform a principal component analysis on the simulated data set to obtain Karhunen-Loève modes, which form the basis set whose weights are the system measurements. A model function, which maps the state to the measurement, is learned using nonlinear least-squares fitting and serves as the measurement function for the nonlinear estimator (extended and unscented Kalman filters) used to calculate control inputs to align the system. We present and discuss simulated and experimental results of the full system in operation.
Sabatini, Angelo Maria
2011-01-01
In this paper we present a quaternion-based Extended Kalman Filter (EKF) for estimating the three-dimensional orientation of a rigid body. The EKF exploits the measurements from an Inertial Measurement Unit (IMU) that is integrated with a tri-axial magnetic sensor. Magnetic disturbances and gyro bias errors are modeled and compensated by including them in the filter state vector. We employ the observability rank criterion based on Lie derivatives to verify the conditions under which the nonlinear system that describes the process of motion tracking by the IMU is observable, namely it may provide sufficient information for performing the estimation task with bounded estimation errors. The observability conditions are that the magnetic field, perturbed by first-order Gauss-Markov magnetic variations, and the gravity vector are not collinear and that the IMU is subject to some angular motions. Computer simulations and experimental testing are presented to evaluate the algorithm performance, including when the observability conditions are critical.
NASA Astrophysics Data System (ADS)
Peyret, Nicolas; Dion, Jean-Luc; Chevallier, Gael
2016-10-01
This paper deals with the use of piezoelectric patches for nonlinear dynamic identification. The patches are glued on the structure to identify amplitude-dependent damping and natural frequency; their positions are defined in order to perform the excitation concentrated on the first bending mode. Their locations on the structure allow to perform "stop sines" tests, as, unlike electrodynamic shakers, piezos are embedded on structures and do not modify the studied structure after the excitation signal is switched off. Although, despite the piezo and the stop-sine, the signal is still modulated by other frequency components or polluted by random signals, a post processing with the extended Kalman Filter allows a very good determination of the modal damping and the natural frequency, especially when they depends on the free vibration amplitude.
Vertical Covariance Localization for Satellite Radiances in Ensemble Kalman Filters
2010-01-01
Vertical Covariance Localization for Satellite Radiances in Ensemble Kalman Filters WILLIAM F. CAMPBELL, CRAIG H. BISHOP, AND DANIEL HODYSS Naval...being used in the operational data assimila- tion system at Environment Canada for their ensemble Corresponding author address: Dr. William F...here. Acknowledgments. The authors thank Jeff Whitaker, Peter Houtekamer, Herschel Mitchell, and our anony- mous reviewer for their valuable comments. We
An optimal modification of a Kalman filter for time scales
NASA Technical Reports Server (NTRS)
Greenhall, C. A.
2003-01-01
The Kalman filter in question, which was implemented in the time scale algorithm TA(NIST), produces time scales with poor short-term stability. A simple modification of the error covariance matrix allows the filter to produce time scales with good stability at all averaging times, as verified by simulations of clock ensembles.
Kalman filter techniques for accelerated Cartesian dynamic cardiac imaging.
Feng, Xue; Salerno, Michael; Kramer, Christopher M; Meyer, Craig H
2013-05-01
In dynamic MRI, spatial and temporal parallel imaging can be exploited to reduce scan time. Real-time reconstruction enables immediate visualization during the scan. Commonly used view-sharing techniques suffer from limited temporal resolution, and many of the more advanced reconstruction methods are either retrospective, time-consuming, or both. A Kalman filter model capable of real-time reconstruction can be used to increase the spatial and temporal resolution in dynamic MRI reconstruction. The original study describing the use of the Kalman filter in dynamic MRI was limited to non-Cartesian trajectories because of a limitation intrinsic to the dynamic model used in that study. Here the limitation is overcome, and the model is applied to the more commonly used Cartesian trajectory with fast reconstruction. Furthermore, a combination of the Kalman filter model with Cartesian parallel imaging is presented to further increase the spatial and temporal resolution and signal-to-noise ratio. Simulations and experiments were conducted to demonstrate that the Kalman filter model can increase the temporal resolution of the image series compared with view-sharing techniques and decrease the spatial aliasing compared with TGRAPPA. The method requires relatively little computation, and thus is suitable for real-time reconstruction.
Iterated unscented Kalman filter for phase unwrapping of interferometric fringes.
Xie, Xianming
2016-08-22
A fresh phase unwrapping algorithm based on iterated unscented Kalman filter is proposed to estimate unambiguous unwrapped phase of interferometric fringes. This method is the result of combining an iterated unscented Kalman filter with a robust phase gradient estimator based on amended matrix pencil model, and an efficient quality-guided strategy based on heap sort. The iterated unscented Kalman filter that is one of the most robust methods under the Bayesian theorem frame in non-linear signal processing so far, is applied to perform simultaneously noise suppression and phase unwrapping of interferometric fringes for the first time, which can simplify the complexity and the difficulty of pre-filtering procedure followed by phase unwrapping procedure, and even can remove the pre-filtering procedure. The robust phase gradient estimator is used to efficiently and accurately obtain phase gradient information from interferometric fringes, which is needed for the iterated unscented Kalman filtering phase unwrapping model. The efficient quality-guided strategy is able to ensure that the proposed method fast unwraps wrapped pixels along the path from the high-quality area to the low-quality area of wrapped phase images, which can greatly improve the efficiency of phase unwrapping. Results obtained from synthetic data and real data show that the proposed method can obtain better solutions with an acceptable time consumption, with respect to some of the most used algorithms.
Adaptive interplanetary orbit determination
NASA Astrophysics Data System (ADS)
Crain, Timothy Price
This work documents the development of a real-time interplanetary orbit determination monitoring algorithm for detecting and identifying changes in the spacecraft dynamic and measurement environments. The algorithm may either be utilized in a stand-alone fashion as a spacecraft monitor and hypothesis tester by navigators or may serve as a component in an autonomous adaptive orbit determination architecture. In either application, the monitoring algorithm serves to identify the orbit determination filter parameters to be modified by an offline process to restore the operational model accuracy when the spacecraft environment changes unexpectedly. The monitoring algorithm utilizes a hierarchical mixture-of-experts to regulate a multilevel bank organization of extended Kalman filters. Banks of filters operate on the hierarchy top-level and are composed of filters with configurations representative of a specific environment change called a macromode. Fine differences, or micromodes, within the macromodes are represented by individual filter configurations. Regulation is provided by two levels of single-layer neural networks called gating networks. A single top-level gating network regulates the weighting among macromodes and each bank uses a gating network to regulate member filters internally. Experiments are conducted on the Mars Pathfinder cruise trajectory environment using range and Doppler data from the Deep Space Network. The experiments investigate the ability of the hierarchical mixture-of-experts to identify three environment macromodes: (1) unmodeled impulsive maneuvers, (2) changes in the solar radiation pressure dynamics, and (3) changes in the measurement noise strength. Two methods of initializing the gating networks are examined in each experiment. One method gives the neurons associated with all filters equivalent synaptic weight. The other method places greater weight on the operational filter initially believed to model the spacecraft environment. The
EEG-fMRI fusion of paradigm-free activity using Kalman filtering.
Deneux, Thomas; Faugeras, Olivier
2010-04-01
We address here the use of EEG and fMRI, and their combination, in order to estimate the full spatiotemporal patterns of activity on the cortical surface in the absence of any particular assumptions on this activity such as stimulation times. For handling such a high-dimension inverse problem, we propose the use of (1) a global forward model of how these measures are functions of the "neural activity" of a large number of sources distributed on the cortical surface, formalized as a dynamical system, and (2) adaptive filters, as a natural solution to solve this inverse problem iteratively along the temporal dimension. This estimation framework relies on realistic physiological models, uses EEG and fMRI in a symmetric manner, and takes into account both their temporal and spatial information. We use the Kalman filter and smoother to perform such an estimation on realistic artificial data and demonstrate that the algorithm can handle the high dimensionality of these data and that it succeeds in solving this inverse problem, combining efficiently the information provided by the two modalities (this information being naturally predominantly temporal for EEG and spatial for fMRI). It performs particularly well in reconstructing a random temporally and spatially smooth activity spread over the cortex. The Kalman filter and smoother show some limitations, however, which call for the development of more specific adaptive filters. First, they do not cope well with the strong nonlinearity in the model that is necessary for an adequate description of the relation between cortical electric activities and the metabolic demand responsible for fMRI signals. Second, they fail to estimate a sparse activity (i.e., presenting sharp peaks at specific locations and times). Finally their computational cost remains high. We use schematic examples to explain these limitations and propose further developments of our method to overcome them.
Effect of the orbit eccentricity on remote sensing micro-satellite attitude Kalman filtering
NASA Astrophysics Data System (ADS)
Roubache, Rima
Remote sensing satellites are required to meet stringent pointing and drift rate requirements for imaging operations. For achieving these pointing and stability requirements, continuous and accurate three-axis attitude information is required. This paper investigates the influence of the orbit eccentricity on the performance of the attitude determination and control subsystem pointing of passive Low Earth Orbit imaging satellites stabilized by a gravity gradient boom. Any non-symmetrical object of finite dimensions in orbit is subject to a gravitational torque because of the variation in earth’s gravitational force on the object. The gravity gradient torque results from the inverse square gravitational force field. For most applications, it is sufficient to assume a spherical mass distribution for the earth. The gravity gradient torque in the case of a non-circular orbit is used in this paper. The Quaternion-based Extended Kalman Filter is analyzed when the orbit eccentricity is considered in order to determine the influence of this disturbance on the convergence and stability of the filter. The simulations in this work are based on the true parameters of ALSAT-1 which is a typical LEO satellite stabilized by a gravity gradient boom. The results show that the orbit eccentricity has a big influence on the pointing system accuracy causing micro-vibrations that affect the geocentric pointing particularly after the de-orbiting phase. In this case, satellites have no orbital correction option. The Quaternion-based Extended Kalman Filter analyzed in this paper, achieved satisfactory results for eccentricity values less than 0.4 with respect to pointing system accuracy. However, singularities were observed for eccentricity values greater than 0.4.
2012-10-17
navigation satellite sys- tems ( GNSS ) signals. However, misalignment errors in such fusion architecture are not observable in the absence of maneuvers. This...sensor measurements corrupted by a random constant model. Position and velocity errors, misalignment, accelerometer bias, rate-gyro drift and GNSS clock...errors with respect to ground-truth are estimated by means of INS/ GNSS /SD fusion and tested for statistical consistency. I. Introduction Advances in
Localization of Mobile Robots Using an Extended Kalman Filter in a LEGO NXT
ERIC Educational Resources Information Center
Pinto, M.; Moreira, A. P.; Matos, A.
2012-01-01
The inspiration for this paper comes from a successful experiment conducted with students in the "Mobile Robots" course in the fifth year of the integrated Master's program in the Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto (FEUP), Porto, Portugal. One of the topics in this Mobile Robots…
2010-12-01
help menu for details. Dx=size(x_k,1); Dy=size(Y_meas,1); NSig=2*Dx+1; sig_x=( chol ((Dx+kappa)*Pxx_k))’ %%%%%%%%%%%%%%%%%%%%% x_sig_k=x_k*ones...vector (mˆ2/s) % n - the magnitude of N % cp - cross product of N and R % RA - right ascension of the ascending node (rad) not used...NSig=2*Dx+1; sig_x= chol ((Dx+lambda)*(Pxx_k+Qbar_k))’ % Ref [3] 5a 104 chi_sig_k=x_k*ones(1,NSig)+[zeros(Dx,1) sig_x -sig_x
Orbit Determination for the Lunar Reconnaissance Orbiter Using an Extended Kalman Filter
NASA Technical Reports Server (NTRS)
Slojkowski, Steven; Lowe, Jonathan; Woodburn, James
2015-01-01
Since launch, the FDF has performed daily OD for LRO using the Goddard Trajectory Determination System (GTDS). GTDS is a batch least-squares (BLS) estimator. The tracking data arc for OD is 36 hours. Current operational OD uses 200 x 200 lunar gravity, solid lunar tides, solar radiation pressure (SRP) using a spherical spacecraft area model, and point mass gravity for the Earth, Sun, and Jupiter. LRO tracking data consists of range and range-rate measurements from: Universal Space Network (USN) stations in Sweden, Germany, Australia, and Hawaii. A NASA antenna at White Sands, New Mexico (WS1S). NASA Deep Space Network (DSN) stations. DSN data was sparse and not included in this study. Tracking is predominantly (50) from WS1S. The OD accuracy requirements are: Definitive ephemeris accuracy of 500 meters total position root-mean-squared (RMS) and18 meters radial RMS. Predicted orbit accuracy less than 800 meters root sum squared (RSS) over an 84-hour prediction span.
Applying Cooperative Localization to Swarm UAVS Using an Extended Kalman Filter
2014-09-01
relatively low cost of manufacturing, and the devel- opment and distribution of open - source control software. Advances in navigation and communications...framework for those in the robotics research community and it was developed in the open using the permissive Berkeley Software Distribution (BSD) open - source ...The 2-D distributed CL algorithm for a unicycle model as shown by [20] was first coded and tested in Python 3.3.2. The numpy and matplotlib packages
NASA Astrophysics Data System (ADS)
Chirico, G. B.; Medina, H.; Romano, N.
2014-07-01
This paper examines the potential of different algorithms, based on the Kalman filtering approach, for assimilating near-surface observations into a one-dimensional Richards equation governing soil water flow in soil. Our specific objectives are: (i) to compare the efficiency of different Kalman filter algorithms in retrieving matric pressure head profiles when they are implemented with different numerical schemes of the Richards equation; (ii) to evaluate the performance of these algorithms when nonlinearities arise from the nonlinearity of the observation equation, i.e. when surface soil water content observations are assimilated to retrieve matric pressure head values. The study is based on a synthetic simulation of an evaporation process from a homogeneous soil column. Our first objective is achieved by implementing a Standard Kalman Filter (SKF) algorithm with both an explicit finite difference scheme (EX) and a Crank-Nicolson (CN) linear finite difference scheme of the Richards equation. The Unscented (UKF) and Ensemble Kalman Filters (EnKF) are applied to handle the nonlinearity of a backward Euler finite difference scheme. To accomplish the second objective, an analogous framework is applied, with the exception of replacing SKF with the Extended Kalman Filter (EKF) in combination with a CN numerical scheme, so as to handle the nonlinearity of the observation equation. While the EX scheme is computationally too inefficient to be implemented in an operational assimilation scheme, the retrieval algorithm implemented with a CN scheme is found to be computationally more feasible and accurate than those implemented with the backward Euler scheme, at least for the examined one-dimensional problem. The UKF appears to be as feasible as the EnKF when one has to handle nonlinear numerical schemes or additional nonlinearities arising from the observation equation, at least for systems of small dimensionality as the one examined in this study.
Pipelined recurrent fuzzy neural networks for nonlinear adaptive speech prediction.
Stavrakoudis, Dimitris G; Theocharis, John B
2007-10-01
A class of pipelined recurrent fuzzy neural networks (PRFNNs) is proposed in this paper for nonlinear adaptive speech prediction. The PRFNNs are modular structures comprising a number of modules that are interconnected in a chained form. Each module is implemented by a small-scale recurrent fuzzy neural network (RFNN) with internal dynamics. Due to module nesting, the PRFNNs offer a number of desirable attributes, including decomposition of the modeling task, enhanced temporal processing capabilities, and multistage dynamic fuzzy inference. Tuning of the PRFNN adaptable parameters is accomplished by a series of gradient descent methods with different weighting of the modules and the decoupled extended Kalman filter (DEKF) algorithm, based on weight grouping. Extensive experimentation is carried out to evaluate the performance of the PRFNNs on the speech prediction platform. Comparative analysis shows that the PRFNNs outperform the single-RFNN models in terms of the prediction gains that are obtained and computational efficiency. Furthermore, PRFNNs provide considerably better performance compared to pipelined recurrent neural networks, for models with similar model complexity.
Optimal control law for classical and multiconjugate adaptive optics.
Le Roux, Brice; Conan, Jean-Marc; Kulcsár, Caroline; Raynaud, Henri-François; Mugnier, Laurent M; Fusco, Thierry
2004-07-01
Classical adaptive optics (AO) is now a widespread technique for high-resolution imaging with astronomical ground-based telescopes. It generally uses simple and efficient control algorithms. Multiconjugate adaptive optics (MCAO) is a more recent and very promising technique that should extend the corrected field of view. This technique has not yet been experimentally validated, but simulations already show its high potential. The importance for MCAO of an optimal reconstruction using turbulence spatial statistics has already been demonstrated through open-loop simulations. We propose an optimal closed-loop control law that accounts for both spatial and temporal statistics. The prior information on the turbulence, as well as on the wave-front sensing noise, is expressed in a state-space model. The optimal phase estimation is then given by a Kalman filter. The equations describing the system are given and the underlying assumptions explained. The control law is then derived. The gain brought by this approach is demonstrated through MCAO numerical simulations representative of astronomical observation on a 8-m-class telescope in the near infrared. We also discuss the application of this control approach to classical AO. Even in classical AO, the technique could be relevant especially for future extreme AO systems.
Simplification of the Kalman filter for meteorological data assimilation
NASA Technical Reports Server (NTRS)
Dee, Dick P.
1991-01-01
The paper proposes a new statistical method of data assimilation that is based on a simplification of the Kalman filter equations. The forecast error covariance evolution is approximated simply by advecting the mass-error covariance field, deriving the remaining covariances geostrophically, and accounting for external model-error forcing only at the end of each forecast cycle. This greatly reduces the cost of computation of the forecast error covariance. In simulations with a linear, one-dimensional shallow-water model and data generated artificially, the performance of the simplified filter is compared with that of the Kalman filter and the optimal interpolation (OI) method. The simplified filter produces analyses that are nearly optimal, and represents a significant improvement over OI.
Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation.
Liu, Xi; Qu, Hua; Zhao, Jihong; Yue, Pengcheng; Wang, Meng
2016-09-20
A new algorithm called maximum correntropy unscented Kalman filter (MCUKF) is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF) provides an efficient tool to solve the non-linear state estimate problem. However, the UKF usually plays well in Gaussian noises. Its performance may deteriorate substantially in the presence of non-Gaussian noises, especially when the measurements are disturbed by some heavy-tailed impulsive noises. By making use of the maximum correntropy criterion (MCC), the proposed algorithm can enhance the robustness of UKF against impulsive noises. In the MCUKF, the unscented transformation (UT) is applied to obtain a predicted state estimation and covariance matrix, and a nonlinear regression method with the MCC cost is then used to reformulate the measurement information. Finally, the UT is adopted to the measurement equation to obtain the filter state and covariance matrix. Illustrative examples demonstrate the superior performance of the new algorithm.
Star spot location estimation using Kalman filter for star tracker.
Liu, Hai-bo; Yang, Jian-kun; Wang, Jiong-qi; Tan, Ji-chun; Li, Xiu-jian
2011-04-20
Star pattern recognition and attitude determination accuracy is highly dependent on star spot location accuracy for the star tracker. A star spot location estimation approach with the Kalman filter for a star tracker has been proposed, which consists of three steps. In the proposed approach, the approximate locations of the star spots in successive frames are predicted first; then the measurement star spot locations are achieved by defining a series of small windows around each predictive star spot location. Finally, the star spot locations are updated by the designed Kalman filter. To confirm the proposed star spot location estimation approach, the simulations based on the orbit data of the CHAMP satellite and the real guide star catalog are performed. The simulation results indicate that the proposed approach can filter out noises from the measurements remarkably if the sampling frequency is sufficient.
Real time estimation of ship motions using Kalman filtering techniques
NASA Technical Reports Server (NTRS)
Triantafyllou, M. S.; Bodson, M.; Athans, M.
1983-01-01
The estimation of the heave, pitch, roll, sway, and yaw motions of a DD-963 destroyer is studied, using Kalman filtering techniques, for application in VTOL aircraft landing. The governing equations are obtained from hydrodynamic considerations in the form of linear differential equations with frequency dependent coefficients. In addition, nonminimum phase characteristics are obtained due to the spatial integration of the water wave forces. The resulting transfer matrix function is irrational and nonminimum phase. The conditions for a finite-dimensional approximation are considered and the impact of the various parameters is assessed. A detailed numerical application for a DD-963 destroyer is presented and simulations of the estimations obtained from Kalman filters are discussed.
Causal MRI reconstruction via Kalman prediction and compressed sensing correction.
Majumdar, Angshul
2017-02-04
This technical note addresses the problem of causal online reconstruction of dynamic MRI, i.e. given the reconstructed frames till the previous time instant, we reconstruct the frame at the current instant. Our work follows a prediction-correction framework. Given the previous frames, the current frame is predicted based on a Kalman estimate. The difference between the estimate and the current frame is then corrected based on the k-space samples of the current frame; this reconstruction assumes that the difference is sparse. The method is compared against prior Kalman filtering based techniques and Compressed Sensing based techniques. Experimental results show that the proposed method is more accurate than these and considerably faster.
Robust tracking with spatio-velocity snakes: Kalman filtering approach
Peterfreund, N.
1997-06-01
Using results from robust Kalman filtering, the author presents a new Kalman filter-based snake model for tracking of nonrigid objects in combined spatio-velocity space. The proposed model is the stochastic version of the velocity snake, an active contour model for combined tracking of position and velocity of nonrigid boundaries. The proposed model uses image gradient and optical flow measurements along the contour as system measurements. An optical-flow based measurement error is used to detect and reject image measurements which correspond to image clutter or to other objects. The method was applied to object tracking of both rigid and nonrigid objects, resulting in good tracking results and robustness to image clutter, occlusions and numerical noise.
Robust tracking with spatio-velocity snakes: Kalman filtering approach
Peterfreund, N.
1998-12-31
Using results from robust Kalman filtering, we present a new Kalman filter-based snake model for tracking of nonrigid objects in combined spatio-velocity space. The proposed model is the stochastic version of the velocity snake, an active contour model for combined tracking of position and velocity of nonrigid boundaries. The proposed model uses image gradient and optical flow measurements along the contour as system measurements. An optical-flow based measurement error is used to detect and reject image measurements which correspond to image clutter or to other objects. The method was applied to object tracking of both rigid and nonrigid objects, resulting in good tracking results and robustness to image clutter, occlusions and numerical noise. 19 refs., 3 figs.
Spacecraft Dynamics Should be Considered in Kalman Filter Attitude Estimation
NASA Technical Reports Server (NTRS)
Yang, Yaguang; Zhou, Zhiqiang
2016-01-01
Kalman filter based spacecraft attitude estimation has been used in some high-profile missions and has been widely discussed in literature. While some models in spacecraft attitude estimation include spacecraft dynamics, most do not. To our best knowledge, there is no comparison on which model is a better choice. In this paper, we discuss the reasons why spacecraft dynamics should be considered in the Kalman filter based spacecraft attitude estimation problem. We also propose a reduced quaternion spacecraft dynamics model which admits additive noise. Geometry of the reduced quaternion model and the additive noise are discussed. This treatment is more elegant in mathematics and easier in computation. We use some simulation example to verify our claims.
Wu, Tao; Chen, Weidong; Kerstel, Erik; Fertein, Eric; Gao, Xiaoming; Koeth, Johannes; Rössner, Karl; Brückner, Daniela
2010-03-01
Kalman adaptive filtering was applied for the first time, to our knowledge, to the real-time simultaneous determination of water isotopic ratios using laser absorption spectroscopy at 2.73 microm. Measurements of the oxygen and hydrogen isotopologue ratios delta(18)O, delta(17)O, and delta(2)H in water showed a 1-sigma precision of 0.72 per thousand for delta(18)O, 0.48 per thousand for delta(17)O, and 0.84 per thousand for delta(2)H, while sampling the output of the tuned Kalman filter at 1 s time intervals. Using a standard running average technique, averaging over approximately 30 s is required to obtain the same level of precision.
Application of Kalman filtering to spacecraft range residual prediction
NASA Technical Reports Server (NTRS)
Madrid, G. A.; Bierman, G. J.
1977-01-01
One function of the Deep Space Network is validation of the range data that they receive. In this paper we present an automated online sequential range predictor which shows promise of significantly reducing computational and manpower expenditures. The proposed algorithm, a U-D covariance factored Kalman filter, is demonstrated by processing a four-month record of Viking spacecraft data taken enroute to Mars.
Design of Linear Quadratic Regulators and Kalman Filters
NASA Technical Reports Server (NTRS)
Lehtinen, B.; Geyser, L.
1986-01-01
AESOP solves problems associated with design of controls and state estimators for linear time-invariant systems. Systems considered are modeled in state-variable form by set of linear differential and algebraic equations with constant coefficients. Two key problems solved by AESOP are linear quadratic regulator (LQR) design problem and steady-state Kalman filter design problem. AESOP is interactive. User solves design problems and analyzes solutions in single interactive session. Both numerical and graphical information available to user during the session.
Three-dimensional motion tracking by Kalman filtering
NASA Astrophysics Data System (ADS)
Gao, Jean; Kosaka, Akio; Kak, Avinash C.
2000-10-01
In this paper, a 3D semantic object motion tracking method based on Kalman filtering is proposed. First, we use a specially designed Color Image Segmentation Editor (CISE) to devise shapes that more accurately describe the object to be tracked. CISE is an integration of edge and region detection, which is based on edge-linking, split-and-merge and the energy minimization for active contour detection. An ROI is further segmented into single motion blobs by considering the constancy of the motion parameters in each blob. Over short time intervals, each blob can be tracked separately and, over longer times, the blobs can be allowed to fragment and coalesce into new blobs as motion evolves. The tracking of each blob is based on a Kalman filter derived from linearization of a constraint equation satisfied by the pinhole model of a camera. The Kalman filter allows the tracker to project the uncertainties associated with a blob center (or with the coordinates of any other features) into the next frame. This projected uncertainty region can then be searched rot eh pixels belonging to the blob. Future work includes investigation of the effects of illumination changes and simultaneous tracking of multiple targets.
Unscented Kalman Filter for Brain-Machine Interfaces
Li, Zheng; O'Doherty, Joseph E.; Hanson, Timothy L.; Lebedev, Mikhail A.; Henriquez, Craig S.; Nicolelis, Miguel A. L.
2009-01-01
Brain machine interfaces (BMIs) are devices that convert neural signals into commands to directly control artificial actuators, such as limb prostheses. Previous real-time methods applied to decoding behavioral commands from the activity of populations of neurons have generally relied upon linear models of neural tuning and were limited in the way they used the abundant statistical information contained in the movement profiles of motor tasks. Here, we propose an n-th order unscented Kalman filter which implements two key features: (1) use of a non-linear (quadratic) model of neural tuning which describes neural activity significantly better than commonly-used linear tuning models, and (2) augmentation of the movement state variables with a history of n-1 recent states, which improves prediction of the desired command even before incorporating neural activity information and allows the tuning model to capture relationships between neural activity and movement at multiple time offsets simultaneously. This new filter was tested in BMI experiments in which rhesus monkeys used their cortical activity, recorded through chronically implanted multielectrode arrays, to directly control computer cursors. The 10th order unscented Kalman filter outperformed the standard Kalman filter and the Wiener filter in both off-line reconstruction of movement trajectories and real-time, closed-loop BMI operation. PMID:19603074
A Robust Kalman Framework with Resampling and Optimal Smoothing
Kautz, Thomas; Eskofier, Bjoern M.
2015-01-01
The Kalman filter (KF) is an extremely powerful and versatile tool for signal processing that has been applied extensively in various fields. We introduce a novel Kalman-based analysis procedure that encompasses robustness towards outliers, Kalman smoothing and real-time conversion from non-uniformly sampled inputs to a constant output rate. These features have been mostly treated independently, so that not all of their benefits could be exploited at the same time. Here, we present a coherent analysis procedure that combines the aforementioned features and their benefits. To facilitate utilization of the proposed methodology and to ensure optimal performance, we also introduce a procedure to calculate all necessary parameters. Thereby, we substantially expand the versatility of one of the most widely-used filtering approaches, taking full advantage of its most prevalent extensions. The applicability and superior performance of the proposed methods are demonstrated using simulated and real data. The possible areas of applications for the presented analysis procedure range from movement analysis over medical imaging, brain-computer interfaces to robot navigation or meteorological studies. PMID:25734647
Relationship between Allan variances and Kalman Filter parameters
NASA Technical Reports Server (NTRS)
Vandierendonck, A. J.; Mcgraw, J. B.; Brown, R. G.
1984-01-01
A relationship was constructed between the Allan variance parameters (H sub z, H sub 1, H sub 0, H sub -1 and H sub -2) and a Kalman Filter model that would be used to estimate and predict clock phase, frequency and frequency drift. To start with the meaning of those Allan Variance parameters and how they are arrived at for a given frequency source is reviewed. Although a subset of these parameters is arrived at by measuring phase as a function of time rather than as a spectral density, they all represent phase noise spectral density coefficients, though not necessarily that of a rational spectral density. The phase noise spectral density is then transformed into a time domain covariance model which can then be used to derive the Kalman Filter model parameters. Simulation results of that covariance model are presented and compared to clock uncertainties predicted by Allan variance parameters. A two state Kalman Filter model is then derived and the significance of each state is explained.
Spitzer Instrument Pointing Frame (IPF) Kalman Filter Algorithm
NASA Technical Reports Server (NTRS)
Bayard, David S.; Kang, Bryan H.
2004-01-01
This paper discusses the Spitzer Instrument Pointing Frame (IPF) Kalman Filter algorithm. The IPF Kalman filter is a high-order square-root iterated linearized Kalman filter, which is parametrized for calibrating the Spitzer Space Telescope focal plane and aligning the science instrument arrays with respect to the telescope boresight. The most stringent calibration requirement specifies knowledge of certain instrument pointing frames to an accuracy of 0.1 arcseconds, per-axis, 1-sigma relative to the Telescope Pointing Frame. In order to achieve this level of accuracy, the filter carries 37 states to estimate desired parameters while also correcting for expected systematic errors due to: (1) optical distortions, (2) scanning mirror scale-factor and misalignment, (3) frame alignment variations due to thermomechanical distortion, and (4) gyro bias and bias-drift in all axes. The resulting estimated pointing frames and calibration parameters are essential for supporting on-board precision pointing capability, in addition to end-to-end 'pixels on the sky' ground pointing reconstruction efforts.
Statistical process control of a Kalman filter model.
Gamse, Sonja; Nobakht-Ersi, Fereydoun; Sharifi, Mohammad A
2014-09-26
For the evaluation of measurement data, different functional and stochastic models can be used. In the case of time series, a Kalman filtering (KF) algorithm can be implemented. In this case, a very well-known stochastic model, which includes statistical tests in the domain of measurements and in the system state domain, is used. Because the output results depend strongly on input model parameters and the normal distribution of residuals is not always fulfilled, it is very important to perform all possible tests on output results. In this contribution, we give a detailed description of the evaluation of the Kalman filter model. We describe indicators of inner confidence, such as controllability and observability, the determinant of state transition matrix and observing the properties of the a posteriori system state covariance matrix and the properties of the Kalman gain matrix. The statistical tests include the convergence of standard deviations of the system state components and normal distribution beside standard tests. Especially, computing controllability and observability matrices and controlling the normal distribution of residuals are not the standard procedures in the implementation of KF. Practical implementation is done on geodetic kinematic observations.
Acoustic cardiac signals analysis: a Kalman filter-based approach.
Salleh, Sheik Hussain; Hussain, Hadrina Sheik; Swee, Tan Tian; Ting, Chee-Ming; Noor, Alias Mohd; Pipatsart, Surasak; Ali, Jalil; Yupapin, Preecha P
2012-01-01
Auscultation of the heart is accompanied by both electrical activity and sound. Heart auscultation provides clues to diagnose many cardiac abnormalities. Unfortunately, detection of relevant symptoms and diagnosis based on heart sound through a stethoscope is difficult. The reason GPs find this difficult is that the heart sounds are of short duration and separated from one another by less than 30 ms. In addition, the cost of false positives constitutes wasted time and emotional anxiety for both patient and GP. Many heart diseases cause changes in heart sound, waveform, and additional murmurs before other signs and symptoms appear. Heart-sound auscultation is the primary test conducted by GPs. These sounds are generated primarily by turbulent flow of blood in the heart. Analysis of heart sounds requires a quiet environment with minimum ambient noise. In order to address such issues, the technique of denoising and estimating the biomedical heart signal is proposed in this investigation. Normally, the performance of the filter naturally depends on prior information related to the statistical properties of the signal and the background noise. This paper proposes Kalman filtering for denoising statistical heart sound. The cycles of heart sounds are certain to follow first-order Gauss-Markov process. These cycles are observed with additional noise for the given measurement. The model is formulated into state-space form to enable use of a Kalman filter to estimate the clean cycles of heart sounds. The estimates obtained by Kalman filtering are optimal in mean squared sense.
The Navstar GPS master control station's Kalman filter experience
NASA Technical Reports Server (NTRS)
Scardera, Michael P.
1990-01-01
The Navstar Global Positioning System (GPS) is a highly accurate space based navigation system providing all weather, 24 hour a day service to both military and civilian users. The system provides a Gaussian position solution with four satellites, each providing its ephemeris and clock offset with respect to GPS time. The GPS Master Clock Station (MCS) is charged with tracking each Navstar spacecraft and precisely defining the ephemeris and clock parameters for upload into the vehicle's navigation message. Briefly described here are the Navstar system and the Kalman filter estimation process used by MCS to determine, predict, and ensure quality control for each of the satellite's ephemeris and clock states. Routine performance is shown. Kalman filter reaction and response is discussed for anomalous clock behavior and trajectory perturbations. Particular attention is given to MCS efforts to improve orbital adjust modeling. The satellite out of service time due to orbital maneuvering has been reduced in the past year from four days to under twelve hours. The planning, reference trajectory model, and Kalman filter management improvements are explained.
Kalman filter analysis of delayed neutron nondestructive assay measurements.
Aumeier, S. E.
1998-04-29
The ability to nondestructively determine the presence and quantity of fissile and fertile nuclei in various matrices is important in several nuclear applications including international and domestics safeguards, radioactive waste characterization and nuclear facility operations. Material irradiation followed by delayed neutron counting is a well known and useful nondestructive assay technique used to determine the fissile-effective content of assay samples. Previous studies have demonstrated the feasibility of using Kalman filters to unfold individual isotopic contributions to delayed neutron measurements resulting from the assay of mixes of uranium and plutonium isotopes. However, the studies in question used simulated measurement data and idealized parameters. We present the results of the Kalman filter analysis of several measurements of U/Pu mixes taken using Argonne National Laboratory's delayed neutron nondestructive assay device. The results demonstrate the use of Kalman filters as a signal processing tool to determine the fissile and fertile isotopic content of an assay sample from the aggregate delayed neutron response following neutron irradiation.
Improved understanding of the loss-of-symmetry phenomenon in the conventional Kalman filter
NASA Technical Reports Server (NTRS)
Verhaegen, M. H.
1989-01-01
This paper corrects an unclear statement of the conventional Kalman filter implementation as presented by M. H. Verhaegen and P. van Dooren in Numerical aspects of different Kalman filter implementations, IEEE Trans. Automat. Contr., v. AC-31, no. 10, pp. 907-917, 1986. It is shown that the habitual, incorrect implementation of the Kalman filter has been the major cause of its insensitivity to the so-called loss-of-symmetry phenomenon.
Determination of High-Speed Multiple Threat Using Kalman Filter Analysis of Maritime Movement
2015-06-01
HIGH-SPEED MULTIPLE THREAT USING KALMAN FILTER ANALYSIS OF MARITIME MOVEMENT by Joseph L. Carnes June 2015 Thesis Advisor: David A. Garren...USING KALMAN FILTER ANALYSIS OF MARITIME MOVEMENT 5. FUNDING NUMBERS 6. AUTHOR(S) Joseph L. Carnes 7. PERFORMING ORGANIZATION NAME(S) AND...is unlimited DETERMINATION OF HIGH-SPEED MULTIPLE THREAT USING KALMAN FILTER ANALYSIS OF MARITIME MOVEMENT Joseph L. Carnes Submitted in
Improved understanding of the loss-of-symmetry phenomenon in the conventional Kalman filter
NASA Technical Reports Server (NTRS)
Verhaegen, M. H.
1987-01-01
This paper corrects an unclear treatment of the conventional Kalman filter implementation as presented by M. H. Verhaegen and P. van Dooren in Numerical aspects of different Kalman filter implementations, IEEE Trans. Automat. Contr., v. AC-31, no. 10, pp. 907-917, 1986. It is shown that habitual, incorrect implementation of the Kalman filter has been the major cause of its sensitivity to the so-called loss-of-symmetry phenomenon.
Enhancement of Spanish Oesophageal Speech vowels using coherent subband modulator Kalman filtering.
Ishaq, Rizwan; Zapirain, Begoña García
2016-01-01
This paper proposes an Oesophageal Speech (OES) enhancement method, based on Kalman filtering. The Kalman filter is applied to modulators of OES frequency subbands instead of the fullband signal. The OES frequency subbands are decomposed into modulators and carriers components using coherent demodulation. In comparison with fullband Kalman filtering and pole stabilization, the proposed technique shows better results. The system performance is evaluated objectively and subjectively using the Harmonic to Noise Ratio (HNR) and Mean Opinion Score (MOS) respectively. Results have shown that Kalman filter in subband modulators processing is robust and efficient, improving the HNR by 4 to 5 dB for all Spanish vowels.
Luo, Yingting; Zhu, Yunmin; Luo, Dandan; Zhou, Jie; Song, Enbin; Wang, Donghua
2008-12-08
This paper proposes a new distributed Kalman filtering fusion with random state transition and measurement matrices, i.e., random parameter matrices Kalman filtering. It is proved that under a mild condition the fused state estimate is equivalent to the centralized Kalman filtering using all sensor measurements; therefore, it achieves the best performance. More importantly, this result can be applied to Kalman filtering with uncertain observations including the measurement with a false alarm probability as a special case, as well as, randomly variant dynamic systems with multiple models. Numerical examples are given which support our analysis and show significant performance loss of ignoring the randomness of the parameter matrices.
Luo, Yingting; Zhu, Yunmin; Luo, Dandan; Zhou, Jie; Song, Enbin; Wang, Donghua
2008-01-01
This paper proposes a new distributed Kalman filtering fusion with random state transition and measurement matrices, i.e., random parameter matrices Kalman filtering. It is proved that under a mild condition the fused state estimate is equivalent to the centralized Kalman filtering using all sensor measurements; therefore, it achieves the best performance. More importantly, this result can be applied to Kalman filtering with uncertain observations including the measurement with a false alarm probability as a special case, as well as, randomly variant dynamic systems with multiple models. Numerical examples are given which support our analysis and show significant performance loss of ignoring the randomness of the parameter matrices. PMID:27873977
A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation
Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao
2016-01-01
The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms. PMID:27999361
Identification of time-variant river bed properties with the ensemble Kalman filter
NASA Astrophysics Data System (ADS)
Kurtz, Wolfgang; Hendricks Franssen, Harrie-Jan; Vereecken, Harry
2012-10-01
An adequate characterization of river bed hydraulic conductivities (L) is crucial for a proper assessment of river-aquifer interactions. However, river bed characteristics may change over time due to dynamic morphological processes like scouring or sedimentation what can lead to erroneous model predictions when static leakage parameters are assumed. Sequential data assimilation with the ensemble Kalman filter (EnKF) allows for an update of model parameters in real-time and may thus be capable of assessing the transient behavior of L. Synthetic experiments with a three-dimensional finite element model of the Limmat aquifer in Zurich were used to assess the performance of data assimilation in capturing time-variant river bed properties. Reference runs were generated where L followed different temporal and/or spatial patterns which should mimic real-world sediment dynamics. Hydraulic head (h) data from these reference runs were then used as input data for EnKF which jointly updated h and L. Results showed that EnKF is able to capture the different spatio-temporal patterns of L in the reference runs well. However, the adaptation time was relatively long which was attributed to the fast decrease of ensemble variance. To improve the performance of EnKF also an adaptive filtering approach with covariance inflation was applied that allowed a faster and more accurate adaptation of model parameters. A sensitivity analysis indicated that even for a low amount of observations a reasonable adaptation of L towards the reference values can be achieved and that EnKF is also able to correct for a biased initial ensemble of L.
Filter accuracy for the Lorenz 96 model: Fixed versus adaptive observation operators
Stuart, Andrew M.; Shukla, Abhishek; Sanz-Alonso, Daniel; Law, K. J. H.
2016-02-23
In the context of filtering chaotic dynamical systems it is well-known that partial observations, if sufficiently informative, can be used to control the inherent uncertainty due to chaos. The purpose of this paper is to investigate, both theoretically and numerically, conditions on the observations of chaotic systems under which they can be accurately filtered. In particular, we highlight the advantage of adaptive observation operators over fixed ones. The Lorenz ’96 model is used to exemplify our findings. Here, we consider discrete-time and continuous-time observations in our theoretical developments. We prove that, for fixed observation operator, the 3DVAR filter can recover the system state within a neighbourhood determined by the size of the observational noise. It is required that a sufficiently large proportion of the state vector is observed, and an explicit form for such sufficient fixed observation operator is given. Numerical experiments, where the data is incorporated by use of the 3DVAR and extended Kalman filters, suggest that less informative fixed operators than given by our theory can still lead to accurate signal reconstruction. Adaptive observation operators are then studied numerically; we show that, for carefully chosen adaptive observation operators, the proportion of the state vector that needs to be observed is drastically smaller than with a fixed observation operator. Indeed, we show that the number of state coordinates that need to be observed may even be significantly smaller than the total number of positive Lyapunov exponents of the underlying system.
Estimating Model Parameters of Adaptive Software Systems in Real-Time
NASA Astrophysics Data System (ADS)
Kumar, Dinesh; Tantawi, Asser; Zhang, Li
Adaptive software systems have the ability to adapt to changes in workload and execution environment. In order to perform resource management through model based control in such systems, an accurate mechanism for estimating the software system's model parameters is required. This paper deals with real-time estimation of a performance model for adaptive software systems that process multiple classes of transactional workload. First, insights in to the static performance model estimation problem are provided. Then an Extended Kalman Filter (EKF) design is combined with an open queueing network model to dynamically estimate the model parameters in real-time. Specific problems that are encountered in the case of multiple classes of workload are analyzed. These problems arise mainly due to the under-deterministic nature of the estimation problem. This motivates us to propose a modified design of the filter. Insights for choosing tuning parameters of the modified design, i.e., number of constraints and sampling intervals are provided. The modified filter design is shown to effectively tackle problems with multiple classes of workload through experiments.
NASA Technical Reports Server (NTRS)
Kucuk, Senol
1988-01-01
Importance of the role of human operator in control systems has led to the particular area of manual control theory. Human describing functions were developed to model human behavior for manual control studies to take advantage of the successful and safe human operations. A single variable approach is presented that can be extended for multi-variable tasks where a low order human response model is used together with its rules, to adapt the model on-line, being capable of responding to the changes in the controlled element dynamics. Basic control theory concepts are used to combine the model, constrained with the physical observations, particularly, for the case of aircraft control. Pilot experience is represented as the initial model parameters. An adaptive root-locus method is presented as the adaptation law of the model where the closed loop bandwidth of the system is to be preserved in a stable manner with the adjustments of the pilot handling qualities which relate the latter to the closed loop bandwidth and damping of the closed loop pilot aircraft combination. A Kalman filter parameter estimator is presented as the controlled element identifier of the adaptive model where any discrepancies of the open loop dynamics from the presented one, are sensed to be compensated.
Time-frequency analysis of band-limited EEG with BMFLC and Kalman filter for BCI applications
2013-01-01
Background Time-Frequency analysis of electroencephalogram (EEG) during different mental tasks received significant attention. As EEG is non-stationary, time-frequency analysis is essential to analyze brain states during different mental tasks. Further, the time-frequency information of EEG signal can be used as a feature for classification in brain-computer interface (BCI) applications. Methods To accurately model the EEG, band-limited multiple Fourier linear combiner (BMFLC), a linear combination of truncated multiple Fourier series models is employed. A state-space model for BMFLC in combination with Kalman filter/smoother is developed to obtain accurate adaptive estimation. By virtue of construction, BMFLC with Kalman filter/smoother provides accurate time-frequency decomposition of the bandlimited signal. Results The proposed method is computationally fast and is suitable for real-time BCI applications. To evaluate the proposed algorithm, a comparison with short-time Fourier transform (STFT) and continuous wavelet transform (CWT) for both synthesized and real EEG data is performed in this paper. The proposed method is applied to BCI Competition data IV for ERD detection in comparison with existing methods. Conclusions Results show that the proposed algorithm can provide optimal time-frequency resolution as compared to STFT and CWT. For ERD detection, BMFLC-KF outperforms STFT and BMFLC-KS in real-time applicability with low computational requirement. PMID:24274109
NASA Astrophysics Data System (ADS)
Aguirre, Luis Antonio; Teixeira, Bruno Otávio S.; Tôrres, Leonardo Antônio B.
2005-08-01
This paper addresses the problem of state estimation for nonlinear systems by means of the unscented Kalman filter (UKF). Compared to the traditional extended Kalman filter, the UKF does not require the local linearization of the system equations used in the propagation stage. Important results using the UKF have been reported recently but in every case the system equations used by the filter were considered known. Not only that, such models are usually considered to be differential equations, which requires that numerical integration be performed during the propagation phase of the filter. In this paper the dynamical equations of the system are taken to be difference equations—thus avoiding numerical integration—and are built from data without prior knowledge. The identified models are subsequently implemented in the filter in order to accomplish state estimation. The paper discusses the impact of not knowing the exact equations and using data-driven models in the context of state and joint state-and-parameter estimation. The procedure is illustrated by means of examples that use simulated and measured data.
Improved Kalman filter method for measurement noise reduction in multi sensor RFID systems.
Eom, Ki Hwan; Lee, Seung Joon; Kyung, Yeo Sun; Lee, Chang Won; Kim, Min Chul; Jung, Kyung Kwon
2011-01-01
Recently, the range of available radio frequency identification (RFID) tags has been widened to include smart RFID tags which can monitor their varying surroundings. One of the most important factors for better performance of smart RFID system is accurate measurement from various sensors. In the multi-sensing environment, some noisy signals are obtained because of the changing surroundings. We propose in this paper an improved Kalman filter method to reduce noise and obtain correct data. Performance of Kalman filter is determined by a measurement and system noise covariance which are usually called the R and Q variables in the Kalman filter algorithm. Choosing a correct R and Q variable is one of the most important design factors for better performance of the Kalman filter. For this reason, we proposed an improved Kalman filter to advance an ability of noise reduction of the Kalman filter. The measurement noise covariance was only considered because the system architecture is simple and can be adjusted by the neural network. With this method, more accurate data can be obtained with smart RFID tags. In a simulation the proposed improved Kalman filter has 40.1%, 60.4% and 87.5% less mean squared error (MSE) than the conventional Kalman filter method for a temperature sensor, humidity sensor and oxygen sensor, respectively. The performance of the proposed method was also verified with some experiments.
Four-dimensional Localization and the Iterative Ensemble Kalman Smoother
NASA Astrophysics Data System (ADS)
Bocquet, M.
2015-12-01
The iterative ensemble Kalman smoother (IEnKS) is a data assimilation method meant for efficiently tracking the state ofnonlinear geophysical models. It combines an ensemble of model states to estimate the errors similarly to the ensemblesquare root Kalman filter, with a 4D-variational analysis performed within the ensemble space. As such it belongs tothe class of ensemble variational methods. Recently introduced 4DEnVar or the 4D-LETKF can be seen as particular casesof the scheme. The IEnKS was shown to outperform 4D-Var, the ensemble Kalman filter (EnKF) and smoother, with low-ordermodels in all investigated dynamical regimes. Like any ensemble method, it could require the use of localization of theanalysis when the state space dimension is high. However, localization for the IEnKS is not as straightforward as forthe EnKF. Indeed, localization needs to be defined across time, and it needs to be as much as possible consistent withthe dynamical flow within the data assimilation variational window. We show that a Liouville equation governs the timeevolution of the localization operator, which is linked to the evolution of the error correlations. It is argued thatits time integration strongly depends on the forecast dynamics. Using either covariance localization or domainlocalization, we propose and test several localization strategies meant to address the issue: (i) a constant and uniformlocalization, (ii) the propagation through the window of a restricted set of dominant modes of the error covariancematrix, (iii) the approximate propagation of the localization operator using model covariant local domains. Theseschemes are illustrated on the one-dimensional Lorenz 40-variable model.
Automatic Certification of Kalman Filters for Reliable Code Generation
NASA Technical Reports Server (NTRS)
Denney, Ewen; Fischer, Bernd; Schumann, Johann; Richardson, Julian
2005-01-01
AUTOFILTER is a tool for automatically deriving Kalman filter code from high-level declarative specifications of state estimation problems. It can generate code with a range of algorithmic characteristics and for several target platforms. The tool has been designed with reliability of the generated code in mind and is able to automatically certify that the code it generates is free from various error classes. Since documentation is an important part of software assurance, AUTOFILTER can also automatically generate various human-readable documents, containing both design and safety related information. We discuss how these features address software assurance standards such as DO-178B.
Optimal subband Kalman filter for normal and oesophageal speech enhancement.
Ishaq, Rizwan; García Zapirain, Begoña
2014-01-01
This paper presents the single channel speech enhancement system using subband Kalman filtering by estimating optimal Autoregressive (AR) coefficients and variance for speech and noise, using Weighted Linear Prediction (WLP) and Noise Weighting Function (NWF). The system is applied for normal and Oesophageal speech signals. The method is evaluated by Perceptual Evaluation of Speech Quality (PESQ) score and Signal to Noise Ratio (SNR) improvement for normal speech and Harmonic to Noise Ratio (HNR) for Oesophageal Speech (OES). Compared with previous systems, the normal speech indicates 30% increase in PESQ score, 4 dB SNR improvement and OES shows 3 dB HNR improvement.
Telescope Multi-Field Wavefront Control with a Kalman Filter
NASA Technical Reports Server (NTRS)
Lou, John Z.; Redding, David; Sigrist, Norbert; Basinger, Scott
2008-01-01
An effective multi-field wavefront control (WFC) approach is demonstrated for an actuated, segmented space telescope using wavefront measurements at the exit pupil, and the optical and computational implications of this approach are discussed. The integration of a Kalman Filter as an optical state estimator into the wavefront control process to further improve the robustness of the optical alignment of the telescope will also be discussed. Through a comparison of WFC performances between on-orbit and ground-test optical system configurations, the connection (and a possible disconnection) between WFC and optical system alignment under these circumstances are analyzed. Our MACOS-based computer simulation results will be presented and discussed.
Weighted Average Consensus-Based Unscented Kalman Filtering.
Li, Wangyan; Wei, Guoliang; Han, Fei; Liu, Yurong
2016-02-01
In this paper, we are devoted to investigate the consensus-based distributed state estimation problems for a class of sensor networks within the unscented Kalman filter (UKF) framework. The communication status among sensors is represented by a connected undirected graph. Moreover, a weighted average consensus-based UKF algorithm is developed for the purpose of estimating the true state of interest, and its estimation error is bounded in mean square which has been proven in the following section. Finally, the effectiveness of the proposed consensus-based UKF algorithm is validated through a simulation example.
Leakage estimation using Kalman filtering in noninvasive mechanical ventilation.
Rodrigues, G G; Freitas, U S; Bounoiare, D; Aguirre, L A; Letellier, C
2013-05-01
Noninvasive mechanical ventilation is today often used to assist patient with chronic respiratory failure. One of the main reasons evoked to explain asynchrony events, discomfort, unwillingness to be treated, etc., is the occurrence of nonintentional leaks in the ventilation circuit, which are difficult to account for because they are not measured. This paper describes a solution to the problem of variable leakage estimation based on a Kalman filter driven by airflow and the pressure signals, both of which are available in the ventilation circuit. The filter was validated by showing that based on the attained leakage estimates, practically all the untriggered cycles can be explained.
Friction coefficient estimation using an unscented Kalman filter
NASA Astrophysics Data System (ADS)
Zhao, Yunshi; Liang, Bo; Iwnicki, Simon
2014-05-01
The friction coefficient between a railway wheel and rail surface is a crucial factor in maintaining high acceleration and braking performance of railway vehicles; therefore, monitoring this friction coefficient is important. Due to the difficulty in directly measuring the friction coefficient, the creep force or creepage, indirect methods using state observers are used more frequently. This paper presents an approach using an unscented kalman filter to estimate the creep force and creepage and the friction coefficient from traction motor behaviours. A scaled roller rig is designed and a series of experiments is carried out to evaluate the estimator performance.
[Application of kalman filtering based on wavelet transform in ICP-AES].
Qin, Xia; Shen, Lan-sun
2002-12-01
Kalman filtering is a recursive algorithm, which has been proposed as an attractive alternative to correct overlapping interferences in ICP-AES. However, the noise in ICP-AES contaminates the signal arising from the analyte and hence limits the accuracy of kalman filtering. Wavelet transform is a powerful technique in signal denoising due to its multi-resolution characteristics. In this paper, first, the effect of noise on kalman filtering is discussed. Then we apply the wavelet-transform-based soft-thresholding as the pre-processing of kalman filtering. The simulation results show that the kalman filtering based on wavelet transform can effectively reduce the noise and increase the accuracy of the analysis.
NASA Technical Reports Server (NTRS)
Snow, Frank; Harman, Richard; Garrick, Joseph
1988-01-01
The Gamma Ray Observatory (GRO) spacecraft needs a highly accurate attitude knowledge to achieve its mission objectives. Utilizing the fixed-head star trackers (FHSTs) for observations and gyroscopes for attitude propagation, the discrete Kalman Filter processes the attitude data to obtain an onboard accuracy of 86 arc seconds (3 sigma). A combination of linear analysis and simulations using the GRO Software Simulator (GROSS) are employed to investigate the Kalman filter for stability and the effects of corrupted observations (misalignment, noise), incomplete dynamic modeling, and nonlinear errors on Kalman filter. In the simulations, on-board attitude is compared with true attitude, the sensitivity of attitude error to model errors is graphed, and a statistical analysis is performed on the residuals of the Kalman Filter. In this paper, the modeling and sensor errors that degrade the Kalman filter solution beyond mission requirements are studied, and methods are offered to identify the source of these errors.
NASA Technical Reports Server (NTRS)
Ballabrera-Poy, J.; Busalacchi, A.; Murtugudde, R.
2000-01-01
A reduced order Kalman Filter, based on a simplification of the Singular Evolutive Extended Kalman (SEEK) filter equations, is used to assimilate observed fields of the surface wind stress, sea surface temperature and sea level into the nonlinear coupled ocean-atmosphere model of Zebiak and Cane. The SEEK filter projects the Kalman Filter equations onto a subspace defined by the eigenvalue decomposition of the error forecast matrix, allowing its application to high dimensional systems. The Zebiak and Cane model couples a linear reduced gravity ocean model with a single vertical mode atmospheric model of Zebiak. The compatibility between the simplified physics of the model and each observed variable is studied separately and together. The results show the ability of the model to represent the simultaneous value of the wind stress, SST and sea level, when the fields are limited to the latitude band 10 deg S - 10 deg N In this first application of the Kalman Filter to a coupled ocean-atmosphere prediction model, the sea level fields are assimilated in terms of the Kelvin and Rossby modes of the thermocline depth anomaly. An estimation of the error of these modes is derived from the projection of an estimation of the sea level error over such modes. This method gives a value of 12 for the error of the Kelvin amplitude, and 6 m of error for the Rossby component of the thermocline depth. The ability of the method to reconstruct the state of the equatorial Pacific and predict its time evolution is demonstrated. The method is shown to be quite robust for predictions up to six months, and able to predict the onset of the 1997 warm event fifteen months before its occurrence.
NASA Technical Reports Server (NTRS)
Ballabrera-Poy, Joaquim; Busalacchi, Antonio J.; Murtugudde, Ragu
2000-01-01
A reduced order Kalman Filter, based on a simplification of the Singular Evolutive Extended Kalman (SEEK) filter equations, is used to assimilate observed fields of the surface wind stress, sea surface temperature and sea level into the nonlinear coupled ocean-atmosphere model. The SEEK filter projects the Kalman Filter equations onto a subspace defined by the eigenvalue decomposition of the error forecast matrix, allowing its application to high dimensional systems. The Zebiak and Cane model couples a linear reduced gravity ocean model with a single vertical mode atmospheric model of Zebiak. The compatibility between the simplified physics of the model and each observed variable is studied separately and together. The results show the ability of the model to represent the simultaneous value of the wind stress, SST and sea level, when the fields are limited to the latitude band 10 deg S - 10 deg N. In this first application of the Kalman Filter to a coupled ocean-atmosphere prediction model, the sea level fields are assimilated in terms of the Kelvin and Rossby modes of the thermocline depth anomaly. An estimation of the error of these modes is derived from the projection of an estimation of the sea level error over such modes. This method gives a value of 12 for the error of the Kelvin amplitude, and 6 m of error for the Rossby component of the thermocline depth. The ability of the method to reconstruct the state of the equatorial Pacific and predict its time evolution is demonstrated. The method is shown to be quite robust for predictions I up to six months, and able to predict the onset of the 1997 warm event fifteen months before its occurrence.
NASA Technical Reports Server (NTRS)
Hacker, Scott C. (Inventor); Dean, Richard J. (Inventor); Burge, Scott W. (Inventor); Dartez, Toby W. (Inventor)
2007-01-01
An adapter for installing a connector to a terminal post, wherein the connector is attached to a cable, is presented. In an embodiment, the adapter is comprised of an elongated collet member having a longitudinal axis comprised of a first collet member end, a second collet member end, an outer collet member surface, and an inner collet member surface. The inner collet member surface at the first collet member end is used to engage the connector. The outer collet member surface at the first collet member end is tapered for a predetermined first length at a predetermined taper angle. The collet includes a longitudinal slot that extends along the longitudinal axis initiating at the first collet member end for a predetermined second length. The first collet member end is formed of a predetermined number of sections segregated by a predetermined number of channels and the longitudinal slot.
System identification, adaptive control and formation driving of farm tractors
NASA Astrophysics Data System (ADS)
Rekow, Andrew Karl Wilhelm
Great increases in agricultural productivity and profitability can be gained by increasing the navigational control accuracy of a farm tractor. To maximize accuracy in the presence of environmental uncertainties, a novel technique for on-line parameter identification has been developed. This method combines the Extended Kalman Filter (EKF) and the Least Mean Square (LMS) algorithms and is used to identify key parameters which describe the dynamics of a farm tractor. This algorithm provides a 15:1 improvement in computational efficiency over the traditional EKF, while offering comparable convergence rates and noise rejection properties. Experimental data on a full-sized John Deere tractor shows a 25 percent improvement in lateral accuracy when using then adaptive controller versus a fixed controller over identical trajectories. In addition to parameter identification, farmers require formation driving capability for routine operations. Multiple farm vehicles work cooperatively together to accomplish a common goal. Several formation driving algorithms were developed for these varying requirements. An experimental implementation of a fully autonomous farm vehicle following a human operated lead vehicle demonstrated an accuracy of 10 centimeters in the in-track direction and 10 centimeters in the cross track direction.
Data Assimilation in the ADAPT Photospheric Flux Transport Model
NASA Astrophysics Data System (ADS)
Hickmann, Kyle S.; Godinez, Humberto C.; Henney, Carl J.; Arge, C. Nick
2015-04-01
Global maps of the solar photospheric magnetic flux are fundamental drivers for simulations of the corona and solar wind and therefore are important predictors of geoeffective events. However, observations of the solar photosphere are only made intermittently over approximately half of the solar surface. The Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model uses localized ensemble Kalman filtering techniques to adjust a set of photospheric simulations to agree with the available observations. At the same time, this information is propagated to areas of the simulation that have not been observed. ADAPT implements a local ensemble transform Kalman filter (LETKF) to accomplish data assimilation, allowing the covariance structure of the flux-transport model to influence assimilation of photosphere observations while eliminating spurious correlations between ensemble members arising from a limited ensemble size. We give a detailed account of the implementation of the LETKF into ADAPT. Advantages of the LETKF scheme over previously implemented assimilation methods are highlighted.
Adaptive state estimation for control of flexible structures
NASA Technical Reports Server (NTRS)
Chen, Chung-Wen; Huang, Jen-Kuang
1990-01-01
This paper proposes a new approach of obtaining adaptive state estimation of a system in the presence of unknown system disturbances and measurement noise. In the beginning, a non-optimal Kalman filter with arbitrary initial guess for the process and measurement noises is implemented. At the same time, an adaptive transversal predictor (ATP) based on the recursive least-squares (RLS) algorithm is used to yield optimal one- to p- step-ahead output predictions using the previous input/output data. Referring to these optimal predictions the Kalman filter gain is updated and the performance of the state estimation is thus improved. If forgetting factor is implemented in the recursive least-squares algorithm, this method is also capable of dealing with the situation when the noise statistics are slowly time-varying. This feature makes this new approach especially suitable for the control of flexible structures. A numerical example demonstrates the feasibility of this real time adaptive state estimation method.
Data Assimilation in the ADAPT Photospheric Flux Transport Model
Hickmann, Kyle S.; Godinez, Humberto C.; Henney, Carl J.; Arge, C. Nick
2015-03-17
Global maps of the solar photospheric magnetic flux are fundamental drivers for simulations of the corona and solar wind and therefore are important predictors of geoeffective events. However, observations of the solar photosphere are only made intermittently over approximately half of the solar surface. The Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model uses localized ensemble Kalman filtering techniques to adjust a set of photospheric simulations to agree with the available observations. At the same time, this information is propagated to areas of the simulation that have not been observed. ADAPT implements a local ensemble transform Kalman filter (LETKF) to accomplish data assimilation, allowing the covariance structure of the flux-transport model to influence assimilation of photosphere observations while eliminating spurious correlations between ensemble members arising from a limited ensemble size. We give a detailed account of the implementation of the LETKF into ADAPT. Advantages of the LETKF scheme over previously implemented assimilation methods are highlighted.
Skew redundant MEMS IMU calibration using a Kalman filter
NASA Astrophysics Data System (ADS)
Jafari, M.; Sahebjameyan, M.; Moshiri, B.; Najafabadi, T. A.
2015-10-01
In this paper, a novel calibration procedure for skew redundant inertial measurement units (SRIMUs) based on micro-electro mechanical systems (MEMS) is proposed. A general model of the SRIMU measurements is derived which contains the effects of bias, scale factor error and misalignments. For more accuracy, the effect of lever arms of the accelerometers to the center of the table are modeled and compensated in the calibration procedure. Two separate Kalman filters (KFs) are proposed to perform the estimation of error parameters for gyroscopes and accelerometers. The predictive error minimization (PEM) stochastic modeling method is used to simultaneously model the effect of bias instability and random walk noise on the calibration Kalman filters to diminish the biased estimations. The proposed procedure is simulated numerically and has expected experimental results. The calibration maneuvers are applied using a two-axis angle turntable in a way that the persistency of excitation (PE) condition for parameter estimation is met. For this purpose, a trapezoidal calibration profile is utilized to excite different deterministic error parameters of the accelerometers and a pulse profile is used for the gyroscopes. Furthermore, to evaluate the performance of the proposed KF calibration method, a conventional least squares (LS) calibration procedure is derived for the SRIMUs and the simulation and experimental results compare the functionality of the two proposed methods with each other.
The Arctic Ocean ice balance - A Kalman smoother estimate
NASA Technical Reports Server (NTRS)
Thomas, D. R.; Rothrock, D. A.
1993-01-01
The methodology of Kalman filtering and smoothing is used to integrate a 7-year time series of buoy-derived ice motion fields and satellite passive microwave observations. The result is a record of the concentrations of open water, first-year ice, and multiyear ice that we believe is better than the estimates based on the microwave data alone. The Kalman procedure interprets the evolution of the ice cover in terms of advection, melt, growth, ridging, and aging of first-year into multiyear ice. Generally, the regions along the coasts of Alaska and Siberia and the area just north of Fram Strait are sources of first-year ice, with the rest of the Arctic Ocean acting as a sink for first-year ice via ridging and aging. All the Arctic Ocean except for the Beaufort and Chukchi seas is a source of multiyear ice, with the Chukchi being the only internal multiyear ice sink. Export through Fram Strait is a major ice sink, but we find only about two-thirds the export and greater interannual variation than found in previous studies. There is no discernible trend in the area of multiyear ice in the Arctic Ocean during the 7 years.
Consensus+Innovations Distributed Kalman Filter With Optimized Gains
NASA Astrophysics Data System (ADS)
Das, Subhro; Moura, Jose M. F.
2017-01-01
In this paper, we address the distributed filtering and prediction of time-varying random fields represented by linear time-invariant (LTI) dynamical systems. The field is observed by a sparsely connected network of agents/sensors collaborating among themselves. We develop a Kalman filter type consensus+innovations distributed linear estimator of the dynamic field termed as Consensus+Innovations Kalman Filter. We analyze the convergence properties of this distributed estimator. We prove that the mean-squared error of the estimator asymptotically converges if the degree of instability of the field dynamics is within a pre-specified threshold defined as tracking capacity of the estimator. The tracking capacity is a function of the local observation models and the agent communication network. We design the optimal consensus and innovation gain matrices yielding distributed estimates with minimized mean-squared error. Through numerical evaluations, we show that, the distributed estimator with optimal gains converges faster and with approximately 3dB better mean-squared error performance than previous distributed estimators.
3D hand tracking using Kalman filter in depth space
NASA Astrophysics Data System (ADS)
Park, Sangheon; Yu, Sunjin; Kim, Joongrock; Kim, Sungjin; Lee, Sangyoun
2012-12-01
Hand gestures are an important type of natural language used in many research areas such as human-computer interaction and computer vision. Hand gestures recognition requires the prior determination of the hand position through detection and tracking. One of the most efficient strategies for hand tracking is to use 2D visual information such as color and shape. However, visual-sensor-based hand tracking methods are very sensitive when tracking is performed under variable light conditions. Also, as hand movements are made in 3D space, the recognition performance of hand gestures using 2D information is inherently limited. In this article, we propose a novel real-time 3D hand tracking method in depth space using a 3D depth sensor and employing Kalman filter. We detect hand candidates using motion clusters and predefined wave motion, and track hand locations using Kalman filter. To verify the effectiveness of the proposed method, we compare the performance of the proposed method with the visual-based method. Experimental results show that the performance of the proposed method out performs visual-based method.
Information and Entropy Flow in the Kalman?Bucy Filter
NASA Astrophysics Data System (ADS)
Mitter, Sanjoy K.; Newton, Nigel J.
2005-01-01
We investigate the information theoretic properties of Kalman-Bucy filters in continuous time, developing notions of information supply, storage and dissipation. Introducing a concept of energy, we develop a physical analogy in which the unobserved signal describes a statistical mechanical system interacting with a heat bath. The abstract `universe' comprising the signal and the heat bath obeys a non-increase law of entropy; however, with the introduction of partial observations, this law can be violated. The Kalman-Bucy filter behaves like a Maxwellian demon in this analogy, returning signal energy to the heat bath without causing entropy increase. This is made possible by the steady supply of new information. In a second analogy the signal and filter interact, setting up a stationary non-equilibrium state, in which energy flows between the heat bath, the signal and the filter without causing any overall entropy increase. We introduce a rate of interactive entropy flow that isolates the statistical mechanics of this flow from marginal effects. Both analogies provide quantitative examples of Landauer's Principle.
Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation
Liu, Xi; Qu, Hua; Zhao, Jihong; Yue, Pengcheng; Wang, Meng
2016-01-01
A new algorithm called maximum correntropy unscented Kalman filter (MCUKF) is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF) provides an efficient tool to solve the non-linear state estimate problem. However, the UKF usually plays well in Gaussian noises. Its performance may deteriorate substantially in the presence of non-Gaussian noises, especially when the measurements are disturbed by some heavy-tailed impulsive noises. By making use of the maximum correntropy criterion (MCC), the proposed algorithm can enhance the robustness of UKF against impulsive noises. In the MCUKF, the unscented transformation (UT) is applied to obtain a predicted state estimation and covariance matrix, and a nonlinear regression method with the MCC cost is then used to reformulate the measurement information. Finally, the UT is adopted to the measurement equation to obtain the filter state and covariance matrix. Illustrative examples demonstrate the superior performance of the new algorithm. PMID:27657069
Multimodal Degradation Prognostics Based on Switching Kalman Filter Ensemble.
Lim, Pin; Goh, Chi Keong; Tan, Kay Chen; Dutta, Partha
2017-01-01
For accurate prognostics, users have to determine the current health of the system and predict future degradation pattern of the system. An increasingly popular approach toward tackling prognostic problems involves the use of switching models to represent various degradation phases, which the system undergoes. Such approaches have the advantage of determining the exact degradation phase of the system and being able to handle nonlinear degradation models through piecewise linear approximation. However, limitations of such existing methods include, limited applicability due to the discretization of predicted remaining useful life, insufficient robustness due to the use of single models and others. This paper circumvents these limitations by proposing a hybrid of ensemble methods with switching methods. The proposed method first implements a switching Kalman filter (SKF) to classify between various linear degradation phases, then predict the future propagation of fault dimension using appropriate Kalman filters for each phase. This proposed method achieves both continuous and discrete prediction values representing the remaining life and degradation phase of the system, respectively. The proposed framework is shown via a case study on benchmark simulated aeroengine data sets. The evaluation of the proposed framework shows that the proposed method achieves better accuracy and robustness against noise compared with other methods reported in the literature. The results also indicate the effectiveness of the SKF in detecting the switching point between various degradation modes.
Time transfer using geostationary satellites: Implementation of a Kalman filter
NASA Technical Reports Server (NTRS)
Meyer, F.
1994-01-01
Since 1988, various experiments have shown that the TV signals transmitted by direct TV satellites may easily be used to perform time transfers at the level of a few tens of nanoseconds, the main source of error being the uncertainty on the satellite position. We first present the two methods used in our experiment to reduce the effects of the satellite residual motion: the first one consists in estimating the longitude variations of the satellite and then using this information to improve other measurements. This allows reducing the uncertainty to values between 9 and 50 nanoseconds according to the position of the involved stations. In the second method we determine the satellite position by using the data collected by three calibrated stations. Time transfer between each of these stations and a fourth one has been shown to be achievable at the precision level of ten nanoseconds. A new approach based on the use of a Kalman filter is proposed in order to take into account the dynamics of the geostationary satellite. The precisions on orbital elements and clock differences and rates determination given by the first simulated applications of the Kalman filter are presented and compared to those obtained by the other methods.
Carmena, Jose M.
2016-01-01
Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain’s behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user’s motor intention during CLDA—a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to
Shanechi, Maryam M; Orsborn, Amy L; Carmena, Jose M
2016-04-01
Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain's behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user's motor intention during CLDA-a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter
NASA Technical Reports Server (NTRS)
Alag, Gurbux S.; Gilyard, Glenn B.
1990-01-01
To develop advanced control systems for optimizing aircraft engine performance, unmeasurable output variables must be estimated. The estimation has to be done in an uncertain environment and be adaptable to varying degrees of modeling errors and other variations in engine behavior over its operational life cycle. This paper represented an approach to estimate unmeasured output variables by explicitly modeling the effects of off-nominal engine behavior as biases on the measurable output variables. A state variable model accommodating off-nominal behavior is developed for the engine, and Kalman filter concepts are used to estimate the required variables. Results are presented from nonlinear engine simulation studies as well as the application of the estimation algorithm on actual flight data. The formulation presented has a wide range of application since it is not restricted or tailored to the particular application described.
Coelho, R.; Alves, D. [Instituto de Plasmas e Fusao Nuclear, Associacao Euratom Hawkes, N.; Brix, M. [Euratom Collaboration: JET EFDA Contributors
2009-06-15
A novel technique for the real-time measurement of the magnetic field pitch angle in JET discharges using the motional Stark effect diagnostic is presented. Kalman filtering techniques are adopted to estimate the amplitude of the avalanche photodiode signals' harmonics that are relevant for the pitch angle calculation. The proposed technique {l_brace}for extended technical details of the generic algorithm see [R. Coelho and D. Alves, IEEE Trans. Plasma Sci. 37, 164 (2009)]{r_brace} is shown to be much more robust and provides less noisy estimates than an equivalent lock-in amplifier scheme, in particular when dealing with edge localized modes.
Coelho, R; Alves, D; Hawkes, N; Brix, M
2009-06-01
A novel technique for the real-time measurement of the magnetic field pitch angle in JET discharges using the motional Stark effect diagnostic is presented. Kalman filtering techniques are adopted to estimate the amplitude of the avalanche photodiode signals' harmonics that are relevant for the pitch angle calculation. The proposed technique {for extended technical details of the generic algorithm see [R. Coelho and D. Alves, IEEE Trans. Plasma Sci. 37, 164 (2009)]} is shown to be much more robust and provides less noisy estimates than an equivalent lock-in amplifier scheme, in particular when dealing with edge localized modes.
A Kalman Filter for SINS Self-Alignment Based on Vector Observation
Xu, Xiang; Xu, Xiaosu; Zhang, Tao; Li, Yao; Tong, Jinwu
2017-01-01
In this paper, a self-alignment method for strapdown inertial navigation systems based on the q-method is studied. In addition, an improved method based on integrating gravitational apparent motion to form apparent velocity is designed, which can reduce the random noises of the observation vectors. For further analysis, a novel self-alignment method using a Kalman filter based on adaptive filter technology is proposed, which transforms the self-alignment procedure into an attitude estimation using the observation vectors. In the proposed method, a linear psuedo-measurement equation is adopted by employing the transfer method between the quaternion and the observation vectors. Analysis and simulation indicate that the accuracy of the self-alignment is improved. Meanwhile, to improve the convergence rate of the proposed method, a new method based on parameter recognition and a reconstruction algorithm for apparent gravitation is devised, which can reduce the influence of the random noises of the observation vectors. Simulations and turntable tests are carried out, and the results indicate that the proposed method can acquire sound alignment results with lower standard variances, and can obtain higher alignment accuracy and a faster convergence rate. PMID:28146059
Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering
Havlicek, Martin; Friston, Karl J.; Jan, Jiri; Brazdil, Milan; Calhoun, Vince D.
2011-01-01
This paper presents a new approach to inverting (fitting) models of coupled dynamical systems based on state-of-the-art (cubature) Kalman filtering. Crucially, this inversion furnishes posterior estimates of both the hidden states and parameters of a system, including any unknown exogenous input. Because the underlying generative model is formulated in continuous time (with a discrete observation process) it can be applied to a wide variety of models specified with either ordinary or stochastic differential equations. These are an important class of models that are particularly appropriate for biological time-series, where the underlying system is specified in terms of kinetics or dynamics (i.e., dynamic causal models). We provide comparative evaluations with generalized Bayesian filtering (dynamic expectation maximization) and demonstrate marked improvements in accuracy and computational efficiency. We compare the schemes using a series of difficult (nonlinear) toy examples and conclude with a special focus on hemodynamic models of evoked brain responses in fMRI. Our scheme promises to provide a significant advance in characterizing the functional architectures of distributed neuronal systems, even in the absence of known exogenous (experimental) input; e.g., resting state fMRI studies and spontaneous fluctuations in electrophysiological studies. Importantly, unlike current Bayesian filters (e.g. DEM), our scheme provides estimates of time-varying parameters, which we will exploit in future work on the adaptation and enabling of connections in the brain. PMID:21396454
A Kalman Filter Technique for Improving Medium-Term Predictions of the Sunspot Number
NASA Astrophysics Data System (ADS)
Podladchikova, T.; van der Linden, R.
2012-04-01
In this work we describe a technique developed to improve medium-term prediction methods of monthly smoothed sunspot numbers. Each month, the predictions are updated using the last available observations (see the monthly output in real time at http://sidc.oma.be/products/kalfil). The improvement of the predictions is provided by applying an adaptive Kalman filter to the medium-term predictions obtained by any other method, using the six-monthly mean values of sunspot numbers covering the six months between the last available value of the 13-month running mean (the starting point for the predictions) and the "current time" ( i.e. now). Our technique provides an effective estimate of the sunspot index at the current time. This estimate becomes the new starting point for the updated prediction that is shifted six months ahead in comparison with the last available 13-month running mean, and it provides an increase of prediction accuracy. Our technique has been tested on three medium-term prediction methods that are currently in real-time operation: The McNish-Lincoln method (NGDC), the standard method (SIDC), and the combined method (SIDC). With our technique, the prediction accuracy for the McNish-Lincoln method is increased by 17 - 30%, for the standard method by 5 - 21% and for the combined method by 6 - 57%.
Segmentation based on motion: an incremental approach using temporal Kalman filtering
NASA Astrophysics Data System (ADS)
Germain, Florence; Baudois, Daniel
1993-06-01
There is an increasing interest for image motion analysis, driven by several application fields ranging from dynamic scene analysis, up to image encoding for transmission purposes. In the context of scene analysis, the image motion information is of crucial help for segmentation and qualitative interpretation. In the case of unstructured, inhomogeneous images, motion information is carried by the spatio-temporal variations of the light intensity function. The apparent distribution inferred from this information is a dense vector field, called optical flow. Assuming the spatial continuity of the field to be estimated, a local determination of optical flow is possible. The main difficulty lies in the handling of motion discontinuities. Usually, the localization of motion frontiers is handled as a binary problem, and thus leads to instabilities during the estimation process. We propose an incremental process, evaluating the optical flow from a sequence of images, using temporal Kalman filtering. Our approach is based on a continuous handling of motion frontiers. The evolution model acts as a temporal low band filter on the estimated field. To cope with motion discontinuities, the filter is continuously adapted according to local motion homogeneity, via the covariance of the model noise. The result is a progressive cancelation of temporal regularization in the neighborhood of motion frontiers, allowing a better convergence of the filter in case of such a discontinuity. Such a continuous handling of motion frontiers leads to a great robustness in the estimation process.
NASA Astrophysics Data System (ADS)
Greybush, Steven J.; Wilson, R. John; Hoffman, Ross N.; Hoffman, Matthew J.; Miyoshi, Takemasa; Ide, Kayo; McConnochie, Timothy; Kalnay, Eugenia
2012-11-01
Thermal Emission Spectrometer (TES) retrieved temperature profiles are assimilated into the GFDL Mars Global Climate Model (MGCM) using the Local Ensemble Transform Kalman Filter (LETKF) to produce synoptic maps of temperature, winds, and surface pressure and their uncertainties over the course of a Martian year. Short-term (0.25 sol) forecasts compared to independent observations show reduced root mean square error (to 3-4 K global RMSE for a 30-sol evaluation period during the northern hemisphere autumn) and bias compared to a free running model. Several enhanced techniques result in further performance gains. A 4D-LETKF considers observations at their correct hour of occurrence rather than every 6 h. Spatially varying adaptive inflation and varying the dust distribution among ensemble members refine estimates of analysis uncertainty through the ensemble spread. Enhancing dust and water ice aerosol schemes and the application of empirical bias correction using time mean analysis increments help account for model biases. Full-year experiments using prescribed dust opacities and observed TES dust opacities show that while realistic dust distributions are essential to match observed temperatures with a free run simulation, analyses from data assimilation are more robust with respect to imperfections in aerosol distribution. The data assimilation system described here is being used to generate a new reanalysis of Mars weather and climate, which will have many scientific and engineering applications.
Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering.
Havlicek, Martin; Friston, Karl J; Jan, Jiri; Brazdil, Milan; Calhoun, Vince D
2011-06-15
This paper presents a new approach to inverting (fitting) models of coupled dynamical systems based on state-of-the-art (cubature) Kalman filtering. Crucially, this inversion furnishes posterior estimates of both the hidden states and parameters of a system, including any unknown exogenous input. Because the underlying generative model is formulated in continuous time (with a discrete observation process) it can be applied to a wide variety of models specified with either ordinary or stochastic differential equations. These are an important class of models that are particularly appropriate for biological time-series, where the underlying system is specified in terms of kinetics or dynamics (i.e., dynamic causal models). We provide comparative evaluations with generalized Bayesian filtering (dynamic expectation maximization) and demonstrate marked improvements in accuracy and computational efficiency. We compare the schemes using a series of difficult (nonlinear) toy examples and conclude with a special focus on hemodynamic models of evoked brain responses in fMRI. Our scheme promises to provide a significant advance in characterizing the functional architectures of distributed neuronal systems, even in the absence of known exogenous (experimental) input; e.g., resting state fMRI studies and spontaneous fluctuations in electrophysiological studies. Importantly, unlike current Bayesian filters (e.g. DEM), our scheme provides estimates of time-varying parameters, which we will exploit in future work on the adaptation and enabling of connections in the brain.
Kalman filter and correction of the temperatures estimated by PRECIS model
NASA Astrophysics Data System (ADS)
Porto de Carvalho, José Ruy; Assad, Eduardo Delgado; Pinto, Hilton Silveira
2011-10-01
The purpose of this study is to evaluate the accuracy of the estimation the monthly mean temperature simulated by the PRECIS model—scenarios A2 and B2 of the IPCC—for Brazilian regions and to develop a Kalman filter to correct the systematic errors of the model for the months of January to June 2010. With a regionalized model, PRECIS aims to reproduce the main features of the climate in complex terrains. The temperature estimates for January to June 2010 are based on linear regression of PRECIS simulations in each pixel of the domain for two time periods, 1961-1990 and 2070-2100. These initial estimates are adapted to 1142 observing stations by a correction using the vertical temperature gradient of the Standard Atmosphere and the difference between model and real topography. The analysis was performed using monthly observed mean temperature data from meteorological stations, along with 1142 simulated data. The PRECIS model with systematic errors was ameliorated by the application of the filter resulting in an improved mean temperature prediction of 66% above the mean square error for the dry months and above 49% for the wet months, for both scenarios under study. At the half-way point, the improvement was 68% for the A2 scenario and 69% for scenario B2.
A Kalman Filter for SINS Self-Alignment Based on Vector Observation.
Xu, Xiang; Xu, Xiaosu; Zhang, Tao; Li, Yao; Tong, Jinwu
2017-01-29
In this paper, a self-alignment method for strapdown inertial navigation systems based on the q-method is studied. In addition, an improved method based on integrating gravitational apparent motion to form apparent velocity is designed, which can reduce the random noises of the observation vectors. For further analysis, a novel self-alignment method using a Kalman filter based on adaptive filter technology is proposed, which transforms the self-alignment procedure into an attitude estimation using the observation vectors. In the proposed method, a linear psuedo-measurement equation is adopted by employing the transfer method between the quaternion and the observation vectors. Analysis and simulation indicate that the accuracy of the self-alignment is improved. Meanwhile, to improve the convergence rate of the proposed method, a new method based on parameter recognition and a reconstruction algorithm for apparent gravitation is devised, which can reduce the influence of the random noises of the observation vectors. Simulations and turntable tests are carried out, and the results indicate that the proposed method can acquire sound alignment results with lower standard variances, and can obtain higher alignment accuracy and a faster convergence rate.
NASA Technical Reports Server (NTRS)
Queen, Steven Z.
2015-01-01
The Magnetospheric Multiscale (MMS) mission consists of four identically instrumented, spin-stabilized observatories, elliptically orbiting the Earth in a tetrahedron formation. For the operational success of the mission, on-board systems must be able to deliver high-precision orbital adjustment maneuvers. On MMS, this is accomplished using feedback from on-board star sensors in tandem with accelerometers whose measurements are dynamically corrected for errors associated with a spinning platform. In order to determine the required corrections to the measured acceleration, precise estimates of attitude, rate, and mass-properties are necessary. To this end, both an on-board and ground-based Multiplicative Extended Kalman Filter (MEKF) were formulated and implemented in order to estimate the dynamic and quasi-static properties of the spacecraft.
De Groote, F; De Laet, T; Jonkers, I; De Schutter, J
2008-12-05
We developed a Kalman smoothing algorithm to improve estimates of joint kinematics from measured marker trajectories during motion analysis. Kalman smoothing estimates are based on complete marker trajectories. This is an improvement over other techniques, such as the global optimisation method (GOM), Kalman filtering, and local marker estimation (LME), where the estimate at each time instant is only based on part of the marker trajectories. We applied GOM, Kalman filtering, LME, and Kalman smoothing to marker trajectories from both simulated and experimental gait motion, to estimate the joint kinematics of a ten segment biomechanical model, with 21 degrees of freedom. Three simulated marker trajectories were studied: without errors, with instrumental errors, and with soft tissue artefacts (STA). Two modelling errors were studied: increased thigh length and hip centre dislocation. We calculated estimation errors from the known joint kinematics in the simulation study. Compared with other techniques, Kalman smoothing reduced the estimation errors for the joint positions, by more than 50% for the simulated marker trajectories without errors and with instrumental errors. Compared with GOM, Kalman smoothing reduced the estimation errors for the joint moments by more than 35%. Compared with Kalman filtering and LME, Kalman smoothing reduced the estimation errors for the joint accelerations by at least 50%. Our simulation results show that the use of Kalman smoothing substantially improves the estimates of joint kinematics and kinetics compared with previously proposed techniques (GOM, Kalman filtering, and LME) for both simulated, with and without modelling errors, and experimentally measured gait motion.
Sabatini, Angelo Maria
2012-01-01
In this paper a quaternion-based Variable-State-Dimension Extended Kalman Filter (VSD-EKF) is developed for estimating the three-dimensional orientation of a rigid body using the measurements from an Inertial Measurement Unit (IMU) integrated with a triaxial magnetic sensor. Gyro bias and magnetic disturbances are modeled and compensated by including them in the filter state vector. The VSD-EKF switches between a quiescent EKF, where the magnetic disturbance is modeled as a first-order Gauss-Markov stochastic process (GM-1), and a higher-order EKF where extra state components are introduced to model the time-rate of change of the magnetic field as a GM-1 stochastic process, namely the magnetic disturbance is modeled as a second-order Gauss-Markov stochastic process (GM-2). Experimental validation tests show the effectiveness of the VSD-EKF, as compared to either the quiescent EKF or the higher-order EKF when they run separately. PMID:23012502
Sabatini, Angelo Maria
2011-01-01
In this paper we present a quaternion-based Extended Kalman Filter (EKF) for estimating the three-dimensional orientation of a rigid body. The EKF exploits the measurements from an Inertial Measurement Unit (IMU) that is integrated with a tri-axial magnetic sensor. Magnetic disturbances and gyro bias errors are modeled and compensated by including them in the filter state vector. We employ the observability rank criterion based on Lie derivatives to verify the conditions under which the nonlinear system that describes the process of motion tracking by the IMU is observable, namely it may provide sufficient information for performing the estimation task with bounded estimation errors. The observability conditions are that the magnetic field, perturbed by first-order Gauss-Markov magnetic variations, and the gravity vector are not collinear and that the IMU is subject to some angular motions. Computer simulations and experimental testing are presented to evaluate the algorithm performance, including when the observability conditions are critical. PMID:22163689
Mobile location with NLOS identification and mitigation based on modified Kalman filtering.
Ke, Wei; Wu, Lenan
2011-01-01
In order to enhance accuracy and reliability of wireless location in the mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments, a robust mobile location algorithm is presented to track the position of a mobile node (MN). An extended Kalman filter (EKF) modified in the updating phase is utilized to reduce the NLOS error in rough wireless environments, in which the NLOS bias contained in each measurement range is estimated directly by the constrained optimization method. To identify the change of channel situation between NLOS and LOS, a low complexity identification method based on innovation vectors is proposed. Numerical results illustrate that the location errors of the proposed algorithm are all significantly smaller than those of the iterated NLOS EKF algorithm and the conventional EKF algorithm in different LOS/NLOS conditions. Moreover, this location method does not require any statistical distribution knowledge of the NLOS error. In addition, complexity experiments suggest that this algorithm supports real-time applications.
Using a LRF sensor in the Kalman-filtering-based localization of a mobile robot.
Teslić, Luka; Skrjanc, Igor; Klancar, Gregor
2010-01-01
This paper deals with the problem of estimating the output-noise covariance matrix that is involved in the localization of a mobile robot. The extended Kalman filter (EKF) is used to localize the mobile robot with a laser range finder (LRF) sensor in an environment described with line segments. The covariances of the observed environment lines, which compose the output-noise covariance matrix in the correction step of the EKF, are the result of the noise arising from a range-sensor's (e.g., a LRF) distance and angle measurements. A method for estimating the covariances of the line parameters based on classic least squares (LSQ) is proposed. This method is compared with the method resulting from the orthogonal LSQ in terms of computational complexity. The results of a comparison show that the use of classic LSQ instead of orthogonal LSQ reduce the number of computations in a localization algorithm which is a part of a SLAM (simultaneous localization and mapping) algorithm. Statistical accuracy of both methods is also compared by simulating the LRF's measurements and the comparison proves the efficiency of the proposed approach.
Capellari, Giovanni; Azam, Saeed Eftekhar; Mariani, Stefano
2015-12-22
Health monitoring of lightweight structures, like thin flexible plates, is of interest in several engineering fields. In this paper, a recursive Bayesian procedure is proposed to monitor the health of such structures through data collected by a network of optimally placed inertial sensors. As a main drawback of standard monitoring procedures is linked to the computational costs, two remedies are jointly considered: first, an order-reduction of the numerical model used to track the structural dynamics, enforced with proper orthogonal decomposition; and, second, an improved particle filter, which features an extended Kalman updating of each evolving particle before the resampling stage. The former remedy can reduce the number of effective degrees-of-freedom of the structural model to a few only (depending on the excitation), whereas the latter one allows to track the evolution of damage and to locate it thanks to an intricate formulation. To assess the effectiveness of the proposed procedure, the case of a plate subject to bending is investigated; it is shown that, when the procedure is appropriately fed by measurements, damage is efficiently and accurately estimated.
Mehta, Daryush D; Rudoy, Daniel; Wolfe, Patrick J
2012-09-01
Vocal tract resonance characteristics in acoustic speech signals are classically tracked using frame-by-frame point estimates of formant frequencies followed by candidate selection and smoothing using dynamic programming methods that minimize ad hoc cost functions. The goal of the current work is to provide both point estimates and associated uncertainties of center frequencies and bandwidths in a statistically principled state-space framework. Extended Kalman (K) algorithms take advantage of a linearized mapping to infer formant and antiformant parameters from frame-based estimates of autoregressive moving average (ARMA) cepstral coefficients. Error analysis of KARMA, wavesurfer, and praat is accomplished in the all-pole case using a manually marked formant database and synthesized speech waveforms. KARMA formant tracks exhibit lower overall root-mean-square error relative to the two benchmark algorithms with the ability to modify parameters in a controlled manner to trade off bias and variance. Antiformant tracking performance of KARMA is illustrated using synthesized and spoken nasal phonemes. The simultaneous tracking of uncertainty levels enables practitioners to recognize time-varying confidence in parameters of interest and adjust algorithmic settings accordingly.
Kalman filter based data fusion for neutral axis tracking in wind turbine towers
NASA Astrophysics Data System (ADS)
Soman, Rohan; Malinowski, Pawel; Ostachowicz, Wieslaw; Paulsen, Uwe S.
2015-03-01
Wind energy is seen as one of the most promising solutions to man's ever increasing demands of a clean source of energy. In particular to reduce the cost of energy (COE) generated, there are efforts to increase the life-time of the wind turbines, to reduce maintenance costs and to ensure high availability. Maintenance costs may be lowered and the high availability and low repair costs ensured through the use of condition monitoring (CM) and structural health monitoring (SHM). SHM allows early detection of damage and allows maintenance planning. Furthermore, it can allow us to avoid unnecessary downtime, hence increasing the availability of the system. The present work is based on the use of neutral axis (NA) for SHM of the structure. The NA is tracked by data fusion of measured yaw angle and strain through the use of Extended Kalman Filter (EKF). The EKF allows accurate tracking even in the presence of changing ambient conditions. NA is defined as the line or plane in the section of the beam which does not experience any tensile or compressive forces when loaded. The NA is the property of the cross section of the tower and is independent of the applied loads and ambient conditions. Any change in the NA position may be used for detecting and locating the damage. The wind turbine tower has been modelled with FE software ABAQUS and validated on data from load measurements carried out on the 34m high tower of the Nordtank, NTK 500/41 wind turbine.
Simplified groundwater flow modeling: an application of Kalman filter based identification
Pimentel, K.D.; Candy, J.V.; Azevedo, S.G.; Doerr, T.A.
1980-05-01
The need exists for methods to simplify groundwater contaminant transport models. Reduced-order models are needed in risk assessments for licensing and regulating long-term nuclear waste repositories. Such models will be used in Monte Carlo simulations to generate probabilities of nuclear waste migration in aquifers at candidate repository sites in the United States. In this feasibility study we focused on groundwater flow rather than contaminant transport because the flow problem is more simple. A pump-drawdown test is modeled with a reduced-order set of ordinary differential equations obtained by differencing the partial differential equation. We determined the accuracy of the reduced model by comparing it with the analytic solution for the drawdown test. We established an accuracy requirement of 2% error at the single observation well and found that a model with only 21 states satisfied that criterion. That model was used in an extended Kalman filter with synthesized measurement data from one observation well to identify transmissivity within 1% error and storage coefficient within 10% error. We used several statistical tests to assess the performance of the estimator/identifier and found it to be satisfactory for this application.
Capellari, Giovanni; Eftekhar Azam, Saeed; Mariani, Stefano
2015-01-01
Health monitoring of lightweight structures, like thin flexible plates, is of interest in several engineering fields. In this paper, a recursive Bayesian procedure is proposed to monitor the health of such structures through data collected by a network of optimally placed inertial sensors. As a main drawback of standard monitoring procedures is linked to the computational costs, two remedies are jointly considered: first, an order-reduction of the numerical model used to track the structural dynamics, enforced with proper orthogonal decomposition; and, second, an improved particle filter, which features an extended Kalman updating of each evolving particle before the resampling stage. The former remedy can reduce the number of effective degrees-of-freedom of the structural model to a few only (depending on the excitation), whereas the latter one allows to track the evolution of damage and to locate it thanks to an intricate formulation. To assess the effectiveness of the proposed procedure, the case of a plate subject to bending is investigated; it is shown that, when the procedure is appropriately fed by measurements, damage is efficiently and accurately estimated. PMID:26703615
NASA Technical Reports Server (NTRS)
Tangborn, Andrew; Auger, Ludovic
2003-01-01
A suboptimal Kalman filter system which evolves error covariances in terms of a truncated set of wavelet coefficients has been developed for the assimilation of chemical tracer observations of CH4. This scheme projects the discretized covariance propagation equations and covariance matrix onto an orthogonal set of compactly supported wavelets. Wavelet representation is localized in both location and scale, which allows for efficient representation of the inherently anisotropic structure of the error covariances. The truncation is carried out in such a way that the resolution of the error covariance is reduced only in the zonal direction, where gradients are smaller. Assimilation experiments which last 24 days, and used different degrees of truncation were carried out. These reduced the covariance size by 90, 97 and 99 % and the computational cost of covariance propagation by 80, 93 and 96 % respectively. The difference in both error covariance and the tracer field between the truncated and full systems over this period were found to be not growing in the first case, and growing relatively slowly in the later two cases. The largest errors in the tracer fields were found to occur in regions of largest zonal gradients in the constituent field. This results indicate that propagation of error covariances for a global two-dimensional data assimilation system are currently feasible. Recommendations for further reduction in computational cost are made with the goal of extending this technique to three-dimensional global assimilation systems.
NASA Technical Reports Server (NTRS)
Liou, Meng-Sing
1995-01-01
A unique formulation of describing fluid motion is presented. The method, referred to as 'extended Lagrangian method,' is interesting from both theoretical and numerical points of view. The formulation offers accuracy in numerical solution by avoiding numerical diffusion resulting from mixing of fluxes in the Eulerian description. The present method and the Arbitrary Lagrangian-Eulerian (ALE) method have a similarity in spirit-eliminating the cross-streamline numerical diffusion. For this purpose, we suggest a simple grid constraint condition and utilize an accurate discretization procedure. This grid constraint is only applied to the transverse cell face parallel to the local stream velocity, and hence our method for the steady state problems naturally reduces to the streamline-curvature method, without explicitly solving the steady stream-coordinate equations formulated a priori. Unlike the Lagrangian method proposed by Loh and Hui which is valid only for steady supersonic flows, the present method is general and capable of treating subsonic flows and supersonic flows as well as unsteady flows, simply by invoking in the same code an appropriate grid constraint suggested in this paper. The approach is found to be robust and stable. It automatically adapts to flow features without resorting to clustering, thereby maintaining rather uniform grid spacing throughout and large time step. Moreover, the method is shown to resolve multi-dimensional discontinuities with a high level of accuracy, similar to that found in one-dimensional problems.
NASA Astrophysics Data System (ADS)
Ulrich, Steve
This work addresses the direct adaptive trajectory tracking control problem associated with lightweight space robotic manipulators that exhibit elastic vibrations in their joints, and which are subject to parametric uncertainties and modeling errors. Unlike existing adaptive control methodologies, the proposed flexible-joint control techniques do not require identification of unknown parameters, or mathematical models of the system to be controlled. The direct adaptive controllers developed in this work are based on the model reference adaptive control approach, and manage modeling errors and parametric uncertainties by time-varying the controller gains using new adaptation mechanisms, thereby reducing the errors between an ideal model and the actual robot system. More specifically, new decentralized adaptation mechanisms derived from the simple adaptive control technique and fuzzy logic control theory are considered in this work. Numerical simulations compare the performance of the adaptive controllers with a nonadaptive and a conventional model-based controller, in the context of 12.6 m xx 12.6 m square trajectory tracking. To validate the robustness of the controllers to modeling errors, a new dynamics formulation that includes several nonlinear effects usually neglected in flexible-joint dynamics models is proposed. Results obtained with the adaptive methodologies demonstrate an increased robustness to both uncertainties in joint stiffness coefficients and dynamics modeling errors, as well as highly improved tracking performance compared with the nonadaptive and model-based strategies. Finally, this work considers the partial state feedback problem related to flexible-joint space robotic manipulators equipped only with sensors that provide noisy measurements of motor positions and velocities. An extended Kalman filter-based estimation strategy is developed to estimate all state variables in real-time. The state estimation filter is combined with an adaptive
Comparison of VLBI TRF solutions based on Kalman filtering and recent ITRS realizations
NASA Astrophysics Data System (ADS)
Soja, Benedikt; Nilsson, Tobias; Glaser, Susanne; Balidakis, Kyriakos; Karbon, Maria; Heinkelmann, Robert; Gross, Richard; Schuh, Harald
2016-04-01
Compared to previous prominent global terrestrial reference frames (TRF) solutions, such as the ITRF2008 or DTRF2008, the current accuracy requirements demand among other things extended parameterization to account for various non-linear signals present in the time series of station coordinates. The next generation of TRFs, built upon geodetic data until the end of 2014, employs different approaches to tackle in particular seasonal variations and post-seismic deformations. The ITRF2014, developed at the International Earth Rotation and Reference Systems Service (IERS) Combination Center (CC) at Institut Géographique National, introduces harmonic, exponential and logarithmic functions to take into account aforementioned effects. In contrast, the ITRS realization of the IERS CC at Jet Propulsion Laboratory is based on Kalman filtering, which allows coordinate variations to be modeled in a stochastic sense besides the parameterized linear and seasonal signals. In our study, we compare these multi-technique TRFs with solutions solely based on VLBI data, including 104 radio telescopes and 4239 VLBI sessions, covering a time span of 34 years. We calculated a VLBI TRF based on the traditional least-squares adjustment of session-wise normal equations, and an ensemble of Kalman filter and smoother solutions with different parameterizations and stochastic models. In particular, we investigate the impact of different process noise levels for station coordinates, the choice of stochastic processes, e.g. random walks, and the application of time- and station-dependent noise models. For instance, we find that the estimation of seasonal signals, while important for predictions, does not affect the filtered coordinate time series when observational data is available. Furthermore, post-seismic deformations after major earthquakes require the process noise to be scaled accordingly. For instance, we detected coordinate differences of up to 5 cm immediately after the Chile 2010
NASA Astrophysics Data System (ADS)
Khaki, Mehdi; Forootan, Ehsan; Kuhn, Michael; Awange, Joseph; Pattiaratchi, Charitha
2016-04-01
Quantifying large-scale (basin/global) water storage changes is essential to understand the Earth's hydrological water cycle. Hydrological models have usually been used to simulate variations in storage compartments resulting from changes in water fluxes (i.e., precipitation, evapotranspiration and runoff) considering physical or conceptual frameworks. Models however represent limited skills in accurately simulating the storage compartments that could be the result of e.g., the uncertainty of forcing parameters, model structure, etc. In this regards, data assimilation provides a great chance to combine observational data with a prior forecast state to improve both the accuracy of model parameters and to improve the estimation of model states at the same time. Various methods exist that can be used to perform data assimilation into hydrological models. The one more frequently used particle-based algorithms suitable for non-linear systems high-dimensional systems is the Ensemble Kalman Filtering (EnKF). Despite efficiency and simplicity (especially in EnKF), this method indicate some drawbacks. To implement EnKF, one should use the sample covariance of observations and model state variables to update a priori estimates of the state variables. The sample covariance can be suboptimal as a result of small ensemble size, model errors, model nonlinearity, and other factors. Small ensemble can also lead to the development of correlations between state components that are at a significant distance from one another where there is no physical relation. To investigate the under-sampling issue raise by EnKF, covariance inflation technique in conjunction with localization was implemented. In this study, a comparison between latest methods used in the data assimilation framework, to overcome the mentioned problem, is performed. For this, in addition to implementing EnKF, we introduce and apply the Local Ensemble Kalman Filter (LEnKF) utilizing covariance localization to remove
Onboard orbit determination using GPS observations based on the unscented Kalman filter
NASA Astrophysics Data System (ADS)
Choi, Eun-Jung; Yoon, Jae-Cheol; Lee, Byoung-Sun; Park, Sang-Young; Choi, Kyu-Hong
2010-12-01
Spaceborne GPS receivers are used for real-time navigation by most low Earth orbit (LEO) satellites. In general, the position and velocity accuracy of GPS navigation solutions without a dynamic filter are 25 m (1 σ) and 0.5 m/s (1 σ), respectively. However, GPS navigation solutions, which consist of position, velocity, and GPS receiver clock bias, have many abnormal excursions from the normal error range for space operation. These excursions lessen the accuracy of attitude control and onboard time synchronization. In this research, a new onboard orbit determination algorithm designed with the unscented Kalman filter (UKF) was developed to improve the performance. Because the UKF is able to obtain the posterior mean and covariance accurately by using the second-order Taylor series expansion through the sampled sigma points that are propagated by using the true nonlinear system, its performance can be better than that of the extended Kalman filter (EKF), which uses the linearized state transition matrix to predict the covariance. The dynamic models for orbit propagation applied perturbations due to the 40 × 40 geo-potential, the gravity of the Sun and Moon, solar radiation pressure, and atmospheric drag. The 7(8)th-order Runge-Kutta numerical integration was applied for orbit propagation. Two types of observations, navigation solutions and C/A code pseudorange, can be used at the user's discretion. The performances of the onboard orbit determination were verified using real GPS data of the CHAMP and KOMPSAT-2 satellites. The results of the orbit determination were compared with the precision orbit ephemeris (POE) of the CHAMP and KOMPSAT-2 satellites. The comparison of the orbit determination results using EKF and UKF shows that orbit determination using the UKF yields better results than that using the EKF. In addition, the estimation of the accuracy using the C/A code pseudorange is better than that using the navigation solutions. The absolute position and
Requirements for Kalman filtering on the GE-701 whole word computer
NASA Technical Reports Server (NTRS)
Pines, S.; Schmidt, S. F.
1978-01-01
The results of a study to determine scaling, storage, and word length requirements for programming the Kalman filter on the GE-701 Whole Word Computer are reported. Simulation tests are presented which indicate that the Kalman filter, using a square root formulation with process noise added, utilizing MLS, radar altimeters, and airspeed as navigation aids, may be programmed for the GE-701 computer to successfully navigate and control the Boeing B737-100 during landing approach, landing rollout, and turnoff. The report contains flow charts, equations, computer storage, scaling, and word length recommendations for the Kalman filter on the GE-701 Whole Word computer.
Zero Gyro Kalman Filtering in the presence of a Reaction Wheel Failure
NASA Technical Reports Server (NTRS)
Hur-Diaz, Sun; Wirzburger, John; Smith, Dan; Myslinski, Mike
2007-01-01
Typical implementation of Kalman filters for spacecraft attitude estimation involves the use of gyros for three-axis rate measurements. When there are less than three axes of information available, the accuracy of the Kalman filter depends highly on the accuracy of the dynamics model. This is particularly significant during the transient period when a reaction wheel with a high momentum fails, is taken off-line, and spins down. This paper looks at how a reaction wheel failure can affect the zero-gyro Kalman filter performance for the Hubble Space Telescope and what steps are taken to minimize its impact.
Zero Gyro Kalman Filtering in the Presence of a Reaction Wheel Failure
NASA Technical Reports Server (NTRS)
Hur-Diaz, Sun; Wirzburger, John; Smith, Dan; Myslinski, Mike
2007-01-01
Typical implementation of Kalman filters for spacecraft attitude estimation involves the use of gyros for three-axis rate measurements. When there are less than three axes of information available, the accuracy of the Kalman filter depends highly on the accuracy of the dynamics model. This is particularly significant during the transient period when a reaction wheel with a high momentum fails, is taken off-line, and spins down. This paper looks at how a reaction wheel failure can affect the zero-gyro Kalman filter performance for the Hubble Space Telescope and what steps are taken to minimize its impact.
Theoretical Bit Error Rate Performance of the Kalman Filter Excisor for FM Interference
1992-12-01
un filtre de Kalman as- servi numeriquement par verrouillage de phase et s’avere quasi-optimum quant a la demodulation d’une interference de type MF...Puisqu’on presuppose que l’interftrence est plus forte le signal ou que le bruit, le filtre de Kalman se verrouille sur l’interfdrence et permet...AD-A263 018 THEORETICAL BIT ERROR RATE PERFORMANCE OF THE KALMAN FILTER EXCISOR FOR FM INTERFERENCE by Brian RKominchuk APR 19 1993 DEFENCE RESEARCH
NASA Astrophysics Data System (ADS)
Briseño, Jessica; Herrera, Graciela S.
2010-05-01
Herrera (1998) proposed a method for the optimal design of groundwater quality monitoring networks that involves space and time in a combined form. The method was applied later by Herrera et al (2001) and by Herrera and Pinder (2005). To get the estimates of the contaminant concentration being analyzed, this method uses a space-time ensemble Kalman filter, based on a stochastic flow and transport model. When the method is applied, it is important that the characteristics of the stochastic model be congruent with field data, but, in general, it is laborious to manually achieve a good match between them. For this reason, the main objective of this work is to extend the space-time ensemble Kalman filter proposed by Herrera, to estimate the hydraulic conductivity, together with hydraulic head and contaminant concentration, and its application in a synthetic example. The method has three steps: 1) Given the mean and the semivariogram of the natural logarithm of hydraulic conductivity (ln K), random realizations of this parameter are obtained through two alternatives: Gaussian simulation (SGSim) and Latin Hypercube Sampling method (LHC). 2) The stochastic model is used to produce hydraulic head (h) and contaminant (C) realizations, for each one of the conductivity realizations. With these realization the mean of ln K, h and C are obtained, for h and C, the mean is calculated in space and time, and also the cross covariance matrix h-ln K-C in space and time. The covariance matrix is obtained averaging products of the ln K, h and C realizations on the estimation points and times, and the positions and times with data of the analyzed variables. The estimation points are the positions at which estimates of ln K, h or C are gathered. In an analogous way, the estimation times are those at which estimates of any of the three variables are gathered. 3) Finally the ln K, h and C estimate are obtained using the space-time ensemble Kalman filter. The realization mean for each one
Kalman Filter Estimation of Spinning Spacecraft Attitude using Markley Variables
NASA Technical Reports Server (NTRS)
Sedlak, Joseph E.; Harman, Richard
2004-01-01
There are several different ways to represent spacecraft attitude and its time rate of change. For spinning or momentum-biased spacecraft, one particular representation has been put forward as a superior parameterization for numerical integration. Markley has demonstrated that these new variables have fewer rapidly varying elements for spinning spacecraft than other commonly used representations and provide advantages when integrating the equations of motion. The current work demonstrates how a Kalman filter can be devised to estimate the attitude using these new variables. The seven Markley variables are subject to one constraint condition, making the error covariance matrix singular. The filter design presented here explicitly accounts for this constraint by using a six-component error state in the filter update step. The reduced dimension error state is unconstrained and its covariance matrix is nonsingular.
Damping strapdown inertial navigation system based on a Kalman filter
NASA Astrophysics Data System (ADS)
Zhao, Lin; Li, Jiushun; Cheng, Jianhua; Hao, Yong
2016-11-01
A damping strapdown inertial navigation system (DSINS) can effectively suppress oscillation errors of strapdown inertial navigation systems (SINSs) and improve the navigation accuracy of SINSs. Aiming at overcoming the disadvantages of traditional damping methods, a DSINS, based on a Kalman filter (KF), is proposed in this paper. Using the measurement data of accelerometers and calculated navigation parameters during the navigation process, the expression of the observation equation is derived. The calculation process of the observation in both the internal damping state and the external damping state is presented. Finally, system oscillation errors are compensated by a KF. Simulation and test results show that, compared with traditional damping methods, the proposed method can reduce system overshoot errors and shorten the convergence time of oscillation errors effectively.
Restoration of color images by multichannel Kalman filtering
NASA Technical Reports Server (NTRS)
Galatsanos, Nikolas P.; Chin, Roland T.
1991-01-01
A Kalman filter for optimal restoration of multichannel images is presented. This filter is derived using a multichannel semicausal image model that includes between-channel degradation. Both stationary and nonstationary image models are developed. This filter is implemented in the Fourier domain and computation is reduced from O(Lambda3N3M4) to O(Lambda3N3M2) for an M x M N-channel image with degradation length Lambda. Color (red, green, and blue (RGB)) images are used as examples of multichannel images, and restoration in the RGB and YIQ domains is investigated. Simulations are presented in which the effectiveness of this filter is tested for different types of degradation and different image model estimates.
The development of a Kalman filter clock predictor
NASA Technical Reports Server (NTRS)
Davis, John A.; Greenhall, Charles A.; Boudjemaa, Redoane
2005-01-01
A Kalman filter based clock predictor is developed, and its performance evaluated using both simulated and real data. The clock predictor is shown to possess a neat to optimal Prediction Error Variance (PEV) when the underlying noise consists of one of the power law noise processes commonly encountered in time and frequency measurements. The predictor's performance is the presence of multiple noise processes is also examined. The relationship between the PEV obtained in the presence of multiple noise processes and those obtained for the individual component noise processes is examined. Comparisons are made with a simple linear clock predictor. The clock predictor is used to predict future values of the time offset between pairs of NPL's active hydrogen masers.