Assessing Performance of Bayesian State-Space Models Fit to Argos Satellite Telemetry Locations Processed with Kalman Filtering

PubMed Central

Silva, Mónica A.; Jonsen, Ian; Russell, Deborah J. F.; Prieto, Rui; Thompson, Dave; Baumgartner, Mark F.

2014-01-01

Argos recently implemented a new algorithm to calculate locations of satellite-tracked animals that uses a Kalman filter (KF). The KF algorithm is reported to increase the number and accuracy of estimated positions over the traditional Least Squares (LS) algorithm, with potential advantages to the application of state-space methods to model animal movement data. We tested the performance of two Bayesian state-space models (SSMs) fitted to satellite tracking data processed with KF algorithm. Tracks from 7 harbour seals (Phoca vitulina) tagged with ARGOS satellite transmitters equipped with Fastloc GPS loggers were used to calculate the error of locations estimated from SSMs fitted to KF and LS data, by comparing those to “true” GPS locations. Data on 6 fin whales (Balaenoptera physalus) were used to investigate consistency in movement parameters, location and behavioural states estimated by switching state-space models (SSSM) fitted to data derived from KF and LS methods. The model fit to KF locations improved the accuracy of seal trips by 27% over the LS model. 82% of locations predicted from the KF model and 73% of locations from the LS model were <5 km from the corresponding interpolated GPS position. Uncertainty in KF model estimates (5.6±5.6 km) was nearly half that of LS estimates (11.6±8.4 km). Accuracy of KF and LS modelled locations was sensitive to precision but not to observation frequency or temporal resolution of raw Argos data. On average, 88% of whale locations estimated by KF models fell within the 95% probability ellipse of paired locations from LS models. Precision of KF locations for whales was generally higher. Whales’ behavioural mode inferred by KF models matched the classification from LS models in 94% of the cases. State-space models fit to KF data can improve spatial accuracy of location estimates over LS models and produce equally reliable behavioural estimates. PMID:24651252

Track reconstruction for the Mu3e experiment based on a novel Multiple Scattering fit

NASA Astrophysics Data System (ADS)

Kozlinskiy, Alexandr

2017-08-01

The Mu3e experiment is designed to search for the lepton flavor violating decay μ+ → e+e+e-. The aim of the experiment is to reach a branching ratio sensitivity of 10-16. In a first phase the experiment will be performed at an existing beam line at the Paul-Scherrer Institute (Switzerland) providing 108 muons per second, which will allow to reach a sensitivity of 2 · 10-15. The muons with a momentum of about 28 MeV/c are stopped and decay at rest on a target. The decay products (positrons and electrons) with energies below 53MeV are measured by a tracking detector consisting of two double layers of 50 μm thin silicon pixel sensors. The high granularity of the pixel detector with a pixel size of 80 μm × 80 μm allows for a precise track reconstruction in the high multiplicity environment of the Mu3e experiment, reaching 100 tracks per reconstruction frame of 50 ns in the final phase of the experiment. To deal with such high rates and combinatorics, the Mu3e track reconstruction uses a novel fit algorithm that in the simplest case takes into account only the multiple scattering, which allows for a fast online tracking on a GPU based filter farm. An implementation of the 3-dimensional multiple scattering fit based on hit triplets is described. The extension of the fit that takes into account energy losses and pixel size is used for offline track reconstruction. The algorithm and performance of the offline track reconstruction based on a full Geant4 simulation of the Mu3e detector are 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.

Model-based adaptive 3D sonar reconstruction in reverberating environments.

PubMed

Saucan, Augustin-Alexandru; Sintes, Christophe; Chonavel, Thierry; Caillec, Jean-Marc Le

2015-10-01

In this paper, we propose a novel model-based approach for 3D underwater scene reconstruction, i.e., bathymetry, for side scan sonar arrays in complex and highly reverberating environments like shallow water areas. The presence of multipath echoes and volume reverberation generates false depth estimates. To improve the resulting bathymetry, this paper proposes and develops an adaptive filter, based on several original geometrical models. This multimodel approach makes it possible to track and separate the direction of arrival trajectories of multiple echoes impinging the array. Echo tracking is perceived as a model-based processing stage, incorporating prior information on the temporal evolution of echoes in order to reject cluttered observations generated by interfering echoes. The results of the proposed filter on simulated and real sonar data showcase the clutter-free and regularized bathymetric reconstruction. Model validation is carried out with goodness of fit tests, and demonstrates the importance of model-based processing for bathymetry reconstruction.

Weighted Optimization-Based Distributed Kalman Filter for Nonlinear Target Tracking in Collaborative Sensor Networks.

PubMed

Chen, Jie; Li, Jiahong; Yang, Shuanghua; Deng, Fang

2017-11-01

The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem in collaborative sensor networks. According to the adaptive Kalman filtering (KF) method, the nonlinearity and coupling can be regarded as the model noise covariance, and estimated by minimizing the innovation or residual errors of the states. However, the method requires large time window of data to achieve reliable covariance measurement, making it impractical for nonlinear systems which are rapidly changing. To deal with the problem, a weighted optimization-based distributed KF algorithm (WODKF) is proposed in this paper. The algorithm enlarges the data size of each sensor by the received measurements and state estimates from its connected sensors instead of the time window. A new cost function is set as the weighted sum of the bias and oscillation of the state to estimate the "best" estimate of the model noise covariance. The bias and oscillation of the state of each sensor are estimated by polynomial fitting a time window of state estimates and measurements of the sensor and its neighbors weighted by the measurement noise covariance. The best estimate of the model noise covariance is computed by minimizing the weighted cost function using the exhaustive method. The sensor selection method is in addition to the algorithm to decrease the computation load of the filter and increase the scalability of the sensor network. The existence, suboptimality and stability analysis of the algorithm are given. The local probability data association method is used in the proposed algorithm for the multitarget tracking case. The algorithm is demonstrated in simulations on tracking examples for a random signal, one nonlinear target, and four nonlinear targets. Results show the feasibility and superiority of WODKF against other filtering algorithms for a large class of systems.

Real Time 3D Facial Movement Tracking Using a Monocular Camera

PubMed Central

Dong, Yanchao; Wang, Yanming; Yue, Jiguang; Hu, Zhencheng

2016-01-01

The paper proposes a robust framework for 3D facial movement tracking in real time using a monocular camera. It is designed to estimate the 3D face pose and local facial animation such as eyelid movement and mouth movement. The framework firstly utilizes the Discriminative Shape Regression method to locate the facial feature points on the 2D image and fuses the 2D data with a 3D face model using Extended Kalman Filter to yield 3D facial movement information. An alternating optimizing strategy is adopted to fit to different persons automatically. Experiments show that the proposed framework could track the 3D facial movement across various poses and illumination conditions. Given the real face scale the framework could track the eyelid with an error of 1 mm and mouth with an error of 2 mm. The tracking result is reliable for expression analysis or mental state inference. PMID:27463714

Real Time 3D Facial Movement Tracking Using a Monocular Camera.

PubMed

Dong, Yanchao; Wang, Yanming; Yue, Jiguang; Hu, Zhencheng

2016-07-25

The paper proposes a robust framework for 3D facial movement tracking in real time using a monocular camera. It is designed to estimate the 3D face pose and local facial animation such as eyelid movement and mouth movement. The framework firstly utilizes the Discriminative Shape Regression method to locate the facial feature points on the 2D image and fuses the 2D data with a 3D face model using Extended Kalman Filter to yield 3D facial movement information. An alternating optimizing strategy is adopted to fit to different persons automatically. Experiments show that the proposed framework could track the 3D facial movement across various poses and illumination conditions. Given the real face scale the framework could track the eyelid with an error of 1 mm and mouth with an error of 2 mm. The tracking result is reliable for expression analysis or mental state inference.

Significant body point labeling and tracking.

PubMed

Azhar, Faisal; Tjahjadi, Tardi

2014-09-01

In this paper, a method is presented to label and track anatomical landmarks (e.g., head, hand/arm, feet), which are referred to as significant body points (SBPs), using implicit body models. By considering the human body as an inverted pendulum model, ellipse fitting and contour moments are applied to classify it as being in Stand, Sit, or Lie posture. A convex hull of the silhouette contour is used to determine the locations of SBPs. The particle filter or a motion flow-based method is used to predict SBPs in occlusion. Stick figures of various activities are generated by connecting the SBPs. The qualitative and quantitative evaluation show that the proposed method robustly labels and tracks SBPs in various activities of two different (low and high) resolution data sets.

Extrapolating target tracks

NASA Astrophysics Data System (ADS)

Van Zandt, James R.

2012-05-01

Steady-state performance of a tracking filter is traditionally evaluated immediately after a track update. However, there is commonly a further delay (e.g., processing and communications latency) before the tracks can actually be used. We analyze the accuracy of extrapolated target tracks for four tracking filters: Kalman filter with the Singer maneuver model and worst-case correlation time, with piecewise constant white acceleration, and with continuous white acceleration, and the reduced state filter proposed by Mookerjee and Reifler.1, 2 Performance evaluation of a tracking filter is significantly simplified by appropriate normalization. For the Kalman filter with the Singer maneuver model, the steady-state RMS error immediately after an update depends on only two dimensionless parameters.3 By assuming a worst case value of target acceleration correlation time, we reduce this to a single parameter without significantly changing the filter performance (within a few percent for air tracking).4 With this simplification, we find for all four filters that the RMS errors for the extrapolated state are functions of only two dimensionless parameters. We provide simple analytic approximations in each case.

LHCb Kalman Filter cross architecture studies

NASA Astrophysics Data System (ADS)

Cámpora Pérez, Daniel Hugo

2017-10-01

The 2020 upgrade of the LHCb detector will vastly increase the rate of collisions the Online system needs to process in software, in order to filter events in real time. 30 million collisions per second will pass through a selection chain, where each step is executed conditional to its prior acceptance. The Kalman Filter is a fit applied to all reconstructed tracks which, due to its time characteristics and early execution in the selection chain, consumes 40% of the whole reconstruction time in the current trigger software. This makes the Kalman Filter a time-critical component as the LHCb trigger evolves into a full software trigger in the Upgrade. I present a new Kalman Filter algorithm for LHCb that can efficiently make use of any kind of SIMD processor, and its design is explained in depth. Performance benchmarks are compared between a variety of hardware architectures, including x86_64 and Power8, and the Intel Xeon Phi accelerator, and the suitability of said architectures to efficiently perform the LHCb Reconstruction process is determined.

Automatically Detect and Track Multiple Fish Swimming in Shallow Water with Frequent Occlusion

PubMed Central

Qian, Zhi-Ming; Cheng, Xi En; Chen, Yan Qiu

2014-01-01

Due to its universality, swarm behavior in nature attracts much attention of scientists from many fields. Fish schools are examples of biological communities that demonstrate swarm behavior. The detection and tracking of fish in a school are of important significance for the quantitative research on swarm behavior. However, different from other biological communities, there are three problems in the detection and tracking of fish school, that is, variable appearances, complex motion and frequent occlusion. To solve these problems, we propose an effective method of fish detection and tracking. In this method, first, the fish head region is positioned through extremum detection and ellipse fitting; second, The Kalman filtering and feature matching are used to track the target in complex motion; finally, according to the feature information obtained by the detection and tracking, the tracking problems caused by frequent occlusion are processed through trajectory linking. We apply this method to track swimming fish school of different densities. The experimental results show that the proposed method is both accurate and reliable. PMID:25207811

Handheld Synthetic Array Final Report, Part B

DTIC Science & Technology

2014-12-01

Multiple Model IMU Inertial Measurement Unit 4/154 IEEE Institute of Electrical and Electronics Engineers KF Kalman Filter KL Kullback - Leibler LAMBDA...important metric in information theory is the input–output mutual information (MI) that is used as an indicator of how much coded information can be...tracking using best- fitting Gaussian distributions,” Proc. Int. Conf. Inform . Fusion, pp. 1–8, 2005. [liv] L. Svensson, “On the Bayesian Cramér-Rao

Learned filters for object detection in multi-object visual tracking

NASA Astrophysics Data System (ADS)

Stamatescu, Victor; Wong, Sebastien; McDonnell, Mark D.; Kearney, David

2016-05-01

We investigate the application of learned convolutional filters in multi-object visual tracking. The filters were learned in both a supervised and unsupervised manner from image data using artificial neural networks. This work follows recent results in the field of machine learning that demonstrate the use learned filters for enhanced object detection and classification. Here we employ a track-before-detect approach to multi-object tracking, where tracking guides the detection process. The object detection provides a probabilistic input image calculated by selecting from features obtained using banks of generative or discriminative learned filters. We present a systematic evaluation of these convolutional filters using a real-world data set that examines their performance as generic object detectors.

Strong Tracking Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking.

PubMed

Liu, Hua; Wu, Wen

2017-03-31

Conventional spherical simplex-radial cubature Kalman filter (SSRCKF) for maneuvering target tracking may decline in accuracy and even diverge when a target makes abrupt state changes. To overcome this problem, a novel algorithm named strong tracking spherical simplex-radial cubature Kalman filter (STSSRCKF) is proposed in this paper. The proposed algorithm uses the spherical simplex-radial (SSR) rule to obtain a higher accuracy than cubature Kalman filter (CKF) algorithm. Meanwhile, by introducing strong tracking filter (STF) into SSRCKF and modifying the predicted states' error covariance with a time-varying fading factor, the gain matrix is adjusted on line so that the robustness of the filter and the capability of dealing with uncertainty factors is improved. In this way, the proposed algorithm has the advantages of both STF's strong robustness and SSRCKF's high accuracy. Finally, a maneuvering target tracking problem with abrupt state changes is used to test the performance of the proposed filter. Simulation results show that the STSSRCKF algorithm can get better estimation accuracy and greater robustness for maneuvering target tracking.

Strong Tracking Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking

PubMed Central

Liu, Hua; Wu, Wen

2017-01-01

Conventional spherical simplex-radial cubature Kalman filter (SSRCKF) for maneuvering target tracking may decline in accuracy and even diverge when a target makes abrupt state changes. To overcome this problem, a novel algorithm named strong tracking spherical simplex-radial cubature Kalman filter (STSSRCKF) is proposed in this paper. The proposed algorithm uses the spherical simplex-radial (SSR) rule to obtain a higher accuracy than cubature Kalman filter (CKF) algorithm. Meanwhile, by introducing strong tracking filter (STF) into SSRCKF and modifying the predicted states’ error covariance with a time-varying fading factor, the gain matrix is adjusted on line so that the robustness of the filter and the capability of dealing with uncertainty factors is improved. In this way, the proposed algorithm has the advantages of both STF’s strong robustness and SSRCKF’s high accuracy. Finally, a maneuvering target tracking problem with abrupt state changes is used to test the performance of the proposed filter. Simulation results show that the STSSRCKF algorithm can get better estimation accuracy and greater robustness for maneuvering target tracking. PMID:28362347

Interacting Multiple Model (IMM) Fifth-Degree Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking

PubMed Central

Liu, Hua; Wu, Wen

2017-01-01

For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named the interacting multiple model fifth-degree spherical simplex-radial cubature Kalman filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and the fifth-degree spherical simplex-radial cubature Kalman filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with the interacting multiple model unscented Kalman filter (IMMUKF), the interacting multiple model cubature Kalman filter (IMMCKF) and the interacting multiple model fifth-degree cubature Kalman filter (IMM5thCKF). PMID:28608843

Interacting Multiple Model (IMM) Fifth-Degree Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking.

PubMed

Liu, Hua; Wu, Wen

2017-06-13

For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named the interacting multiple model fifth-degree spherical simplex-radial cubature Kalman filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and the fifth-degree spherical simplex-radial cubature Kalman filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with the interacting multiple model unscented Kalman filter (IMMUKF), the interacting multiple model cubature Kalman filter (IMMCKF) and the interacting multiple model fifth-degree cubature Kalman filter (IMM5thCKF).

Correlation Filter Learning Toward Peak Strength for Visual Tracking.

PubMed

Sui, Yao; Wang, Guanghui; Zhang, Li

2018-04-01

This paper presents a novel visual tracking approach to correlation filter learning toward peak strength of correlation response. Previous methods leverage all features of the target and the immediate background to learn a correlation filter. Some features, however, may be distractive to tracking, like those from occlusion and local deformation, resulting in unstable tracking performance. This paper aims at solving this issue and proposes a novel algorithm to learn the correlation filter. The proposed approach, by imposing an elastic net constraint on the filter, can adaptively eliminate those distractive features in the correlation filtering. A new peak strength metric is proposed to measure the discriminative capability of the learned correlation filter. It is demonstrated that the proposed approach effectively strengthens the peak of the correlation response, leading to more discriminative performance than previous methods. Extensive experiments on a challenging visual tracking benchmark demonstrate that the proposed tracker outperforms most state-of-the-art methods.

Theatre Ballistic Missile Defense-Multisensor Fusion, Targeting and Tracking Techniques

DTIC Science & Technology

1998-03-01

Washington, D.C., 1994. 8. Brown , R., and Hwang , P., Introduction to Random Signals and Applied Kaiman Filtering, Third Edition, John Wiley and Sons...C. ADDING MEASUREMENT NOISE 15 III. EXTENDED KALMAN FILTER 19 A. DISCRETE TIME KALMAN FILTER 19 B. EXTENDED KALMAN FILTER 21 C. EKF IN TARGET...tracking algorithms. 17 18 in. EXTENDED KALMAN FILTER This chapter provides background information on the development of a tracking algorithm

Suppression of Biodynamic Interference by Adaptive Filtering

NASA Technical Reports Server (NTRS)

Velger, M.; Merhav, S. J.; Grunwald, A. J.

1984-01-01

Preliminary experimental results obtained in moving base simulator tests are presented. Both for pursuit and compensatory tracking tasks, a strong deterioration in tracking performance due to biodynamic interference is found. The use of adaptive filtering is shown to substantially alleviate these effects, resulting in a markedly improved tracking performance and reduction in task difficulty. The effect of simulator motion and of adaptive filtering on human operator describing functions is investigated. Adaptive filtering is found to substantially increase pilot gain and cross-over frequency, implying a more tight tracking behavior. The adaptive filter is found to be effective in particular for high-gain proportional dynamics, low display forcing function power and for pursuit tracking task configurations.

An Improved Interacting Multiple Model Filtering Algorithm Based on the Cubature Kalman Filter for Maneuvering Target Tracking.

PubMed

Zhu, Wei; Wang, Wei; Yuan, Gannan

2016-06-01

In order to improve the tracking accuracy, model estimation accuracy and quick response of multiple model maneuvering target tracking, the interacting multiple models five degree cubature Kalman filter (IMM5CKF) is proposed in this paper. In the proposed algorithm, the interacting multiple models (IMM) algorithm processes all the models through a Markov Chain to simultaneously enhance the model tracking accuracy of target tracking. Then a five degree cubature Kalman filter (5CKF) evaluates the surface integral by a higher but deterministic odd ordered spherical cubature rule to improve the tracking accuracy and the model switch sensitivity of the IMM algorithm. Finally, the simulation results demonstrate that the proposed algorithm exhibits quick and smooth switching when disposing different maneuver models, and it also performs better than the interacting multiple models cubature Kalman filter (IMMCKF), interacting multiple models unscented Kalman filter (IMMUKF), 5CKF and the optimal mode transition matrix IMM (OMTM-IMM).

Multisensor fusion for 3D target tracking using track-before-detect particle filter

NASA Astrophysics Data System (ADS)

Moshtagh, Nima; Romberg, Paul M.; Chan, Moses W.

2015-05-01

This work presents a novel fusion mechanism for estimating the three-dimensional trajectory of a moving target using images collected by multiple imaging sensors. The proposed projective particle filter avoids the explicit target detection prior to fusion. In projective particle filter, particles that represent the posterior density (of target state in a high-dimensional space) are projected onto the lower-dimensional observation space. Measurements are generated directly in the observation space (image plane) and a marginal (sensor) likelihood is computed. The particles states and their weights are updated using the joint likelihood computed from all the sensors. The 3D state estimate of target (system track) is then generated from the states of the particles. This approach is similar to track-before-detect particle filters that are known to perform well in tracking dim and stealthy targets in image collections. Our approach extends the track-before-detect approach to 3D tracking using the projective particle filter. The performance of this measurement-level fusion method is compared with that of a track-level fusion algorithm using the projective particle filter. In the track-level fusion algorithm, the 2D sensor tracks are generated separately and transmitted to a fusion center, where they are treated as measurements to the state estimator. The 2D sensor tracks are then fused to reconstruct the system track. A realistic synthetic scenario with a boosting target was generated, and used to study the performance of the fusion mechanisms.

Tracking and people counting using Particle Filter Method

NASA Astrophysics Data System (ADS)

Sulistyaningrum, D. R.; Setiyono, B.; Rizky, M. S.

2018-03-01

In recent years, technology has developed quite rapidly, especially in the field of object tracking. Moreover, if the object under study is a person and the number of people a lot. The purpose of this research is to apply Particle Filter method for tracking and counting people in certain area. Tracking people will be rather difficult if there are some obstacles, one of which is occlusion. The stages of tracking and people counting scheme in this study include pre-processing, segmentation using Gaussian Mixture Model (GMM), tracking using particle filter, and counting based on centroid. The Particle Filter method uses the estimated motion included in the model used. The test results show that the tracking and people counting can be done well with an average accuracy of 89.33% and 77.33% respectively from six videos test data. In the process of tracking people, the results are good if there is partial occlusion and no occlusion

Robotic fish tracking method based on suboptimal interval Kalman filter

NASA Astrophysics Data System (ADS)

Tong, Xiaohong; Tang, Chao

2017-11-01

Autonomous Underwater Vehicle (AUV) research focused on tracking and positioning, precise guidance and return to dock and other fields. The robotic fish of AUV has become a hot application in intelligent education, civil and military etc. In nonlinear tracking analysis of robotic fish, which was found that the interval Kalman filter algorithm contains all possible filter results, but the range is wide, relatively conservative, and the interval data vector is uncertain before implementation. This paper proposes a ptimization algorithm of suboptimal interval Kalman filter. Suboptimal interval Kalman filter scheme used the interval inverse matrix with its worst inverse instead, is more approximate nonlinear state equation and measurement equation than the standard interval Kalman filter, increases the accuracy of the nominal dynamic system model, improves the speed and precision of tracking system. Monte-Carlo simulation results show that the optimal trajectory of sub optimal interval Kalman filter algorithm is better than that of the interval Kalman filter method and the standard method of the filter.

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

DOE PAGES

Farrell, Steven; Anderson, Dustin; Calafiura, Paolo; ...

2017-08-08

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problemmore » thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. Furthermore, we will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data.« less

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Farrell, Steven; Anderson, Dustin; Calafiura, Paolo

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problemmore » thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. Furthermore, we will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data.« less

The HEP.TrkX Project: deep neural networks for HL-LHC online and offline tracking

NASA Astrophysics Data System (ADS)

Farrell, Steven; Anderson, Dustin; Calafiura, Paolo; Cerati, Giuseppe; Gray, Lindsey; Kowalkowski, Jim; Mudigonda, Mayur; Prabhat; Spentzouris, Panagiotis; Spiropoulou, Maria; Tsaris, Aristeidis; Vlimant, Jean-Roch; Zheng, Stephan

2017-08-01

Particle track reconstruction in dense environments such as the detectors of the High Luminosity Large Hadron Collider (HL-LHC) is a challenging pattern recognition problem. Traditional tracking algorithms such as the combinatorial Kalman Filter have been used with great success in LHC experiments for years. However, these state-of-the-art techniques are inherently sequential and scale poorly with the expected increases in detector occupancy in the HL-LHC conditions. The HEP.TrkX project is a pilot project with the aim to identify and develop cross-experiment solutions based on machine learning algorithms for track reconstruction. Machine learning algorithms bring a lot of potential to this problem thanks to their capability to model complex non-linear data dependencies, to learn effective representations of high-dimensional data through training, and to parallelize easily on high-throughput architectures such as GPUs. This contribution will describe our initial explorations into this relatively unexplored idea space. We will discuss the use of recurrent (LSTM) and convolutional neural networks to find and fit tracks in toy detector data.

Visual object tracking by correlation filters and online learning

NASA Astrophysics Data System (ADS)

Zhang, Xin; Xia, Gui-Song; Lu, Qikai; Shen, Weiming; Zhang, Liangpei

2018-06-01

Due to the complexity of background scenarios and the variation of target appearance, it is difficult to achieve high accuracy and fast speed for object tracking. Currently, correlation filters based trackers (CFTs) show promising performance in object tracking. The CFTs estimate the target's position by correlation filters with different kinds of features. However, most of CFTs can hardly re-detect the target in the case of long-term tracking drifts. In this paper, a feature integration object tracker named correlation filters and online learning (CFOL) is proposed. CFOL estimates the target's position and its corresponding correlation score using the same discriminative correlation filter with multi-features. To reduce tracking drifts, a new sampling and updating strategy for online learning is proposed. Experiments conducted on 51 image sequences demonstrate that the proposed algorithm is superior to the state-of-the-art approaches.

Development and validation of a Kalman filter-based model for vehicle slip angle estimation

NASA Astrophysics Data System (ADS)

Gadola, M.; Chindamo, D.; Romano, M.; Padula, F.

2014-01-01

It is well known that vehicle slip angle is one of the most difficult parameters to measure on a vehicle during testing or racing activities. Moreover, the appropriate sensor is very expensive and it is often difficult to fit to a car, especially on race cars. We propose here a strategy to eliminate the need for this sensor by using a mathematical tool which gives a good estimation of the vehicle slip angle. A single-track car model, coupled with an extended Kalman filter, was used in order to achieve the result. Moreover, a tuning procedure is proposed that takes into consideration both nonlinear and saturation characteristics typical of vehicle lateral dynamics. The effectiveness of the proposed algorithm has been proven by both simulation results and real-world data.

An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers.

PubMed

Luo, Zhibin; Ding, Jicheng; Zhao, Lin; Wu, Mouyan

2017-11-18

Tracking error estimation is of great importance in global navigation satellite system (GNSS) receivers. Any inaccurate estimation for tracking error will decrease the signal tracking ability of signal tracking loops and the accuracies of position fixing, velocity determination, and timing. Tracking error estimation can be done by traditional discriminator, or Kalman filter-based pre-filter. The pre-filter can be divided into two categories: coherent and non-coherent. This paper focuses on the performance improvements of non-coherent pre-filter. Firstly, the signal characteristics of coherent and non-coherent integration-which are the basis of tracking error estimation-are analyzed in detail. After that, the probability distribution of estimation noise of four-quadrant arctangent (ATAN2) discriminator is derived according to the mathematical model of coherent integration. Secondly, the statistical property of observation noise of non-coherent pre-filter is studied through Monte Carlo simulation to set the observation noise variance matrix correctly. Thirdly, a simple fault detection and exclusion (FDE) structure is introduced to the non-coherent pre-filter design, and thus its effective working range for carrier phase error estimation extends from (-0.25 cycle, 0.25 cycle) to (-0.5 cycle, 0.5 cycle). Finally, the estimation accuracies of discriminator, coherent pre-filter, and the enhanced non-coherent pre-filter are evaluated comprehensively through the carefully designed experiment scenario. The pre-filter outperforms traditional discriminator in estimation accuracy. In a highly dynamic scenario, the enhanced non-coherent pre-filter provides accuracy improvements of 41.6%, 46.4%, and 50.36% for carrier phase error, carrier frequency error, and code phase error estimation, respectively, when compared with coherent pre-filter. The enhanced non-coherent pre-filter outperforms the coherent pre-filter in code phase error estimation when carrier-to-noise density ratio is less than 28.8 dB-Hz, in carrier frequency error estimation when carrier-to-noise density ratio is less than 20 dB-Hz, and in carrier phase error estimation when carrier-to-noise density belongs to (15, 23) dB-Hz ∪ (26, 50) dB-Hz.

An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers

PubMed Central

Luo, Zhibin; Ding, Jicheng; Zhao, Lin; Wu, Mouyan

2017-01-01

Tracking error estimation is of great importance in global navigation satellite system (GNSS) receivers. Any inaccurate estimation for tracking error will decrease the signal tracking ability of signal tracking loops and the accuracies of position fixing, velocity determination, and timing. Tracking error estimation can be done by traditional discriminator, or Kalman filter-based pre-filter. The pre-filter can be divided into two categories: coherent and non-coherent. This paper focuses on the performance improvements of non-coherent pre-filter. Firstly, the signal characteristics of coherent and non-coherent integration—which are the basis of tracking error estimation—are analyzed in detail. After that, the probability distribution of estimation noise of four-quadrant arctangent (ATAN2) discriminator is derived according to the mathematical model of coherent integration. Secondly, the statistical property of observation noise of non-coherent pre-filter is studied through Monte Carlo simulation to set the observation noise variance matrix correctly. Thirdly, a simple fault detection and exclusion (FDE) structure is introduced to the non-coherent pre-filter design, and thus its effective working range for carrier phase error estimation extends from (−0.25 cycle, 0.25 cycle) to (−0.5 cycle, 0.5 cycle). Finally, the estimation accuracies of discriminator, coherent pre-filter, and the enhanced non-coherent pre-filter are evaluated comprehensively through the carefully designed experiment scenario. The pre-filter outperforms traditional discriminator in estimation accuracy. In a highly dynamic scenario, the enhanced non-coherent pre-filter provides accuracy improvements of 41.6%, 46.4%, and 50.36% for carrier phase error, carrier frequency error, and code phase error estimation, respectively, when compared with coherent pre-filter. The enhanced non-coherent pre-filter outperforms the coherent pre-filter in code phase error estimation when carrier-to-noise density ratio is less than 28.8 dB-Hz, in carrier frequency error estimation when carrier-to-noise density ratio is less than 20 dB-Hz, and in carrier phase error estimation when carrier-to-noise density belongs to (15, 23) dB-Hz ∪ (26, 50) dB-Hz. PMID:29156581

Angle only tracking with particle flow filters

NASA Astrophysics Data System (ADS)

Daum, Fred; Huang, Jim

2011-09-01

We show the results of numerical experiments for tracking ballistic missiles using only angle measurements. We compare the performance of an extended Kalman filter with a new nonlinear filter using particle flow to compute Bayes' rule. For certain difficult geometries, the particle flow filter is an order of magnitude more accurate than the EKF. Angle only tracking is of interest in several different sensors; for example, passive optics and radars in which range and Doppler data are spoiled by jamming.

Feature aided Monte Carlo probabilistic data association filter for ballistic missile tracking

NASA Astrophysics Data System (ADS)

Ozdemir, Onur; Niu, Ruixin; Varshney, Pramod K.; Drozd, Andrew L.; Loe, Richard

2011-05-01

The problem of ballistic missile tracking in the presence of clutter is investigated. Probabilistic data association filter (PDAF) is utilized as the basic filtering algorithm. We propose to use sequential Monte Carlo methods, i.e., particle filters, aided with amplitude information (AI) in order to improve the tracking performance of a single target in clutter when severe nonlinearities exist in the system. We call this approach "Monte Carlo probabilistic data association filter with amplitude information (MCPDAF-AI)." Furthermore, we formulate a realistic problem in the sense that we use simulated radar cross section (RCS) data for a missile warhead and a cylinder chaff using Lucernhammer1, a state of the art electromagnetic signature prediction software, to model target and clutter amplitude returns as additional amplitude features which help to improve data association and tracking performance. A performance comparison is carried out between the extended Kalman filter (EKF) and the particle filter under various scenarios using single and multiple sensors. The results show that, when only one sensor is used, the MCPDAF performs significantly better than the EKF in terms of tracking accuracy under severe nonlinear conditions for ballistic missile tracking applications. However, when the number of sensors is increased, even under severe nonlinear conditions, the EKF performs as well as the MCPDAF.

Using MaxCompiler for the high level synthesis of trigger algorithms

NASA Astrophysics Data System (ADS)

Summers, S.; Rose, A.; Sanders, P.

2017-02-01

Firmware for FPGA trigger applications at the CMS experiment is conventionally written using hardware description languages such as Verilog and VHDL. MaxCompiler is an alternative, Java based, tool for developing FPGA applications which uses a higher level of abstraction from the hardware than a hardware description language. An implementation of the jet and energy sum algorithms for the CMS Level-1 calorimeter trigger has been written using MaxCompiler to benchmark against the VHDL implementation in terms of accuracy, latency, resource usage, and code size. A Kalman Filter track fitting algorithm has been developed using MaxCompiler for a proposed CMS Level-1 track trigger for the High-Luminosity LHC upgrade. The design achieves a low resource usage, and has a latency of 187.5 ns per iteration.

Trends in Correlation-Based Pattern Recognition and Tracking in Forward-Looking Infrared Imagery

PubMed Central

Alam, Mohammad S.; Bhuiyan, Sharif M. A.

2014-01-01

In this paper, we review the recent trends and advancements on correlation-based pattern recognition and tracking in forward-looking infrared (FLIR) imagery. In particular, we discuss matched filter-based correlation techniques for target detection and tracking which are widely used for various real time applications. We analyze and present test results involving recently reported matched filters such as the maximum average correlation height (MACH) filter and its variants, and distance classifier correlation filter (DCCF) and its variants. Test results are presented for both single/multiple target detection and tracking using various real-life FLIR image sequences. PMID:25061840

Dual linear structured support vector machine tracking method via scale correlation filter

NASA Astrophysics Data System (ADS)

Li, Weisheng; Chen, Yanquan; Xiao, Bin; Feng, Chen

2018-01-01

Adaptive tracking-by-detection methods based on structured support vector machine (SVM) performed well on recent visual tracking benchmarks. However, these methods did not adopt an effective strategy of object scale estimation, which limits the overall tracking performance. We present a tracking method based on a dual linear structured support vector machine (DLSSVM) with a discriminative scale correlation filter. The collaborative tracker comprised of a DLSSVM model and a scale correlation filter obtains good results in tracking target position and scale estimation. The fast Fourier transform is applied for detection. Extensive experiments show that our tracking approach outperforms many popular top-ranking trackers. On a benchmark including 100 challenging video sequences, the average precision of the proposed method is 82.8%.

Labeled RFS-Based Track-Before-Detect for Multiple Maneuvering Targets in the Infrared Focal Plane Array.

PubMed

Li, Miao; Li, Jun; Zhou, Yiyu

2015-12-08

The problem of jointly detecting and tracking multiple targets from the raw observations of an infrared focal plane array is a challenging task, especially for the case with uncertain target dynamics. In this paper a multi-model labeled multi-Bernoulli (MM-LMB) track-before-detect method is proposed within the labeled random finite sets (RFS) framework. The proposed track-before-detect method consists of two parts-MM-LMB filter and MM-LMB smoother. For the MM-LMB filter, original LMB filter is applied to track-before-detect based on target and measurement models, and is integrated with the interacting multiple models (IMM) approach to accommodate the uncertainty of target dynamics. For the MM-LMB smoother, taking advantage of the track labels and posterior model transition probability, the single-model single-target smoother is extended to a multi-model multi-target smoother. A Sequential Monte Carlo approach is also presented to implement the proposed method. Simulation results show the proposed method can effectively achieve tracking continuity for multiple maneuvering targets. In addition, compared with the forward filtering alone, our method is more robust due to its combination of forward filtering and backward smoothing.

Labeled RFS-Based Track-Before-Detect for Multiple Maneuvering Targets in the Infrared Focal Plane Array

PubMed Central

Li, Miao; Li, Jun; Zhou, Yiyu

2015-01-01

The problem of jointly detecting and tracking multiple targets from the raw observations of an infrared focal plane array is a challenging task, especially for the case with uncertain target dynamics. In this paper a multi-model labeled multi-Bernoulli (MM-LMB) track-before-detect method is proposed within the labeled random finite sets (RFS) framework. The proposed track-before-detect method consists of two parts—MM-LMB filter and MM-LMB smoother. For the MM-LMB filter, original LMB filter is applied to track-before-detect based on target and measurement models, and is integrated with the interacting multiple models (IMM) approach to accommodate the uncertainty of target dynamics. For the MM-LMB smoother, taking advantage of the track labels and posterior model transition probability, the single-model single-target smoother is extended to a multi-model multi-target smoother. A Sequential Monte Carlo approach is also presented to implement the proposed method. Simulation results show the proposed method can effectively achieve tracking continuity for multiple maneuvering targets. In addition, compared with the forward filtering alone, our method is more robust due to its combination of forward filtering and backward smoothing. PMID:26670234

Tracking Algorithm of Multiple Pedestrians Based on Particle Filters in Video Sequences

PubMed Central

Liu, Yun; Wang, Chuanxu; Zhang, Shujun; Cui, Xuehong

2016-01-01

Pedestrian tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in pedestrian tracking for nonlinear and non-Gaussian estimation problems. However, pedestrian tracking in complex environment is still facing many problems due to changes of pedestrian postures and scale, moving background, mutual occlusion, and presence of pedestrian. To surmount these difficulties, this paper presents tracking algorithm of multiple pedestrians based on particle filters in video sequences. The algorithm acquires confidence value of the object and the background through extracting a priori knowledge thus to achieve multipedestrian detection; it adopts color and texture features into particle filter to get better observation results and then automatically adjusts weight value of each feature according to current tracking environment. During the process of tracking, the algorithm processes severe occlusion condition to prevent drift and loss phenomena caused by object occlusion and associates detection results with particle state to propose discriminated method for object disappearance and emergence thus to achieve robust tracking of multiple pedestrians. Experimental verification and analysis in video sequences demonstrate that proposed algorithm improves the tracking performance and has better tracking results. PMID:27847514

Adaptive particle filter for robust visual tracking

NASA Astrophysics Data System (ADS)

Dai, Jianghua; Yu, Shengsheng; Sun, Weiping; Chen, Xiaoping; Xiang, Jinhai

2009-10-01

Object tracking plays a key role in the field of computer vision. Particle filter has been widely used for visual tracking under nonlinear and/or non-Gaussian circumstances. In particle filter, the state transition model for predicting the next location of tracked object assumes the object motion is invariable, which cannot well approximate the varying dynamics of the motion changes. In addition, the state estimate calculated by the mean of all the weighted particles is coarse or inaccurate due to various noise disturbances. Both these two factors may degrade tracking performance greatly. In this work, an adaptive particle filter (APF) with a velocity-updating based transition model (VTM) and an adaptive state estimate approach (ASEA) is proposed to improve object tracking. In APF, the motion velocity embedded into the state transition model is updated continuously by a recursive equation, and the state estimate is obtained adaptively according to the state posterior distribution. The experiment results show that the APF can increase the tracking accuracy and efficiency in complex environments.

An Improved Strong Tracking Cubature Kalman Filter for GPS/INS Integrated Navigation Systems.

PubMed

Feng, Kaiqiang; Li, Jie; Zhang, Xi; Zhang, Xiaoming; Shen, Chong; Cao, Huiliang; Yang, Yanyu; Liu, Jun

2018-06-12

The cubature Kalman filter (CKF) is widely used in the application of GPS/INS integrated navigation systems. However, its performance may decline in accuracy and even diverge in the presence of process uncertainties. To solve the problem, a new algorithm named improved strong tracking seventh-degree spherical simplex-radial cubature Kalman filter (IST-7thSSRCKF) is proposed in this paper. In the proposed algorithm, the effect of process uncertainty is mitigated by using the improved strong tracking Kalman filter technique, in which the hypothesis testing method is adopted to identify the process uncertainty and the prior state estimate covariance in the CKF is further modified online according to the change in vehicle dynamics. In addition, a new seventh-degree spherical simplex-radial rule is employed to further improve the estimation accuracy of the strong tracking cubature Kalman filter. In this way, the proposed comprehensive algorithm integrates the advantage of 7thSSRCKF’s high accuracy and strong tracking filter’s strong robustness against process uncertainties. The GPS/INS integrated navigation problem with significant dynamic model errors is utilized to validate the performance of proposed IST-7thSSRCKF. Results demonstrate that the improved strong tracking cubature Kalman filter can achieve higher accuracy than the existing CKF and ST-CKF, and is more robust for the GPS/INS integrated navigation system.

Comparison of field portable measurements of ultrafine TiO2: X-ray fluorescence, laser-induced breakdown spectroscopy, and Fourier-transform infrared spectroscopy.

PubMed

LeBouf, Ryan F; Miller, Arthur L; Stipe, Christopher; Brown, Jonathan; Murphy, Nate; Stefaniak, Aleksandr B

2013-06-01

Laboratory measurements of ultrafine titanium dioxide (TiO2) particulate matter loaded on filters were made using three field portable methods (X-ray fluorescence (XRF), laser-induced breakdown spectroscopy (LIBS), and Fourier-transform infrared (FTIR) spectroscopy) to assess their potential for determining end-of-shift exposure. Ultrafine TiO2 particles were aerosolized and collected onto 37 mm polycarbonate track-etched (PCTE) filters in the range of 3 to 578 μg titanium (Ti). Limit of detection (LOD), limit of quantification (LOQ), and calibration fit were determined for each measurement method. The LOD's were 11.8, 0.032, and 108 μg Ti per filter, for XRF, LIBS, and FTIR, respectively and the LOQ's were 39.2, 0.11, and 361 μg Ti per filter, respectively. The XRF calibration curve was linear over the widest dynamic range, up to the maximum loading tested (578 μg Ti per filter). LIBS was more sensitive but, due to the sample preparation method, the highest loaded filter measurable was 252 μg Ti per filter. XRF and LIBS had good predictability measured by regressing the predicted mass to the gravimetric mass on the filter. XRF and LIBS produced overestimations of 4% and 2%, respectively, with coefficients of determination (R(2)) of 0.995 and 0.998. FTIR measurements were less dependable due to interference from the PCTE filter media and overestimated mass by 2% with an R(2) of 0.831.

Multitarget mixture reduction algorithm with incorporated target existence recursions

NASA Astrophysics Data System (ADS)

Ristic, Branko; Arulampalam, Sanjeev

2000-07-01

The paper derives a deferred logic data association algorithm based on the mixture reduction approach originally due to Salmond [SPIE vol.1305, 1990]. The novelty of the proposed algorithm provides the recursive formulae for both data association and target existence (confidence) estimation, thus allowing automatic track initiation and termination. T he track initiation performance of the proposed filter is investigated by computer simulations. It is observed that at moderately high levels of clutter density the proposed filter initiates tracks more reliably than its corresponding PDA filter. An extension of the proposed filter to the multi-target case is also presented. In addition, the paper compares the track maintenance performance of the MR algorithm with an MHT implementation.

A data processing method based on tracking light spot for the laser differential confocal component parameters measurement system

NASA Astrophysics Data System (ADS)

Shao, Rongjun; Qiu, Lirong; Yang, Jiamiao; Zhao, Weiqian; Zhang, Xin

2013-12-01

We have proposed the component parameters measuring method based on the differential confocal focusing theory. In order to improve the positioning precision of the laser differential confocal component parameters measurement system (LDDCPMS), the paper provides a data processing method based on tracking light spot. To reduce the error caused by the light point moving in collecting the axial intensity signal, the image centroiding algorithm is used to find and track the center of Airy disk of the images collected by the laser differential confocal system. For weakening the influence of higher harmonic noises during the measurement, Gaussian filter is used to process the axial intensity signal. Ultimately the zero point corresponding to the focus of the objective in a differential confocal system is achieved by linear fitting for the differential confocal axial intensity data. Preliminary experiments indicate that the method based on tracking light spot can accurately collect the axial intensity response signal of the virtual pinhole, and improve the anti-interference ability of system. Thus it improves the system positioning accuracy.

Performance evaluation of an asynchronous multisensor track fusion filter

NASA Astrophysics Data System (ADS)

Alouani, Ali T.; Gray, John E.; McCabe, D. H.

2003-08-01

Recently the authors developed a new filter that uses data generated by asynchronous sensors to produce a state estimate that is optimal in the minimum mean square sense. The solution accounts for communications delay between sensors platform and fusion center. It also deals with out of sequence data as well as latent data by processing the information in a batch-like manner. This paper compares, using simulated targets and Monte Carlo simulations, the performance of the filter to the optimal sequential processing approach. It was found that the new asynchronous Multisensor track fusion filter (AMSTFF) performance is identical to that of the extended sequential Kalman filter (SEKF), while the new filter updates its track at a much lower rate than the SEKF.

Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods.

PubMed

Hoak, Anthony; Medeiros, Henry; Povinelli, Richard J

2017-03-03

We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, Australian Rules Football League (AFL) and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (optimal sub-pattern assignment (OSPA) and classification of events, activities and relationships for multi-object trackers (CLEAR MOT)). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

Image-Based Multi-Target Tracking through Multi-Bernoulli Filtering with Interactive Likelihoods

PubMed Central

Hoak, Anthony; Medeiros, Henry; Povinelli, Richard J.

2017-01-01

We develop an interactive likelihood (ILH) for sequential Monte Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, Australian Rules Football League (AFL) and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (optimal sub-pattern assignment (OSPA) and classification of events, activities and relationships for multi-object trackers (CLEAR MOT)). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter. PMID:28273796

Alternatives to an extended Kalman Filter for target image tracking

NASA Astrophysics Data System (ADS)

Leuthauser, P. R.

1981-12-01

Four alternative filters are compared to an extended Kalman filter (EKF) algorithm for tracking a distributed (elliptical) source target in a closed loop tracking problem, using outputs from a forward looking (FLIR) sensor as measurements. These were (1) an EKF with (second order) bias correction term, (2) a constant gain EKF, (3) a constant gain EKF with bias correction term, and (4) a statistically linearized filter. Estimates are made of both actual target motion and of apparent motion due to atmospheric jitter. These alternative designs are considered specifically to address some of the significant biases exhibited by an EKF due to initial acquisition difficulties, unmodelled maneuvering by the target, low signal-to-noise ratio, and real world conditions varying significantly from those assumed in the filter design (robustness). Filter performance was determined with a Monte Carlo study under both ideal and non ideal conditions for tracking targets on a constant velocity cross range path, and during constant acceleration turns of 5G, 10G, and 20G.

Target Tracking Using SePDAF under Ambiguous Angles for Distributed Array Radar.

PubMed

Long, Teng; Zhang, Honggang; Zeng, Tao; Chen, Xinliang; Liu, Quanhua; Zheng, Le

2016-09-09

Distributed array radar can improve radar detection capability and measurement accuracy. However, it will suffer cyclic ambiguity in its angle estimates according to the spatial Nyquist sampling theorem since the large sparse array is undersampling. Consequently, the state estimation accuracy and track validity probability degrades when the ambiguous angles are directly used for target tracking. This paper proposes a second probability data association filter (SePDAF)-based tracking method for distributed array radar. Firstly, the target motion model and radar measurement model is built. Secondly, the fusion result of each radar's estimation is employed to the extended Kalman filter (EKF) to finish the first filtering. Thirdly, taking this result as prior knowledge, and associating with the array-processed ambiguous angles, the SePDAF is applied to accomplish the second filtering, and then achieving a high accuracy and stable trajectory with relatively low computational complexity. Moreover, the azimuth filtering accuracy will be promoted dramatically and the position filtering accuracy will also improve. Finally, simulations illustrate the effectiveness of the proposed method.

Extending Correlation Filter-Based Visual Tracking by Tree-Structured Ensemble and Spatial Windowing.

PubMed

Gundogdu, Erhan; Ozkan, Huseyin; Alatan, A Aydin

2017-11-01

Correlation filters have been successfully used in visual tracking due to their modeling power and computational efficiency. However, the state-of-the-art correlation filter-based (CFB) tracking algorithms tend to quickly discard the previous poses of the target, since they consider only a single filter in their models. On the contrary, our approach is to register multiple CFB trackers for previous poses and exploit the registered knowledge when an appearance change occurs. To this end, we propose a novel tracking algorithm [of complexity O(D) ] based on a large ensemble of CFB trackers. The ensemble [of size O(2 D ) ] is organized over a binary tree (depth D ), and learns the target appearance subspaces such that each constituent tracker becomes an expert of a certain appearance. During tracking, the proposed algorithm combines only the appearance-aware relevant experts to produce boosted tracking decisions. Additionally, we propose a versatile spatial windowing technique to enhance the individual expert trackers. For this purpose, spatial windows are learned for target objects as well as the correlation filters and then the windowed regions are processed for more robust correlations. In our extensive experiments on benchmark datasets, we achieve a substantial performance increase by using the proposed tracking algorithm together with the spatial windowing.

Accurate mask-based spatially regularized correlation filter for visual tracking

NASA Astrophysics Data System (ADS)

Gu, Xiaodong; Xu, Xinping

2017-01-01

Recently, discriminative correlation filter (DCF)-based trackers have achieved extremely successful results in many competitions and benchmarks. These methods utilize a periodic assumption of the training samples to efficiently learn a classifier. However, this assumption will produce unwanted boundary effects, which severely degrade the tracking performance. Correlation filters with limited boundaries and spatially regularized DCFs were proposed to reduce boundary effects. However, their methods used the fixed mask or predesigned weights function, respectively, which was unsuitable for large appearance variation. We propose an accurate mask-based spatially regularized correlation filter for visual tracking. Our augmented objective can reduce the boundary effect even in large appearance variation. In our algorithm, the masking matrix is converted into the regularized function that acts on the correlation filter in frequency domain, which makes the algorithm fast convergence. Our online tracking algorithm performs favorably against state-of-the-art trackers on OTB-2015 Benchmark in terms of efficiency, accuracy, and robustness.

New color-based tracking algorithm for joints of the upper extremities

NASA Astrophysics Data System (ADS)

Wu, Xiangping; Chow, Daniel H. K.; Zheng, Xiaoxiang

2007-11-01

To track the joints of the upper limb of stroke sufferers for rehabilitation assessment, a new tracking algorithm which utilizes a developed color-based particle filter and a novel strategy for handling occlusions is proposed in this paper. Objects are represented by their color histogram models and particle filter is introduced to track the objects within a probability framework. Kalman filter, as a local optimizer, is integrated into the sampling stage of the particle filter that steers samples to a region with high likelihood and therefore fewer samples is required. A color clustering method and anatomic constraints are used in dealing with occlusion problem. Compared with the general basic particle filtering method, the experimental results show that the new algorithm has reduced the number of samples and hence the computational consumption, and has achieved better abilities of handling complete occlusion over a few frames.

Evaluation of an image-based tracking workflow with Kalman filtering for automatic image plane alignment in interventional MRI.

PubMed

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.

Enhanced online convolutional neural networks for object tracking

NASA Astrophysics Data System (ADS)

Zhang, Dengzhuo; Gao, Yun; Zhou, Hao; Li, Tianwen

2018-04-01

In recent several years, object tracking based on convolution neural network has gained more and more attention. The initialization and update of convolution filters can directly affect the precision of object tracking effective. In this paper, a novel object tracking via an enhanced online convolution neural network without offline training is proposed, which initializes the convolution filters by a k-means++ algorithm and updates the filters by an error back-propagation. The comparative experiments of 7 trackers on 15 challenging sequences showed that our tracker can perform better than other trackers in terms of AUC and precision.

Track-before-detect labeled multi-bernoulli particle filter with label switching

NASA Astrophysics Data System (ADS)

Garcia-Fernandez, Angel F.

2016-10-01

This paper presents a multitarget tracking particle filter (PF) for general track-before-detect measurement models. The PF is presented in the random finite set framework and uses a labelled multi-Bernoulli approximation. We also present a label switching improvement algorithm based on Markov chain Monte Carlo that is expected to increase filter performance if targets get in close proximity for a sufficiently long time. The PF is tested in two challenging numerical examples.

An object tracking method based on guided filter for night fusion image

NASA Astrophysics Data System (ADS)

Qian, Xiaoyan; Wang, Yuedong; Han, Lei

2016-01-01

Online object tracking is a challenging problem as it entails learning an effective model to account for appearance change caused by intrinsic and extrinsic factors. In this paper, we propose a novel online object tracking with guided image filter for accurate and robust night fusion image tracking. Firstly, frame difference is applied to produce the coarse target, which helps to generate observation models. Under the restriction of these models and local source image, guided filter generates sufficient and accurate foreground target. Then accurate boundaries of the target can be extracted from detection results. Finally timely updating for observation models help to avoid tracking shift. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-art methods.

Noncoherent pseudonoise code tracking performance of spread spectrum receivers

NASA Technical Reports Server (NTRS)

Simon, M. K.

1977-01-01

The optimum design and performance of two noncoherent PN tracking loop configurations, namely, the delay-locked loop and tau-dither loop, are described. In particular, the bandlimiting effects of the bandpass arm filters are considered by demonstrating that for a fixed data rate and data signal-to-noise ratio, there exists an optimum filter bandwidth in the sense of minimizing the loop's tracking jitter. Both the linear and nonlinear loop analyses are presented, and the region of validity of the former relative to the latter is indicated. In addition, numerical results are given for several filter types. For example, assuming ideal bandpass arm filters, it is shown that the tau-dither loop requires approximately 1 dB more signal-to-noise ratio than the delay-locked loop for equal rms tracking jitters.

Estimation of three-dimensional radar tracking using modified extended kalman filter

NASA Astrophysics Data System (ADS)

Aditya, Prima; Apriliani, Erna; Khusnul Arif, Didik; Baihaqi, Komar

2018-03-01

Kalman filter is an estimation method by combining data and mathematical models then developed be extended Kalman filter to handle nonlinear systems. Three-dimensional radar tracking is one of example of nonlinear system. In this paper developed a modification method of extended Kalman filter from the direct decline of the three-dimensional radar tracking case. The development of this filter algorithm can solve the three-dimensional radar measurements in the case proposed in this case the target measured by radar with distance r, azimuth angle θ, and the elevation angle ϕ. Artificial covariance and mean adjusted directly on the three-dimensional radar system. Simulations result show that the proposed formulation is effective in the calculation of nonlinear measurement compared with extended Kalman filter with the value error at 0.77% until 1.15%.

Target Tracking Using SePDAF under Ambiguous Angles for Distributed Array Radar

PubMed Central

Long, Teng; Zhang, Honggang; Zeng, Tao; Chen, Xinliang; Liu, Quanhua; Zheng, Le

2016-01-01

Distributed array radar can improve radar detection capability and measurement accuracy. However, it will suffer cyclic ambiguity in its angle estimates according to the spatial Nyquist sampling theorem since the large sparse array is undersampling. Consequently, the state estimation accuracy and track validity probability degrades when the ambiguous angles are directly used for target tracking. This paper proposes a second probability data association filter (SePDAF)-based tracking method for distributed array radar. Firstly, the target motion model and radar measurement model is built. Secondly, the fusion result of each radar’s estimation is employed to the extended Kalman filter (EKF) to finish the first filtering. Thirdly, taking this result as prior knowledge, and associating with the array-processed ambiguous angles, the SePDAF is applied to accomplish the second filtering, and then achieving a high accuracy and stable trajectory with relatively low computational complexity. Moreover, the azimuth filtering accuracy will be promoted dramatically and the position filtering accuracy will also improve. Finally, simulations illustrate the effectiveness of the proposed method. PMID:27618058

Joint polarization tracking and channel equalization based on radius-directed linear Kalman filter

NASA Astrophysics Data System (ADS)

Zhang, Qun; Yang, Yanfu; Zhong, Kangping; Liu, Jie; Wu, Xiong; Yao, Yong

2018-01-01

We propose a joint polarization tracking and channel equalization scheme based on radius-directed linear Kalman filter (RD-LKF) by introducing the butterfly finite-impulse-response (FIR) filter in our previously proposed RD-LKF method. Along with the fast polarization tracking, it can also simultaneously compensate the inter-symbol interference (ISI) effects including residual chromatic dispersion and polarization mode dispersion. Compared with the conventional radius-directed equalizer (RDE) algorithm, it is demonstrated experimentally that three times faster convergence speed, one order of magnitude better tracking capability, and better BER performance is obtained in polarization division multiplexing 16 quadrature amplitude modulation system. Besides, the influences of the algorithm parameters on the convergence and the tracking performance are investigated by numerical simulation.

Particle Filtering for Obstacle Tracking in UAS Sense and Avoid Applications

PubMed Central

Moccia, Antonio

2014-01-01

Obstacle detection and tracking is a key function for UAS sense and avoid applications. In fact, obstacles in the flight path must be detected and tracked in an accurate and timely manner in order to execute a collision avoidance maneuver in case of collision threat. The most important parameter for the assessment of a collision risk is the Distance at Closest Point of Approach, that is, the predicted minimum distance between own aircraft and intruder for assigned current position and speed. Since assessed methodologies can cause some loss of accuracy due to nonlinearities, advanced filtering methodologies, such as particle filters, can provide more accurate estimates of the target state in case of nonlinear problems, thus improving system performance in terms of collision risk estimation. The paper focuses on algorithm development and performance evaluation for an obstacle tracking system based on a particle filter. The particle filter algorithm was tested in off-line simulations based on data gathered during flight tests. In particular, radar-based tracking was considered in order to evaluate the impact of particle filtering in a single sensor framework. The analysis shows some accuracy improvements in the estimation of Distance at Closest Point of Approach, thus reducing the delay in collision detection. PMID:25105154

The new approach for infrared target tracking based on the particle filter algorithm

NASA Astrophysics Data System (ADS)

Sun, Hang; Han, Hong-xia

2011-08-01

Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring, precision, and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection, the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure, but in order to capture the change of the state space, it need a certain amount of particles to ensure samples is enough, and this number will increase in accompany with dimension and increase exponentially, this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining", we expand the classic Mean Shift tracking framework .Based on the previous perspective, we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis, Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism, used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation, and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value .Last because of the gray and fusion target motion information, this approach also inhibit interference from the background, ultimately improve the stability and the real-time of the target track.

High-bandwidth and flexible tracking control for precision motion with application to a piezo nanopositioner.

PubMed

Feng, Zhao; Ling, Jie; Ming, Min; Xiao, Xiao-Hui

2017-08-01

For precision motion, high-bandwidth and flexible tracking are the two important issues for significant performance improvement. Iterative learning control (ILC) is an effective feedforward control method only for systems that operate strictly repetitively. Although projection ILC can track varying references, the performance is still limited by the fixed-bandwidth Q-filter, especially for triangular waves tracking commonly used in a piezo nanopositioner. In this paper, a wavelet transform-based linear time-varying (LTV) Q-filter design for projection ILC is proposed to compensate high-frequency errors and improve the ability to tracking varying references simultaneously. The LVT Q-filter is designed based on the modulus maximum of wavelet detail coefficients calculated by wavelet transform to determine the high-frequency locations of each iteration with the advantages of avoiding cross-terms and segmenting manually. The proposed approach was verified on a piezo nanopositioner. Experimental results indicate that the proposed approach can locate the high-frequency regions accurately and achieve the best performance under varying references compared with traditional frequency-domain and projection ILC with a fixed-bandwidth Q-filter, which validates that through implementing the LTV filter on projection ILC, high-bandwidth and flexible tracking can be achieved simultaneously by the proposed approach.

Towards large scale multi-target tracking

NASA Astrophysics Data System (ADS)

Vo, Ba-Ngu; Vo, Ba-Tuong; Reuter, Stephan; Lam, Quang; Dietmayer, Klaus

2014-06-01

Multi-target tracking is intrinsically an NP-hard problem and the complexity of multi-target tracking solutions usually do not scale gracefully with problem size. Multi-target tracking for on-line applications involving a large number of targets is extremely challenging. This article demonstrates the capability of the random finite set approach to provide large scale multi-target tracking algorithms. In particular it is shown that an approximate filter known as the labeled multi-Bernoulli filter can simultaneously track one thousand five hundred targets in clutter on a standard laptop computer.

Joint Target Detection and Tracking Filter for Chilbolton Advanced Meteorological Radar Data Processing

NASA Astrophysics Data System (ADS)

Pak, A.; Correa, J.; Adams, M.; Clark, D.; Delande, E.; Houssineau, J.; Franco, J.; Frueh, C.

2016-09-01

Recently, the growing number of inactive Resident Space Objects (RSOs), or space debris, has provoked increased interest in the field of Space Situational Awareness (SSA) and various investigations of new methods for orbital object tracking. In comparison with conventional tracking scenarios, state estimation of an orbiting object entails additional challenges, such as orbit determination and orbital state and covariance propagation in the presence of highly nonlinear system dynamics. The sensors which are available for detecting and tracking space debris are prone to multiple clutter measurements. Added to this problem, is the fact that it is unknown whether or not a space debris type target is present within such sensor measurements. Under these circumstances, traditional single-target filtering solutions such as Kalman Filters fail to produce useful trajectory estimates. The recent Random Finite Set (RFS) based Finite Set Statistical (FISST) framework has yielded filters which are more appropriate for such situations. The RFS based Joint Target Detection and Tracking (JoTT) filter, also known as the Bernoulli filter, is a single target, multiple measurements filter capable of dealing with cluttered and time-varying backgrounds as well as modeling target appearance and disappearance in the scene. Therefore, this paper presents the application of the Gaussian mixture-based JoTT filter for processing measurements from Chilbolton Advanced Meteorological Radar (CAMRa) which contain both defunct and operational satellites. The CAMRa is a fully-steerable radar located in southern England, which was recently modified to be used as a tracking asset in the European Space Agency SSA program. The experiments conducted show promising results regarding the capability of such filters in processing cluttered radar data. The work carried out in this paper was funded by the USAF Grant No. FA9550-15-1-0069, Chilean Conicyt - Fondecyt grant number 1150930, EU Erasmus Mundus MSc Scholarship, Defense Science and Technology Laboratory (DSTL), U. K., and the Chilean Conicyt, Fondecyt project grant number 1150930.

Infrared target tracking via weighted correlation filter

NASA Astrophysics Data System (ADS)

He, Yu-Jie; Li, Min; Zhang, JinLi; Yao, Jun-Ping

2015-11-01

Design of an effective target tracker is an important and challenging task for many applications due to multiple factors which can cause disturbance in infrared video sequences. In this paper, an infrared target tracking method under tracking by detection framework based on a weighted correlation filter is presented. This method consists of two parts: detection and filtering. For the detection stage, we propose a sequential detection method for the infrared target based on low-rank representation. For the filtering stage, a new multi-feature weighted function which fuses different target features is proposed, which takes the importance of the different regions into consideration. The weighted function is then incorporated into a correlation filter to compute a confidence map more accurately, in order to indicate the best target location based on the detection results obtained from the first stage. Extensive experimental results on different video sequences demonstrate that the proposed method performs favorably for detection and tracking compared with baseline methods in terms of efficiency and accuracy.

A game theory approach to target tracking in sensor networks.

PubMed

Gu, Dongbing

2011-02-01

In this paper, we investigate a moving-target tracking problem with sensor networks. Each sensor node has a sensor to observe the target and a processor to estimate the target position. It also has wireless communication capability but with limited range and can only communicate with neighbors. The moving target is assumed to be an intelligent agent, which is "smart" enough to escape from the detection by maximizing the estimation error. This adversary behavior makes the target tracking problem more difficult. We formulate this target estimation problem as a zero-sum game in this paper and use a minimax filter to estimate the target position. The minimax filter is a robust filter that minimizes the estimation error by considering the worst case noise. Furthermore, we develop a distributed version of the minimax filter for multiple sensor nodes. The distributed computation is implemented via modeling the information received from neighbors as measurements in the minimax filter. The simulation results show that the target tracking algorithm proposed in this paper provides a satisfactory result.

A mathematical model for computer image tracking.

PubMed

Legters, G R; Young, T Y

1982-06-01

A mathematical model using an operator formulation for a moving object in a sequence of images is presented. Time-varying translation and rotation operators are derived to describe the motion. A variational estimation algorithm is developed to track the dynamic parameters of the operators. The occlusion problem is alleviated by using a predictive Kalman filter to keep the tracking on course during severe occlusion. The tracking algorithm (variational estimation in conjunction with Kalman filter) is implemented to track moving objects with occasional occlusion in computer-simulated binary images.

A hand tracking algorithm with particle filter and improved GVF snake model

NASA Astrophysics Data System (ADS)

Sun, Yi-qi; Wu, Ai-guo; Dong, Na; Shao, Yi-zhe

2017-07-01

To solve the problem that the accurate information of hand cannot be obtained by particle filter, a hand tracking algorithm based on particle filter combined with skin-color adaptive gradient vector flow (GVF) snake model is proposed. Adaptive GVF and skin color adaptive external guidance force are introduced to the traditional GVF snake model, guiding the curve to quickly converge to the deep concave region of hand contour and obtaining the complex hand contour accurately. This algorithm realizes a real-time correction of the particle filter parameters, avoiding the particle drift phenomenon. Experimental results show that the proposed algorithm can reduce the root mean square error of the hand tracking by 53%, and improve the accuracy of hand tracking in the case of complex and moving background, even with a large range of occlusion.

Bearings Only Tracking with Fusion from Heterogenous Passive Sensors: ESM/EO and Acoustic

DTIC Science & Technology

2017-02-01

consists of an unscented Kalman filter (UKF) to handle in-sequence ESM/EO measurements and an OOSM unscented Gauss-Helmert filter (OOSM-UGHF) to handle out...bearings-only tracking, target motion analysis, unscented Gauss-Helmert filter , out-of-sequence measurement. I. INTRODUCTION The commonly used passive...proposed an unscented Gauss-Helmert filter (UGHF) [22] [21] to solve this problem. The existing UGHF works with in-sequence measurements. Further

Utilization of Historic Information in an Optimisation Task

NASA Technical Reports Server (NTRS)

Boesser, T.

1984-01-01

One of the basic components of a discrete model of motor behavior and decision making, which describes tracking and supervisory control in unitary terms, is assumed to be a filtering mechanism which is tied to the representational principles of human memory for time-series information. In a series of experiments subjects used the time-series information with certain significant limitations: there is a range-effect; asymmetric distributions seem to be recognized, but it does not seem to be possible to optimize performance based on skewed distributions. Thus there is a transformation of the displayed data between the perceptual system and representation in memory involving a loss of information. This rules out a number of representational principles for time-series information in memory and fits very well into the framework of a comprehensive discrete model for control of complex systems, modelling continuous control (tracking), discrete responses, supervisory behavior and learning.

Robust lane detection and tracking using multiple visual cues under stochastic lane shape conditions

NASA Astrophysics Data System (ADS)

Huang, Zhi; Fan, Baozheng; Song, Xiaolin

2018-03-01

As one of the essential components of environment perception techniques for an intelligent vehicle, lane detection is confronted with challenges including robustness against the complicated disturbance and illumination, also adaptability to stochastic lane shapes. To overcome these issues, we proposed a robust lane detection method named classification-generation-growth-based (CGG) operator to the detected lines, whereby the linear lane markings are identified by synergizing multiple visual cues with the a priori knowledge and spatial-temporal information. According to the quality of linear lane fitting, the linear and linear-parabolic models are dynamically switched to describe the actual lane. The Kalman filter with adaptive noise covariance and the region of interests (ROI) tracking are applied to improve the robustness and efficiency. Experiments were conducted with images covering various challenging scenarios. The experimental results evaluate the effectiveness of the presented method for complicated disturbances, illumination, and stochastic lane shapes.

Impact of Physician Education and a Dedicated Inferior Vena Cava Filter Tracking System on Inferior Vena Cava Filter Use and Retrieval Rates Across a Large US Health Care Region.

PubMed

Wang, Stephen L; Cha, Hsien-Hwa A; Lin, James R; Francis, Bolanos; Elizabeth, Wakley; Martin, Porras; Rajan, Sudhir

2016-05-01

To evaluate the effects of physician familiarity with current evidence and guidelines on inferior vena cava (IVC) filter use and the availability of IVC filter tracking infrastructure on retrieval rates. Fourteen continuing medical education-approved in-hospital grand rounds covering evidence-based review of the literature on IVC filter efficacy, patient-centered outcomes, guidelines for IVC filter indications, and complications were performed across a large United States (US) health care region serving more than 3.5 million members. A computer-based IVC filter tracking system was deployed simultaneously. IVC filter use, rates of attempted retrieval, and fulfillment of guidelines for IVC filter indications were retrospectively evaluated at each facility for 12 months before intervention (n = 427) and for 12 months after intervention (n = 347). After education, IVC filter use decreased 18.7%, with a member enrollment-adjusted decrease of 22.2%, despite an increasing IVC filter use trend for 4 years. Reduction in IVC filter use at each facility strongly correlated with physician attendance at grand rounds (r = -0.69; P = .007). Rates of attempted retrieval increased from 38.9% to 54.0% (P = .0006), with similar rates of successful retrieval (82.3% before education and 85.8% after education on first attempt). Improvement in IVC filter retrieval attempts correlated with physician attendance at grand rounds (r = 0.51; P = .051). IVC filter dwell times at first retrieval attempt were similar (10.2 wk before and 10.8 wk after). Physician education dramatically reduced IVC filter use across a large US health care region, and represents a learning opportunity for physicians who request and place them. Education and a novel tracking system improved rates of retrieval for IVC filter devices. Copyright © 2016 SIR. Published by Elsevier Inc. All rights reserved.

Event-triggered Kalman-consensus filter for two-target tracking sensor networks.

PubMed

Su, Housheng; Li, Zhenghao; Ye, Yanyan

2017-11-01

This paper is concerned with the problem of event-triggered Kalman-consensus filter for two-target tracking sensor networks. According to the event-triggered protocol and the mean-square analysis, a suboptimal Kalman gain matrix is derived and a suboptimal event-triggered distributed filter is obtained. Based on the Kalman-consensus filter protocol, all sensors which only depend on its neighbors' information can track their corresponding targets. Furthermore, utilizing Lyapunov method and matrix theory, some sufficient conditions are presented for ensuring the stability of the system. Finally, a simulation example is presented to verify the effectiveness of the proposed event-triggered protocol. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Research on the Filtering Algorithm in Speed and Position Detection of Maglev Trains

PubMed Central

Dai, Chunhui; Long, Zhiqiang; Xie, Yunde; Xue, Song

2011-01-01

This paper introduces in brief the traction system of a permanent magnet electrodynamic suspension (EDS) train. The synchronous traction mode based on long stators and track cable is described. A speed and position detection system is recommended. It is installed on board and is used as the feedback end. Restricted by the maglev train’s structure, the permanent magnet electrodynamic suspension (EDS) train uses the non-contact method to detect its position. Because of the shake and the track joints, the position signal sent by the position sensor is always aberrant and noisy. To solve this problem, a linear discrete track-differentiator filtering algorithm is proposed. The filtering characters of the track-differentiator (TD) and track-differentiator group are analyzed. The four series of TD are used in the signal processing unit. The result shows that the track-differentiator could have a good effect and make the traction system run normally. PMID:22164012

Research on the filtering algorithm in speed and position detection of maglev trains.

PubMed

Dai, Chunhui; Long, Zhiqiang; Xie, Yunde; Xue, Song

2011-01-01

This paper introduces in brief the traction system of a permanent magnet electrodynamic suspension (EDS) train. The synchronous traction mode based on long stators and track cable is described. A speed and position detection system is recommended. It is installed on board and is used as the feedback end. Restricted by the maglev train's structure, the permanent magnet electrodynamic suspension (EDS) train uses the non-contact method to detect its position. Because of the shake and the track joints, the position signal sent by the position sensor is always aberrant and noisy. To solve this problem, a linear discrete track-differentiator filtering algorithm is proposed. The filtering characters of the track-differentiator (TD) and track-differentiator group are analyzed. The four series of TD are used in the signal processing unit. The result shows that the track-differentiator could have a good effect and make the traction system run normally.

Online tracking of instantaneous frequency and amplitude of dynamical system response

NASA Astrophysics Data System (ADS)

Frank Pai, P.

2010-05-01

This paper presents a sliding-window tracking (SWT) method for accurate tracking of the instantaneous frequency and amplitude of arbitrary dynamic response by processing only three (or more) most recent data points. Teager-Kaiser algorithm (TKA) is a well-known four-point method for online tracking of frequency and amplitude. Because finite difference is used in TKA, its accuracy is easily destroyed by measurement and/or signal-processing noise. Moreover, because TKA assumes the processed signal to be a pure harmonic, any moving average in the signal can destroy the accuracy of TKA. On the other hand, because SWT uses a constant and a pair of windowed regular harmonics to fit the data and estimate the instantaneous frequency and amplitude, the influence of any moving average is eliminated. Moreover, noise filtering is an implicit capability of SWT when more than three data points are used, and this capability increases with the number of processed data points. To compare the accuracy of SWT and TKA, Hilbert-Huang transform is used to extract accurate time-varying frequencies and amplitudes by processing the whole data set without assuming the signal to be harmonic. Frequency and amplitude trackings of different amplitude- and frequency-modulated signals, vibrato in music, and nonlinear stationary and non-stationary dynamic signals are studied. Results show that SWT is more accurate, robust, and versatile than TKA for online tracking of frequency and amplitude.

Novel branching particle method for tracking

NASA Astrophysics Data System (ADS)

Ballantyne, David J.; Chan, Hubert Y.; Kouritzin, Michael A.

2000-07-01

Particle approximations are used to track a maneuvering signal given only a noisy, corrupted sequence of observations, as are encountered in target tracking and surveillance. The signal exhibits nonlinearities that preclude the optimal use of a Kalman filter. It obeys a stochastic differential equation (SDE) in a seven-dimensional state space, one dimension of which is a discrete maneuver type. The maneuver type switches as a Markov chain and each maneuver identifies a unique SDE for the propagation of the remaining six state parameters. Observations are constructed at discrete time intervals by projecting a polygon corresponding to the target state onto two dimensions and incorporating the noise. A new branching particle filter is introduced and compared with two existing particle filters. The filters simulate a large number of independent particles, each of which moves with the stochastic law of the target. Particles are weighted, redistributed, or branched, depending on the method of filtering, based on their accordance with the current observation from the sequence. Each filter provides an approximated probability distribution of the target state given all back observations. All three particle filters converge to the exact conditional distribution as the number of particles goes to infinity, but differ in how well they perform with a finite number of particles. Using the exactly known ground truth, the root-mean-squared (RMS) errors in target position of the estimated distributions from the three filters are compared. The relative tracking power of the filters is quantified for this target at varying sizes, particle counts, and levels of observation noise.

SHI induced nano track polymer filters and characterization

NASA Astrophysics Data System (ADS)

Vijay, Y. K.

2009-07-01

Swift heavy ion irradiation produces damage in polymers in the form of latent tracks. Latent tracks can be enlarged by etching it in a suitable etchant and thus nuclear track etch membrane can be formed for gas permeation / purification in particular for hydrogen where the molecular size is very small. By applying suitable and controlled etching conditions well defined tracks can be formed for specific applications of the membranes. After etching gas permeation method is used for characterizing the tracks. In the present work polycarbonate (PC) of various thickness were irradiated with energetic ion beam at Inter University Accelerator Centre (IUAC), New Delhi. Nuclear tracks were modified by etching the PC in 6N NaOH at 60 (±1) °C from both sides for different times to produce track etch membranes. At critical etch time the etched pits from both the sides meet a rapid increase in gas permeation was observed. Permeability of hydrogen and carbon dioxide has been measured in samples etched for different times. The latent tracks produced by SHI irradiation in the track etch membranes show enhancement of free volume of the polymer. Nano filters are separation devices for the mixture of gases, different ions in the solution and isotopes and isobars separations. The polymer thin films with controlled porosity finding it self as best choice. However, the permeability and selectivity of these polymer based membrane filters are very important at the nano scale separation. The Swift Heavy Ion (SHI) induced nuclear track etched polymeric films with controlled etching have been attempted and characterized as nano scale filters.

State estimation with incomplete nonlinear constraint

NASA Astrophysics Data System (ADS)

Huang, Yuan; Wang, Xueying; An, Wei

2017-10-01

A problem of state estimation with a new constraints named incomplete nonlinear constraint is considered. The targets are often move in the curve road, if the width of road is neglected, the road can be considered as the constraint, and the position of sensors, e.g., radar, is known in advance, this info can be used to enhance the performance of the tracking filter. The problem of how to incorporate the priori knowledge is considered. In this paper, a second-order sate constraint is considered. A fitting algorithm of ellipse is adopted to incorporate the priori knowledge by estimating the radius of the trajectory. The fitting problem is transformed to the nonlinear estimation problem. The estimated ellipse function is used to approximate the nonlinear constraint. Then, the typical nonlinear constraint methods proposed in recent works can be used to constrain the target state. Monte-Carlo simulation results are presented to illustrate the effectiveness proposed method in state estimation with incomplete constraint.

Iterative Track Fitting Using Cluster Classification in Multi Wire Proportional Chamber

NASA Astrophysics Data System (ADS)

Primor, David; Mikenberg, Giora; Etzion, Erez; Messer, Hagit

2007-10-01

This paper addresses the problem of track fitting of a charged particle in a multi wire proportional chamber (MWPC) using cathode readout strips. When a charged particle crosses a MWPC, a positive charge is induced on a cluster of adjacent strips. In the presence of high radiation background, the cluster charge measurements may be contaminated due to background particles, leading to less accurate hit position estimation. The least squares method for track fitting assumes the same position error distribution for all hits and thus loses its optimal properties on contaminated data. For this reason, a new robust algorithm is proposed. The algorithm first uses the known spatial charge distribution caused by a single charged particle over the strips, and classifies the clusters into ldquocleanrdquo and ldquodirtyrdquo clusters. Then, using the classification results, it performs an iterative weighted least squares fitting procedure, updating its optimal weights each iteration. The performance of the suggested algorithm is compared to other track fitting techniques using a simulation of tracks with radiation background. It is shown that the algorithm improves the track fitting performance significantly. A practical implementation of the algorithm is presented for muon track fitting in the cathode strip chamber (CSC) of the ATLAS experiment.

Curve fitting air sample filter decay curves to estimate transuranic content.

PubMed

Hayes, Robert B; Chiou, Hung Cheng

2004-01-01

By testing industry standard techniques for radon progeny evaluation on air sample filters, a new technique is developed to evaluate transuranic activity on air filters by curve fitting the decay curves. The industry method modified here is simply the use of filter activity measurements at different times to estimate the air concentrations of radon progeny. The primary modification was to not look for specific radon progeny values but rather transuranic activity. By using a method that will provide reasonably conservative estimates of the transuranic activity present on a filter, some credit for the decay curve shape can then be taken. By carrying out rigorous statistical analysis of the curve fits to over 65 samples having no transuranic activity taken over a 10-mo period, an optimization of the fitting function and quality tests for this purpose was attained.

Low Complexity Track Initialization and Fusion for Multi-Modal Sensor Networks

DTIC Science & Technology

2012-11-08

feature was demonstrated via the simulations. Aerospace 2011work further documents our investigation of multiple target tracking filters in...bounds that determine how well a sensor network can resolve and localize multiple targets as a function of the operating parameters such as sensor...probability density (PHD) filter for binary measurements using proximity sensors. 15. SUBJECT TERMS proximity sensors, PHD filter, multiple

A Student’s t Mixture Probability Hypothesis Density Filter for Multi-Target Tracking with Outliers

PubMed Central

Liu, Zhuowei; Chen, Shuxin; Wu, Hao; He, Renke; Hao, Lin

2018-01-01

In multi-target tracking, the outliers-corrupted process and measurement noises can reduce the performance of the probability hypothesis density (PHD) filter severely. To solve the problem, this paper proposed a novel PHD filter, called Student’s t mixture PHD (STM-PHD) filter. The proposed filter models the heavy-tailed process noise and measurement noise as a Student’s t distribution as well as approximates the multi-target intensity as a mixture of Student’s t components to be propagated in time. Then, a closed PHD recursion is obtained based on Student’s t approximation. Our approach can make full use of the heavy-tailed characteristic of a Student’s t distribution to handle the situations with heavy-tailed process and the measurement noises. The simulation results verify that the proposed filter can overcome the negative effect generated by outliers and maintain a good tracking accuracy in the simultaneous presence of process and measurement outliers. PMID:29617348

Space Object Maneuver Detection Algorithms Using TLE Data

NASA Astrophysics Data System (ADS)

Pittelkau, M.

2016-09-01

An important aspect of Space Situational Awareness (SSA) is detection of deliberate and accidental orbit changes of space objects. Although space surveillance systems detect orbit maneuvers within their tracking algorithms, maneuver data are not readily disseminated for general use. However, two-line element (TLE) data is available and can be used to detect maneuvers of space objects. This work is an attempt to improve upon existing TLE-based maneuver detection algorithms. Three adaptive maneuver detection algorithms are developed and evaluated: The first is a fading-memory Kalman filter, which is equivalent to the sliding-window least-squares polynomial fit, but computationally more efficient and adaptive to the noise in the TLE data. The second algorithm is based on a sample cumulative distribution function (CDF) computed from a histogram of the magnitude-squared |V|2 of change-in-velocity vectors (V), which is computed from the TLE data. A maneuver detection threshold is computed from the median estimated from the CDF, or from the CDF and a specified probability of false alarm. The third algorithm is a median filter. The median filter is the simplest of a class of nonlinear filters called order statistics filters, which is within the theory of robust statistics. The output of the median filter is practically insensitive to outliers, or large maneuvers. The median of the |V|2 data is proportional to the variance of the V, so the variance is estimated from the output of the median filter. A maneuver is detected when the input data exceeds a constant times the estimated variance.

Application of Ensemble Kalman Filter in Power System State Tracking and Sensitivity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Yulan; Huang, Zhenyu; Zhou, Ning

2012-05-01

Ensemble Kalman Filter (EnKF) is proposed to track dynamic states of generators. The algorithm of EnKF and its application to generator state tracking are presented in detail. The accuracy and sensitivity of the method are analyzed with respect to initial state errors, measurement noise, unknown fault locations, time steps and parameter errors. It is demonstrated through simulation studies that even with some errors in the parameters, the developed EnKF can effectively track generator dynamic states using disturbance data.

A particle filter for multi-target tracking in track before detect context

NASA Astrophysics Data System (ADS)

Amrouche, Naima; Khenchaf, Ali; Berkani, Daoud

2016-10-01

The track-before-detect (TBD) approach can be used to track a single target in a highly noisy radar scene. This is because it makes use of unthresholded observations and incorporates a binary target existence variable into its target state estimation process when implemented as a particle filter (PF). This paper proposes the recursive PF-TBD approach to detect multiple targets in low-signal-to noise ratios (SNR). The algorithm's successful performance is demonstrated using a simulated two target example.

Tracking moving radar targets with parallel, velocity-tuned filters

DOEpatents

Bickel, Douglas L.; Harmony, David W.; Bielek, Timothy P.; Hollowell, Jeff A.; Murray, Margaret S.; Martinez, Ana

2013-04-30

Radar data associated with radar illumination of a movable target is processed to monitor motion of the target. A plurality of filter operations are performed in parallel on the radar data so that each filter operation produces target image information. The filter operations are defined to have respectively corresponding velocity ranges that differ from one another. The target image information produced by one of the filter operations represents the target more accurately than the target image information produced by the remainder of the filter operations when a current velocity of the target is within the velocity range associated with the one filter operation. In response to the current velocity of the target being within the velocity range associated with the one filter operation, motion of the target is tracked based on the target image information produced by the one filter operation.

Multi-Object Tracking with Correlation Filter for Autonomous Vehicle.

PubMed

Zhao, Dawei; Fu, Hao; Xiao, Liang; Wu, Tao; Dai, Bin

2018-06-22

Multi-object tracking is a crucial problem for autonomous vehicle. Most state-of-the-art approaches adopt the tracking-by-detection strategy, which is a two-step procedure consisting of the detection module and the tracking module. In this paper, we improve both steps. We improve the detection module by incorporating the temporal information, which is beneficial for detecting small objects. For the tracking module, we propose a novel compressed deep Convolutional Neural Network (CNN) feature based Correlation Filter tracker. By carefully integrating these two modules, the proposed multi-object tracking approach has the ability of re-identification (ReID) once the tracked object gets lost. Extensive experiments were performed on the KITTI and MOT2015 tracking benchmarks. Results indicate that our approach outperforms most state-of-the-art tracking approaches.

An Integrated Approach to Indoor and Outdoor Localization

DTIC Science & Technology

2017-04-17

localization estimate, followed by particle filter based tracking. Initial localization is performed using WiFi and image observations. For tracking we...source. A two-step process is proposed that performs an initial localization es-timate, followed by particle filter based t racking. Initial...mapped, it is possible to use them for localization [20, 21, 22]. Haverinen et al. show that these fields could be used with a particle filter to

Target Information Processing: A Joint Decision and Estimation Approach

DTIC Science & Technology

2012-03-29

ground targets ( track - before - detect ) using computer cluster and graphics processing unit. Estimation and filtering theory is one of the most important...targets ( track - before - detect ) using computer cluster and graphics processing unit. Estimation and filtering theory is one of the most important

Real-Time Visual Tracking through Fusion Features

PubMed Central

Ruan, Yang; Wei, Zhenzhong

2016-01-01

Due to their high-speed, correlation filters for object tracking have begun to receive increasing attention. Traditional object trackers based on correlation filters typically use a single type of feature. In this paper, we attempt to integrate multiple feature types to improve the performance, and we propose a new DD-HOG fusion feature that consists of discriminative descriptors (DDs) and histograms of oriented gradients (HOG). However, fusion features as multi-vector descriptors cannot be directly used in prior correlation filters. To overcome this difficulty, we propose a multi-vector correlation filter (MVCF) that can directly convolve with a multi-vector descriptor to obtain a single-channel response that indicates the location of an object. Experiments on the CVPR2013 tracking benchmark with the evaluation of state-of-the-art trackers show the effectiveness and speed of the proposed method. Moreover, we show that our MVCF tracker, which uses the DD-HOG descriptor, outperforms the structure-preserving object tracker (SPOT) in multi-object tracking because of its high-speed and ability to address heavy occlusion. PMID:27347951

Actin dynamics affect mitochondrial quality control and aging in budding yeast.

PubMed

Higuchi, Ryo; Vevea, Jason D; Swayne, Theresa C; Chojnowski, Robert; Hill, Vanessa; Boldogh, Istvan R; Pon, Liza A

2013-12-02

Actin cables of budding yeast are bundles of F-actin that extend from the bud tip or neck to the mother cell tip, serve as tracks for bidirectional cargo transport, and undergo continuous movement from buds toward mother cells [1]. This movement, retrograde actin cable flow (RACF), is similar to retrograde actin flow in lamellipodia, growth cones, immunological synapses, dendritic spines, and filopodia [2-5]. In all cases, actin flow is driven by the push of actin polymerization and assembly at the cell cortex, and myosin-driven pulling forces deeper within the cell [6-10]. Therefore, for movement and inheritance from mothers to buds, mitochondria must "swim upstream" against the opposing force of RACF [11]. We find that increasing RACF rates results in increased fitness of mitochondria inherited by buds and that the increase in mitochondrial fitness leads to extended replicative lifespan and increased cellular healthspan. The sirtuin SIR2 is required for normal RACF and mitochondrial fitness, and increasing RACF rates in sir2Δ cells increases mitochondrial fitness and cellular healthspan but does not affect replicative lifespan. These studies support the model that RACF serves as a filter for segregation of fit from less-fit mitochondria during inheritance, which controls cellular lifespan and healthspan. They also support a role for Sir2p in these processes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Adaptive Filter Techniques for Optical Beam Jitter Control and Target Tracking

DTIC Science & Technology

2008-12-01

OPTICAL BEAM JITTER CONTROL AND TARGET TRACKING Michael J. Beerer Civilian, United States Air Force B.S., University of California Irvine, 2006...TECHNIQUES FOR OPTICAL BEAM JITTER CONTROL AND TARGET TRACKING by Michael J. Beerer December 2008 Thesis Advisor: Brij N. Agrawal Co...DATE December 2008 3. REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE Adaptive Filter Techniques for Optical Beam Jitter

The research of radar target tracking observed information linear filter method

NASA Astrophysics Data System (ADS)

Chen, Zheng; Zhao, Xuanzhi; Zhang, Wen

2018-05-01

Aiming at the problems of low precision or even precision divergent is caused by nonlinear observation equation in radar target tracking, a new filtering algorithm is proposed in this paper. In this algorithm, local linearization is carried out on the observed data of the distance and angle respectively. Then the kalman filter is performed on the linearized data. After getting filtered data, a mapping operation will provide the posteriori estimation of target state. A large number of simulation results show that this algorithm can solve above problems effectively, and performance is better than the traditional filtering algorithm for nonlinear dynamic systems.

Automatic detection, tracking and sensor integration

NASA Astrophysics Data System (ADS)

Trunk, G. V.

1988-06-01

This report surveys the state of the art of automatic detection, tracking, and sensor integration. In the area of detection, various noncoherent integrators such as the moving window integrator, feedback integrator, two-pole filter, binary integrator, and batch processor are discussed. Next, the three techniques for controlling false alarms, adapting thresholds, nonparametric detectors, and clutter maps are presented. In the area of tracking, a general outline is given of a track-while-scan system, and then a discussion is presented of the file system, contact-entry logic, coordinate systems, tracking filters, maneuver-following logic, tracking initiating, track-drop logic, and correlation procedures. Finally, in the area of multisensor integration the problems of colocated-radar integration, multisite-radar integration, radar-IFF integration, and radar-DF bearing strobe integration are treated.

Visual tracking using objectness-bounding box regression and correlation filters

NASA Astrophysics Data System (ADS)

Mbelwa, Jimmy T.; Zhao, Qingjie; Lu, Yao; Wang, Fasheng; Mbise, Mercy

2018-03-01

Visual tracking is a fundamental problem in computer vision with extensive application domains in surveillance and intelligent systems. Recently, correlation filter-based tracking methods have shown a great achievement in terms of robustness, accuracy, and speed. However, such methods have a problem of dealing with fast motion (FM), motion blur (MB), illumination variation (IV), and drifting caused by occlusion (OCC). To solve this problem, a tracking method that integrates objectness-bounding box regression (O-BBR) model and a scheme based on kernelized correlation filter (KCF) is proposed. The scheme based on KCF is used to improve the tracking performance of FM and MB. For handling drift problem caused by OCC and IV, we propose objectness proposals trained in bounding box regression as prior knowledge to provide candidates and background suppression. Finally, scheme KCF as a base tracker and O-BBR are fused to obtain a state of a target object. Extensive experimental comparisons of the developed tracking method with other state-of-the-art trackers are performed on some of the challenging video sequences. Experimental comparison results show that our proposed tracking method outperforms other state-of-the-art tracking methods in terms of effectiveness, accuracy, and robustness.

Methods to Improve the Maintenance of the Earth Catalog of Satellites During Severe Solar Storms

NASA Technical Reports Server (NTRS)

Wilkin, Paul G.; Tolson, Robert H.

1998-01-01

The objective of this thesis is to investigate methods to improve the ability to maintain the inventory of orbital elements of Earth satellites during periods of atmospheric disturbance brought on by severe solar activity. Existing techniques do not account for such atmospheric dynamics, resulting in tracking errors of several seconds in predicted crossing time. Two techniques are examined to reduce of these tracking errors. First, density predicted from various atmospheric models is fit to the orbital decay rate for a number of satellites. An orbital decay model is then developed that could be used to reduce tracking errors by accounting for atmospheric changes. The second approach utilizes a Kalman filter to estimate the orbital decay rate of a satellite after every observation. The new information is used to predict the next observation. Results from the first approach demonstrated the feasibility of building an orbital decay model based on predicted atmospheric density. Correlation of atmospheric density to orbital decay was as high as 0.88. However, it is clear that contemporary: atmospheric models need further improvement in modeling density perturbations polar region brought on by solar activity. The second approach resulted in a dramatic reduction in tracking errors for certain satellites during severe solar Storms. For example, in the limited cases studied, the reduction in tracking errors ranged from 79 to 25 percent.

Optical Flow Analysis and Kalman Filter Tracking in Video Surveillance Algorithms

DTIC Science & Technology

2007-06-01

Grover Brown and Patrick Y.C. Hwang , Introduction to Random Signals and Applied Kalman Filtering, Third edition, John Wiley & Sons, New York, 1997...noise. Brown and Hwang [6] achieve this improvement by linearly blending the prior estimate, 1kx ∧ − , with the noisy measurement, kz , in the equation...AND KALMAN FILTER TRACKING IN VIDEO SURVEILLANCE ALGORITHMS by David A. Semko June 2007 Thesis Advisor: Monique P. Fargues Second

Online Multi-Modal Robust Non-Negative Dictionary Learning for Visual Tracking

PubMed Central

Zhang, Xiang; Guan, Naiyang; Tao, Dacheng; Qiu, Xiaogang; Luo, Zhigang

2015-01-01

Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality. PMID:25961715

Online multi-modal robust non-negative dictionary learning for visual tracking.

PubMed

Zhang, Xiang; Guan, Naiyang; Tao, Dacheng; Qiu, Xiaogang; Luo, Zhigang

2015-01-01

Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality.

Track membranes with open pores used as diffractive filters for space-based x-ray and EUV solar observations.

PubMed

Dominique, Marie; Mitrofanov, A V; Hochedez, J-F; Apel, P Yu; Schühle, U; Pudonin, F A; Orelovich, O L; Zuev, S Yu; Bolsée, D; Hermans, C; BenMoussa, A

2009-02-10

We describe the fabrication and performance of diffractive filters designed for space-based x-ray and EUV solar observations. Unlike traditional thin film filters, diffractive filters can be made to have a high resistance against the destructive mechanical and acoustic loads of a satellite launch. The filters studied are made of plastic track-etched membranes that are metal-coated on one side only. They have all-through open cylindrical pores with diameters as small as 500 nm, limiting their transmittance to very short wavelengths. The spectral transmittance of various diffractive filters with different pore parameters was measured from the soft x-ray to the near IR range (namely, from 1-1100 nm).

Frequency tracking and variable bandwidth for line noise filtering without a reference.

PubMed

Kelly, John W; Collinger, Jennifer L; Degenhart, Alan D; Siewiorek, Daniel P; Smailagic, Asim; Wang, Wei

2011-01-01

This paper presents a method for filtering line noise using an adaptive noise canceling (ANC) technique. This method effectively eliminates the sinusoidal contamination while achieving a narrower bandwidth than typical notch filters and without relying on the availability of a noise reference signal as ANC methods normally do. A sinusoidal reference is instead digitally generated and the filter efficiently tracks the power line frequency, which drifts around a known value. The filter's learning rate is also automatically adjusted to achieve faster and more accurate convergence and to control the filter's bandwidth. In this paper the focus of the discussion and the data will be electrocorticographic (ECoG) neural signals, but the presented technique is applicable to other recordings.

77 FR 60887 - Airworthiness Directives; Alpha Aviation Concept Limited Airplanes

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-05

... possible installation of non-conforming air filter elements that are not fitted with metallic mesh and... finding a non conforming air filter fitted to an overseas aircraft during maintenance. Investigation revealed that air filters with P/N 57.34.00.010 supplied by CEAPR between June 2009 and April 2012 may not...

77 FR 44511 - Airworthiness Directives; Alpha Aviation Concept Limited Airplanes

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-30

... possible installation of non-conforming air filter elements that are not fitted with metallic mesh and... conforming air filter fitted to an overseas aircraft during maintenance. Investigation revealed that air filters with P/N 57.34.00.010 supplied by CEAPR between June 2009 and April 2012 may not have the metallic...

Basilar-membrane responses to broadband noise modeled using linear filters with rational transfer functions.

PubMed

Recio-Spinoso, Alberto; Fan, Yun-Hui; Ruggero, Mario A

2011-05-01

Basilar-membrane responses to white Gaussian noise were recorded using laser velocimetry at basal sites of the chinchilla cochlea with characteristic frequencies near 10 kHz and first-order Wiener kernels were computed by cross correlation of the stimuli and the responses. The presence or absence of minimum-phase behavior was explored by fitting the kernels with discrete linear filters with rational transfer functions. Excellent fits to the kernels were obtained with filters with transfer functions including zeroes located outside the unit circle, implying nonminimum-phase behavior. These filters accurately predicted basilar-membrane responses to other noise stimuli presented at the same level as the stimulus for the kernel computation. Fits with all-pole and other minimum-phase discrete filters were inferior to fits with nonminimum-phase filters. Minimum-phase functions predicted from the amplitude functions of the Wiener kernels by Hilbert transforms were different from the measured phase curves. These results, which suggest that basilar-membrane responses do not have the minimum-phase property, challenge the validity of models of cochlear processing, which incorporate minimum-phase behavior. © 2011 IEEE

Generation of Plausible Hurricane Tracks for Preparedness Exercises

DTIC Science & Technology

2017-04-25

wind extents are simulated by Poisson regression and temporal filtering . The un-optimized MATLAB code runs in less than a minute and is integrated into...of real hurricanes. After wind radii have been simulated for the entire track, median filtering , attenuation over land, and smoothing clean up the wind

Fish tracking by combining motion based segmentation and particle filtering

NASA Astrophysics Data System (ADS)

Bichot, E.; Mascarilla, L.; Courtellemont, P.

2006-01-01

In this paper, we suggest a new importance sampling scheme to improve a particle filtering based tracking process. This scheme relies on exploitation of motion segmentation. More precisely, we propagate hypotheses from particle filtering to blobs of similar motion to target. Hence, search is driven toward regions of interest in the state space and prediction is more accurate. We also propose to exploit segmentation to update target model. Once the moving target has been identified, a representative model is learnt from its spatial support. We refer to this model in the correction step of the tracking process. The importance sampling scheme and the strategy to update target model improve the performance of particle filtering in complex situations of occlusions compared to a simple Bootstrap approach as shown by our experiments on real fish tank sequences.

Feedback Robust Cubature Kalman Filter for Target Tracking Using an Angle Sensor.

PubMed

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.

SME filter approach to multiple target tracking with false and missing measurements

NASA Astrophysics Data System (ADS)

Lee, Yong J.; Kamen, Edward W.

1993-10-01

The symmetric measurement equation (SME) filter for track maintenance in multiple target tracking is extended to the general case when there are an arbitrary unknown number of false and missing position measurements in the measurement set at any time point. It is assumed that the number N of targets is known a priori and that the target motions consist of random perturbations of constant-velocity trajectories. The key idea in the paper is to generate a new measurement vector from sums-of-products of the elements of 'feasible' N-element data vectors that pass a thresholding operation in the sums-of-products framework. Via this construction, the data association problem is completely avoided, and in addition, there is no need to identify which target measurements may correspond to false returns or which target measurements may be missing. A computer simulation of SME filter performance is given, including a comparison with the associated filter (a benchmark) and the joint probabilistic data association (JPDA) filter.

Automated tilt series alignment and tomographic reconstruction in IMOD.

PubMed

Mastronarde, David N; Held, Susannah R

2017-02-01

Automated tomographic reconstruction is now possible in the IMOD software package, including the merging of tomograms taken around two orthogonal axes. Several developments enable the production of high-quality tomograms. When using fiducial markers for alignment, the markers to be tracked through the series are chosen automatically; if there is an excess of markers available, a well-distributed subset is selected that is most likely to track well. Marker positions are refined by applying an edge-enhancing Sobel filter, which results in a 20% improvement in alignment error for plastic-embedded samples and 10% for frozen-hydrated samples. Robust fitting, in which outlying points are given less or no weight in computing the fitting error, is used to obtain an alignment solution, so that aberrant points from the automated tracking can have little effect on the alignment. When merging two dual-axis tomograms, the alignment between them is refined from correlations between local patches; a measure of structure was developed so that patches with insufficient structure to give accurate correlations can now be excluded automatically. We have also developed a script for running all steps in the reconstruction process with a flexible mechanism for setting parameters, and we have added a user interface for batch processing of tilt series to the Etomo program in IMOD. Batch processing is fully compatible with interactive processing and can increase efficiency even when the automation is not fully successful, because users can focus their effort on the steps that require manual intervention. Copyright © 2016 Elsevier Inc. All rights reserved.

40 CFR 721.9719 - Tris carbamoyl triazine (generic).

Code of Federal Regulations, 2012 CFR

2012-07-01

... the requirements for § 721.63(a)(4): (A) Air purifying, tight-fitting half-face respirator equipped... filter (N100 if oil aerosols are absent, R100, or P100); (B) Air purifying, tight-fitting full-face...) filter; powered air-purifying respirator equipped with tight-fitting facepiece (either half-face or full...

40 CFR 721.10077 - 3H-1,2,4-Triazol-3-one, 1,2-dihydro-.

Code of Federal Regulations, 2011 CFR

2011-07-01

...-fitting respirator equipped with N100 (if aerosols absent), R100, or P100 filters (either half- or full... Efficiency Particulate Air (HEPA) filters; powered air-purifying respirator equipped with a tight-fitting facepiece (either half- or full-face) and HEPA filters; and supplied-air respirator operated in pressure...

40 CFR 721.10077 - 3H-1,2,4-Triazol-3-one, 1,2-dihydro-.

Code of Federal Regulations, 2013 CFR

2013-07-01

...-fitting respirator equipped with N100 (if aerosols absent), R100, or P100 filters (either half- or full... Efficiency Particulate Air (HEPA) filters; powered air-purifying respirator equipped with a tight-fitting facepiece (either half- or full-face) and HEPA filters; and supplied-air respirator operated in pressure...

40 CFR 721.10077 - 3H-1,2,4-Triazol-3-one, 1,2-dihydro-.

Code of Federal Regulations, 2012 CFR

2012-07-01

...-fitting respirator equipped with N100 (if aerosols absent), R100, or P100 filters (either half- or full... Efficiency Particulate Air (HEPA) filters; powered air-purifying respirator equipped with a tight-fitting facepiece (either half- or full-face) and HEPA filters; and supplied-air respirator operated in pressure...

40 CFR 721.10077 - 3H-1,2,4-Triazol-3-one, 1,2-dihydro-.

Code of Federal Regulations, 2014 CFR

2014-07-01

...-fitting respirator equipped with N100 (if aerosols absent), R100, or P100 filters (either half- or full... Efficiency Particulate Air (HEPA) filters; powered air-purifying respirator equipped with a tight-fitting facepiece (either half- or full-face) and HEPA filters; and supplied-air respirator operated in pressure...

40 CFR 721.10583 - Benzenamine, 4,4′-[1,3-phenylenebis(1-methylethylidene)]bis-.

Code of Federal Regulations, 2013 CFR

2013-07-01

..., tight-fitting half-face respirator equipped with N100 (if oil aerosols absent), R100, or P100 filters... aerosols absent), R100, or P100 filters. (C) NIOSH-certified powered air-purifying respirator equipped with a loose-fitting hood or helmet and high efficiency particulate air (HEPA) filters. (D) NIOSH...

40 CFR 721.10583 - Benzenamine, 4,4′-[1,3-phenylenebis(1-methylethylidene)]bis-.

Code of Federal Regulations, 2014 CFR

2014-07-01

..., tight-fitting half-face respirator equipped with N100 (if oil aerosols absent), R100, or P100 filters... aerosols absent), R100, or P100 filters. (C) NIOSH-certified powered air-purifying respirator equipped with a loose-fitting hood or helmet and high efficiency particulate air (HEPA) filters. (D) NIOSH...

The influence of surface roughness on the contact stiffness and the contact filter effect in nonlinear wheel-track interaction

NASA Astrophysics Data System (ADS)

Lundberg, Oskar E.; Nordborg, Anders; Lopez Arteaga, Ines

2016-03-01

A state-dependent contact model including nonlinear contact stiffness and nonlinear contact filtering is used to calculate contact forces and rail vibrations with a time-domain wheel-track interaction model. In the proposed method, the full three-dimensional contact geometry is reduced to a point contact in order to lower the computational cost and to reduce the amount of required input roughness-data. Green's functions including the linear dynamics of the wheel and the track are coupled with a point contact model, leading to a numerically efficient model for the wheel-track interaction. Nonlinear effects due to the shape and roughness of the wheel and the rail surfaces are included in the point contact model by pre-calculation of functions for the contact stiffness and contact filters. Numerical results are compared to field measurements of rail vibrations for passenger trains running at 200 kph on a ballast track. Moreover, the influence of vehicle pre-load and different degrees of roughness excitation on the resulting wheel-track interaction is studied by means of numerical predictions.

Autonomous space target recognition and tracking approach using star sensors based on a Kalman filter.

PubMed

Ye, Tao; Zhou, Fuqiang

2015-04-10

When imaged by detectors, space targets (including satellites and debris) and background stars have similar point-spread functions, and both objects appear to change as detectors track targets. Therefore, traditional tracking methods cannot separate targets from stars and cannot directly recognize targets in 2D images. Consequently, we propose an autonomous space target recognition and tracking approach using a star sensor technique and a Kalman filter (KF). A two-step method for subpixel-scale detection of star objects (including stars and targets) is developed, and the combination of the star sensor technique and a KF is used to track targets. The experimental results show that the proposed method is adequate for autonomously recognizing and tracking space targets.

Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant system.

PubMed

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.

Resolving occlusion and segmentation errors in multiple video object tracking

NASA Astrophysics Data System (ADS)

Cheng, Hsu-Yung; Hwang, Jenq-Neng

2009-02-01

In this work, we propose a method to integrate the Kalman filter and adaptive particle sampling for multiple video object tracking. The proposed framework is able to detect occlusion and segmentation error cases and perform adaptive particle sampling for accurate measurement selection. Compared with traditional particle filter based tracking methods, the proposed method generates particles only when necessary. With the concept of adaptive particle sampling, we can avoid degeneracy problem because the sampling position and range are dynamically determined by parameters that are updated by Kalman filters. There is no need to spend time on processing particles with very small weights. The adaptive appearance for the occluded object refers to the prediction results of Kalman filters to determine the region that should be updated and avoids the problem of using inadequate information to update the appearance under occlusion cases. The experimental results have shown that a small number of particles are sufficient to achieve high positioning and scaling accuracy. Also, the employment of adaptive appearance substantially improves the positioning and scaling accuracy on the tracking results.

Track Detection in Railway Sidings Based on MEMS Gyroscope Sensors

PubMed Central

Broquetas, Antoni; Comerón, Adolf; Gelonch, Antoni; Fuertes, Josep M.; Castro, J. Antonio; Felip, Damià; López, Miguel A.; Pulido, José A.

2012-01-01

The paper presents a two-step technique for real-time track detection in single-track railway sidings using low-cost MEMS gyroscopes. The objective is to reliably know the path the train has taken in a switch, diverted or main road, immediately after the train head leaves the switch. The signal delivered by the gyroscope is first processed by an adaptive low-pass filter that rejects noise and converts the temporal turn rate data in degree/second units into spatial turn rate data in degree/meter. The conversion is based on the travelled distance taken from odometer data. The filter is implemented to achieve a speed-dependent cut-off frequency to maximize the signal-to-noise ratio. Although direct comparison of the filtered turn rate signal with a predetermined threshold is possible, the paper shows that better detection performance can be achieved by processing the turn rate signal with a filter matched to the rail switch curvature parameters. Implementation aspects of the track detector have been optimized for real-time operation. The detector has been tested with both simulated data and real data acquired in railway campaigns. PMID:23443376

Synchronous response modelling and control of an annular momentum control device

NASA Astrophysics Data System (ADS)

Hockney, Richard; Johnson, Bruce G.; Misovec, Kathleen

1988-08-01

Research on the synchronous response modelling and control of an advanced Annular Momentun Control Device (AMCD) used to control the attitude of a spacecraft is described. For the flexible rotor AMCD, two sources of synchronous vibrations were identified. One source, which corresponds to the mass unbalance problem of rigid rotors suspended in conventional bearings, is caused by measurement errors of the rotor center of mass position. The other sources of synchronous vibrations is misalignment between the hub and flywheel masses of the AMCD. Four different control algorithms were examined. These were lead-lag compensators that mimic conventional bearing dynamics, tracking notch filters used in the feedback loop, tracking differential-notch filters, and model-based compensators. The tracking differential-notch filters were shown to have a number of advantages over more conventional approaches for both rigid-body rotor applications and flexible rotor applications such as the AMCD. Hardware implementation schemes for the tracking differential-notch filter were investigated. A simple design was developed that can be implemented with analog multipliers and low bandwidth, digital hardware.

Synchronous response modelling and control of an annular momentum control device

NASA Technical Reports Server (NTRS)

Hockney, Richard; Johnson, Bruce G.; Misovec, Kathleen

1988-01-01

Research on the synchronous response modelling and control of an advanced Annular Momentun Control Device (AMCD) used to control the attitude of a spacecraft is described. For the flexible rotor AMCD, two sources of synchronous vibrations were identified. One source, which corresponds to the mass unbalance problem of rigid rotors suspended in conventional bearings, is caused by measurement errors of the rotor center of mass position. The other sources of synchronous vibrations is misalignment between the hub and flywheel masses of the AMCD. Four different control algorithms were examined. These were lead-lag compensators that mimic conventional bearing dynamics, tracking notch filters used in the feedback loop, tracking differential-notch filters, and model-based compensators. The tracking differential-notch filters were shown to have a number of advantages over more conventional approaches for both rigid-body rotor applications and flexible rotor applications such as the AMCD. Hardware implementation schemes for the tracking differential-notch filter were investigated. A simple design was developed that can be implemented with analog multipliers and low bandwidth, digital hardware.

Assessment of the pseudo-tracking approach for the calculation of material acceleration and pressure fields from time-resolved PIV: part II. Spatio-temporal filtering

NASA Astrophysics Data System (ADS)

van Gent, P. L.; Schrijer, F. F. J.; van Oudheusden, B. W.

2018-04-01

The present study characterises the spatio-temporal filtering associated with pseudo-tracking. A combined theoretical and numerical assessment is performed that uses the relatively simple flow case of a two-dimensional Taylor vortex as analytical test case. An additional experimental assessment considers the more complex flow of a low-speed axisymmetric base flow, for which time-resolved tomographic PIV measurements and microphone measurements were obtained. The results of these assessments show how filtering along Lagrangian tracks leads to amplitude modulation of flow structures. A cut-off track length and spatial resolution are specified to support future applications of the pseudo-tracking approach. The experimental results show a fair agreement between PIV and microphone pressure data in terms of fluctuation levels and pressure frequency spectra. The coherence and correlation between microphone and PIV pressure measurements were found to be substantial and almost independent of the track length, indicating that the low-frequency behaviour of the flow could be reproduced regardless of the track length. It is suggested that a spectral analysis can be used inform the selection of a suitable track length and to estimate the local error margin of reconstructed pressure values.

Determination of time zero from a charged particle detector

DOEpatents

Green, Jesse Andrew [Los Alamos, NM

2011-03-15

A method, system and computer program is used to determine a linear track having a good fit to a most likely or expected path of charged particle passing through a charged particle detector having a plurality of drift cells. Hit signals from the charged particle detector are associated with a particular charged particle track. An initial estimate of time zero is made from these hit signals and linear tracks are then fit to drift radii for each particular time-zero estimate. The linear track having the best fit is then searched and selected and errors in fit and tracking parameters computed. The use of large and expensive fast detectors needed to time zero in the charged particle detectors can be avoided by adopting this method and system.

Multi-object tracking of human spermatozoa

NASA Astrophysics Data System (ADS)

Sørensen, Lauge; Østergaard, Jakob; Johansen, Peter; de Bruijne, Marleen

2008-03-01

We propose a system for tracking of human spermatozoa in phase-contrast microscopy image sequences. One of the main aims of a computer-aided sperm analysis (CASA) system is to automatically assess sperm quality based on spermatozoa motility variables. In our case, the problem of assessing sperm quality is cast as a multi-object tracking problem, where the objects being tracked are the spermatozoa. The system combines a particle filter and Kalman filters for robust motion estimation of the spermatozoa tracks. Further, the combinatorial aspect of assigning observations to labels in the particle filter is formulated as a linear assignment problem solved using the Hungarian algorithm on a rectangular cost matrix, making the algorithm capable of handling missing or spurious observations. The costs are calculated using hidden Markov models that express the plausibility of an observation being the next position in the track history of the particle labels. Observations are extracted using a scale-space blob detector utilizing the fact that the spermatozoa appear as bright blobs in a phase-contrast microscope. The output of the system is the complete motion track of each of the spermatozoa. Based on these tracks, different CASA motility variables can be computed, for example curvilinear velocity or straight-line velocity. The performance of the system is tested on three different phase-contrast image sequences of varying complexity, both by visual inspection of the estimated spermatozoa tracks and by measuring the mean squared error (MSE) between the estimated spermatozoa tracks and manually annotated tracks, showing good agreement.

Consistently Sampled Correlation Filters with Space Anisotropic Regularization for Visual Tracking

PubMed Central

Shi, Guokai; Xu, Tingfa; Luo, Jiqiang; Li, Yuankun

2017-01-01

Most existing correlation filter-based tracking algorithms, which use fixed patches and cyclic shifts as training and detection measures, assume that the training samples are reliable and ignore the inconsistencies between training samples and detection samples. We propose to construct and study a consistently sampled correlation filter with space anisotropic regularization (CSSAR) to solve these two problems simultaneously. Our approach constructs a spatiotemporally consistent sample strategy to alleviate the redundancies in training samples caused by the cyclical shifts, eliminate the inconsistencies between training samples and detection samples, and introduce space anisotropic regularization to constrain the correlation filter for alleviating drift caused by occlusion. Moreover, an optimization strategy based on the Gauss-Seidel method was developed for obtaining robust and efficient online learning. Both qualitative and quantitative evaluations demonstrate that our tracker outperforms state-of-the-art trackers in object tracking benchmarks (OTBs). PMID:29231876

Precise orbit determination using the batch filter based on particle filtering with genetic resampling approach

NASA Astrophysics Data System (ADS)

Kim, Young-Rok; Park, Eunseo; Choi, Eun-Jung; Park, Sang-Young; Park, Chandeok; Lim, Hyung-Chul

2014-09-01

In this study, genetic resampling (GRS) approach is utilized for precise orbit determination (POD) using the batch filter based on particle filtering (PF). Two genetic operations, which are arithmetic crossover and residual mutation, are used for GRS of the batch filter based on PF (PF batch filter). For POD, Laser-ranging Precise Orbit Determination System (LPODS) and satellite laser ranging (SLR) observations of the CHAMP satellite are used. Monte Carlo trials for POD are performed by one hundred times. The characteristics of the POD results by PF batch filter with GRS are compared with those of a PF batch filter with minimum residual resampling (MRRS). The post-fit residual, 3D error by external orbit comparison, and POD repeatability are analyzed for orbit quality assessments. The POD results are externally checked by NASA JPL’s orbits using totally different software, measurements, and techniques. For post-fit residuals and 3D errors, both MRRS and GRS give accurate estimation results whose mean root mean square (RMS) values are at a level of 5 cm and 10-13 cm, respectively. The mean radial orbit errors of both methods are at a level of 5 cm. For POD repeatability represented as the standard deviations of post-fit residuals and 3D errors by repetitive PODs, however, GRS yields 25% and 13% more robust estimation results than MRRS for post-fit residual and 3D error, respectively. This study shows that PF batch filter with GRS approach using genetic operations is superior to PF batch filter with MRRS in terms of robustness in POD with SLR observations.

40 CFR 721.10575 - 1-Propanone, 1,1'-(oxydi-4,1-phenylene)bis[2-hydroxy-2-methyl-.

Code of Federal Regulations, 2013 CFR

2013-07-01

...), R100, or P100 filters. (B) NIOSH-certified air-purifying, tight-fitting full-face respirator equipped with N100 (if oil aerosols absent), R100, or P100 filters. (C) NIOSH-certified powered air-purifying respirator equipped with a loose-fitting hood or helmet and high efficiency particulate air (HEPA) filters...

40 CFR 721.10575 - 1-Propanone, 1,1'-(oxydi-4,1-phenylene)bis[2-hydroxy-2-methyl-.

Code of Federal Regulations, 2014 CFR

2014-07-01

...), R100, or P100 filters. (B) NIOSH-certified air-purifying, tight-fitting full-face respirator equipped with N100 (if oil aerosols absent), R100, or P100 filters. (C) NIOSH-certified powered air-purifying respirator equipped with a loose-fitting hood or helmet and high efficiency particulate air (HEPA) filters...

78 FR 12684 - Proposed Significant New Use Rules on Certain Chemical Substances

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-25

...-purifying, tight-fitting full-face respirator equipped with N100 filters with an APF of at least 50 (when... respirator equipped with N100 filters with an APF of at least 50 (when there is potential inhalation exposure...-fitting full-face respirator equipped with N100 filters with an APF of at least 50 (when there is...

Scene-Aware Adaptive Updating for Visual Tracking via Correlation Filters

PubMed Central

Zhang, Sirou; Qiao, Xiaoya

2017-01-01

In recent years, visual object tracking has been widely used in military guidance, human-computer interaction, road traffic, scene monitoring and many other fields. The tracking algorithms based on correlation filters have shown good performance in terms of accuracy and tracking speed. However, their performance is not satisfactory in scenes with scale variation, deformation, and occlusion. In this paper, we propose a scene-aware adaptive updating mechanism for visual tracking via a kernel correlation filter (KCF). First, a low complexity scale estimation method is presented, in which the corresponding weight in five scales is employed to determine the final target scale. Then, the adaptive updating mechanism is presented based on the scene-classification. We classify the video scenes as four categories by video content analysis. According to the target scene, we exploit the adaptive updating mechanism to update the kernel correlation filter to improve the robustness of the tracker, especially in scenes with scale variation, deformation, and occlusion. We evaluate our tracker on the CVPR2013 benchmark. The experimental results obtained with the proposed algorithm are improved by 33.3%, 15%, 6%, 21.9% and 19.8% compared to those of the KCF tracker on the scene with scale variation, partial or long-time large-area occlusion, deformation, fast motion and out-of-view. PMID:29140311

PSO Algorithm Particle Filters for Improving the Performance of Lane Detection and Tracking Systems in Difficult Roads

PubMed Central

Cheng, Wen-Chang

2012-01-01

In this paper we propose a robust lane detection and tracking method by combining particle filters with the particle swarm optimization method. This method mainly uses the particle filters to detect and track the local optimum of the lane model in the input image and then seeks the global optimal solution of the lane model by a particle swarm optimization method. The particle filter can effectively complete lane detection and tracking in complicated or variable lane environments. However, the result obtained is usually a local optimal system status rather than the global optimal system status. Thus, the particle swarm optimization method is used to further refine the global optimal system status in all system statuses. Since the particle swarm optimization method is a global optimization algorithm based on iterative computing, it can find the global optimal lane model by simulating the food finding way of fish school or insects under the mutual cooperation of all particles. In verification testing, the test environments included highways and ordinary roads as well as straight and curved lanes, uphill and downhill lanes, lane changes, etc. Our proposed method can complete the lane detection and tracking more accurately and effectively then existing options. PMID:23235453

Modified superposition: A simple time series approach to closed-loop manual controller identification

NASA Technical Reports Server (NTRS)

Biezad, D. J.; Schmidt, D. K.; Leban, F.; Mashiko, S.

1986-01-01

Single-channel pilot manual control output in closed-tracking tasks is modeled in terms of linear discrete transfer functions which are parsimonious and guaranteed stable. The transfer functions are found by applying a modified super-position time series generation technique. A Levinson-Durbin algorithm is used to determine the filter which prewhitens the input and a projective (least squares) fit of pulse response estimates is used to guarantee identified model stability. Results from two case studies are compared to previous findings, where the source of data are relatively short data records, approximately 25 seconds long. Time delay effects and pilot seasonalities are discussed and analyzed. It is concluded that single-channel time series controller modeling is feasible on short records, and that it is important for the analyst to determine a criterion for best time domain fit which allows association of model parameter values, such as pure time delay, with actual physical and physiological constraints. The purpose of the modeling is thus paramount.

Tracking of BMI, fatness and cardiorespiratory fitness from adolescence to middle adulthood: the Zagreb Growth and Development Longitudinal Study.

PubMed

Sorić, Maroje; Jembrek Gostović, Mirjana; Gostović, Mladen; Hočevar, Marija; Mišigoj-Duraković, Marjeta

2014-01-01

Effective intervention strategies aiming to improve cardiorespiratory fitness and to decrease body fatness are needed. However, long-term stability of these traits is not well understood. To assess long-term tracking of cardiorespiratory fitness and body fatness from late adolescence to middle adulthood. The sample consisted of 50 participants (31 boys) from the Zagreb Growth and Development Longitudinal Study who were followed up in adulthood (median age = 43). Fatness was evaluated through BMI and skin-folds, while cardiorespiratory fitness was assessed using a cardiopulmonary exercise test. Inter-age partial correlation coefficients were calculated to evaluate tracking. Body mass index and skin-folds showed moderate tracking from age 15 years to middle adulthood (partial r = 0.55, p < 0.001 and partial r = 0.52, p < 0.001, respectively), while tracking of subcutaneous fat distribution was somewhat lower (partial r = 0.38, p < 0.01). At the same time, the observed tracking of peak oxygen uptake was low-to-moderate (partial r = 0.30, p = 0.03), while ventilatory aerobic and anaerobic thresholds did not show significant tracking. The results of this study indicate that preventive efforts aiming to increase cardiorespiratory fitness should include all adolescents, irrespective of their cardiorespiratory fitness status. Conversely, strategies aiming at obesity prevention should focus on high-risk groups of adolescents.

An Adaptive Filter for the Removal of Drifting Sinusoidal Noise Without a Reference.

PubMed

Kelly, John W; Siewiorek, Daniel P; Smailagic, Asim; Wang, Wei

2016-01-01

This paper presents a method for filtering sinusoidal noise with a variable bandwidth filter that is capable of tracking a sinusoid's drifting frequency. The method, which is based on the adaptive noise canceling (ANC) technique, will be referred to here as the adaptive sinusoid canceler (ASC). The ASC eliminates sinusoidal contamination by tracking its frequency and achieving a narrower bandwidth than typical notch filters. The detected frequency is used to digitally generate an internal reference instead of relying on an external one as ANC filters typically do. The filter's bandwidth adjusts to achieve faster and more accurate convergence. In this paper, the focus of the discussion and the data is physiological signals, specifically electrocorticographic (ECoG) neural data contaminated with power line noise, but the presented technique could be applicable to other recordings as well. On simulated data, the ASC was able to reliably track the noise's frequency, properly adjust its bandwidth, and outperform comparative methods including standard notch filters and an adaptive line enhancer. These results were reinforced by visual results obtained from real ECoG data. The ASC showed that it could be an effective method for increasing signal to noise ratio in the presence of drifting sinusoidal noise, which is of significant interest for biomedical applications.

Improved Spatial Registration and Target Tracking Method for Sensors on Multiple Missiles.

PubMed

Lu, Xiaodong; Xie, Yuting; Zhou, Jun

2018-05-27

Inspired by the problem that the current spatial registration methods are unsuitable for three-dimensional (3-D) sensor on high-dynamic platform, this paper focuses on the estimation for the registration errors of cooperative missiles and motion states of maneuvering target. There are two types of errors being discussed: sensor measurement biases and attitude biases. Firstly, an improved Kalman Filter on Earth-Centered Earth-Fixed (ECEF-KF) coordinate algorithm is proposed to estimate the deviations mentioned above, from which the outcomes are furtherly compensated to the error terms. Secondly, the Pseudo Linear Kalman Filter (PLKF) and the nonlinear scheme the Unscented Kalman Filter (UKF) with modified inputs are employed for target tracking. The convergence of filtering results are monitored by a position-judgement logic, and a low-pass first order filter is selectively introduced before compensation to inhibit the jitter of estimations. In the simulation, the ECEF-KF enhancement is proven to improve the accuracy and robustness of the space alignment, while the conditional-compensation-based PLKF method is demonstrated to be the optimal performance in target tracking.

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.

A Pole-Zero Filter Cascade Provides Good Fits to Human Masking Data and to Basilar Membrane and Neural Data

NASA Astrophysics Data System (ADS)

Lyon, Richard F.

2011-11-01

A cascade of two-pole-two-zero filters with level-dependent pole and zero dampings, with few parameters, can provide a good match to human psychophysical and physiological data. The model has been fitted to data on detection threshold for tones in notched-noise masking, including bandwidth and filter shape changes over a wide range of levels, and has been shown to provide better fits with fewer parameters compared to other auditory filter models such as gammachirps. Originally motivated as an efficient machine implementation of auditory filtering related to the WKB analysis method of cochlear wave propagation, such filter cascades also provide good fits to mechanical basilar membrane data, and to auditory nerve data, including linear low-frequency tail response, level-dependent peak gain, sharp tuning curves, nonlinear compression curves, level-independent zero-crossing times in the impulse response, realistic instantaneous frequency glides, and appropriate level-dependent group delay even with minimum-phase response. As part of exploring different level-dependent parameterizations of such filter cascades, we have identified a simple sufficient condition for stable zero-crossing times, based on the shifting property of the Laplace transform: simply move all the s-domain poles and zeros by equal amounts in the real-s direction. Such pole-zero filter cascades are efficient front ends for machine hearing applications, such as music information retrieval, content identification, speech recognition, and sound indexing.

Probabilistic multi-person localisation and tracking in image sequences

NASA Astrophysics Data System (ADS)

Klinger, T.; Rottensteiner, F.; Heipke, C.

2017-05-01

The localisation and tracking of persons in image sequences in commonly guided by recursive filters. Especially in a multi-object tracking environment, where mutual occlusions are inherent, the predictive model is prone to drift away from the actual target position when not taking context into account. Further, if the image-based observations are imprecise, the trajectory is prone to be updated towards a wrong position. In this work we address both these problems by using a new predictive model on the basis of Gaussian Process Regression, and by using generic object detection, as well as instance-specific classification, for refined localisation. The predictive model takes into account the motion of every tracked pedestrian in the scene and the prediction is executed with respect to the velocities of neighbouring persons. In contrast to existing methods our approach uses a Dynamic Bayesian Network in which the state vector of a recursive Bayes filter, as well as the location of the tracked object in the image, are modelled as unknowns. This allows the detection to be corrected before it is incorporated into the recursive filter. Our method is evaluated on a publicly available benchmark dataset and outperforms related methods in terms of geometric precision and tracking accuracy.

Kalman filter-based tracking of moving objects using linear ultrasonic sensor array for road vehicles

NASA Astrophysics Data System (ADS)

Li, Shengbo Eben; Li, Guofa; Yu, Jiaying; Liu, Chang; Cheng, Bo; Wang, Jianqiang; Li, Keqiang

2018-01-01

Detection and tracking of objects in the side-near-field has attracted much attention for the development of advanced driver assistance systems. This paper presents a cost-effective approach to track moving objects around vehicles using linearly arrayed ultrasonic sensors. To understand the detection characteristics of a single sensor, an empirical detection model was developed considering the shapes and surface materials of various detected objects. Eight sensors were arrayed linearly to expand the detection range for further application in traffic environment recognition. Two types of tracking algorithms, including an Extended Kalman filter (EKF) and an Unscented Kalman filter (UKF), for the sensor array were designed for dynamic object tracking. The ultrasonic sensor array was designed to have two types of fire sequences: mutual firing or serial firing. The effectiveness of the designed algorithms were verified in two typical driving scenarios: passing intersections with traffic sign poles or street lights, and overtaking another vehicle. Experimental results showed that both EKF and UKF had more precise tracking position and smaller RMSE (root mean square error) than a traditional triangular positioning method. The effectiveness also encourages the application of cost-effective ultrasonic sensors in the near-field environment perception in autonomous driving systems.

A New Filtering and Smoothing Algorithm for Railway Track Surveying Based on Landmark and IMU/Odometer

PubMed Central

Jiang, Qingan; Wu, Wenqi; Jiang, Mingming; Li, Yun

2017-01-01

High-accuracy railway track surveying is essential for railway construction and maintenance. The traditional approaches based on total station equipment are not efficient enough since high precision surveying frequently needs static measurements. This paper proposes a new filtering and smoothing algorithm based on the IMU/odometer and landmarks integration for the railway track surveying. In order to overcome the difficulty of estimating too many error parameters with too few landmark observations, a new model with completely observable error states is established by combining error terms of the system. Based on covariance analysis, the analytical relationship between the railway track surveying accuracy requirements and equivalent gyro drifts including bias instability and random walk noise are established. Experiment results show that the accuracy of the new filtering and smoothing algorithm for railway track surveying can reach 1 mm (1σ) when using a Ring Laser Gyroscope (RLG)-based Inertial Measurement Unit (IMU) with gyro bias instability of 0.03°/h and random walk noise of 0.005°/h while control points of the track control network (CPIII) position observations are provided by the optical total station in about every 60 m interval. The proposed approach can satisfy at the same time the demands of high accuracy and work efficiency for railway track surveying. PMID:28629191

Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant system

PubMed Central

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

Charged particle tracking without magnetic field: Optimal measurement of track momentum by a Bayesian analysis of the multiple measurements of deflections due to multiple scattering

NASA Astrophysics Data System (ADS)

Frosini, Mikael; Bernard, Denis

2017-09-01

We revisit the precision of the measurement of track parameters (position, angle) with optimal methods in the presence of detector resolution, multiple scattering and zero magnetic field. We then obtain an optimal estimator of the track momentum by a Bayesian analysis of the filtering innovations of a series of Kalman filters applied to the track. This work could pave the way to the development of autonomous high-performance gas time-projection chambers (TPC) or silicon wafer γ-ray space telescopes and be a powerful guide in the optimization of the design of the multi-kilo-ton liquid argon TPCs that are under development for neutrino studies.

Real time eye tracking using Kalman extended spatio-temporal context learning

NASA Astrophysics Data System (ADS)

Munir, Farzeen; Minhas, Fayyaz ul Amir Asfar; Jalil, Abdul; Jeon, Moongu

2017-06-01

Real time eye tracking has numerous applications in human computer interaction such as a mouse cursor control in a computer system. It is useful for persons with muscular or motion impairments. However, tracking the movement of the eye is complicated by occlusion due to blinking, head movement, screen glare, rapid eye movements, etc. In this work, we present the algorithmic and construction details of a real time eye tracking system. Our proposed system is an extension of Spatio-Temporal context learning through Kalman Filtering. Spatio-Temporal Context Learning offers state of the art accuracy in general object tracking but its performance suffers due to object occlusion. Addition of the Kalman filter allows the proposed method to model the dynamics of the motion of the eye and provide robust eye tracking in cases of occlusion. We demonstrate the effectiveness of this tracking technique by controlling the computer cursor in real time by eye movements.

Modular Filter and Source-Management Upgrade of RADAC

NASA Technical Reports Server (NTRS)

Lanzi, R. James; Smith, Donna C.

2007-01-01

In an upgrade of the Range Data Acquisition Computer (RADAC) software, a modular software object library was developed to implement required functionality for filtering of flight-vehicle-tracking data and management of tracking-data sources. (The RADAC software is used to process flight-vehicle metric data for realtime display in the Wallops Flight Facility Range Control Center and Mobile Control Center.)

An extended Kalman filter for mouse tracking.

PubMed

Choi, Hongjun; Kim, Mingi; Lee, Onseok

2018-05-19

Animal tracking is an important tool for observing behavior, which is useful in various research areas. Animal specimens can be tracked using dynamic models and observation models that require several types of data. Tracking mouse has several barriers due to the physical characteristics of the mouse, their unpredictable movement, and cluttered environments. Therefore, we propose a reliable method that uses a detection stage and a tracking stage to successfully track mouse. The detection stage detects the surface area of the mouse skin, and the tracking stage implements an extended Kalman filter to estimate the state variables of a nonlinear model. The changes in the overall shape of the mouse are tracked using an oval-shaped tracking model to estimate the parameters for the ellipse. An experiment is conducted to demonstrate the performance of the proposed tracking algorithm using six video images showing various types of movement, and the ground truth values for synthetic images are compared to the values generated by the tracking algorithm. A conventional manual tracking method is also applied to compare across eight experimenters. Furthermore, the effectiveness of the proposed tracking method is also demonstrated by applying the tracking algorithm with actual images of mouse. Graphical abstract.

Development of an Advanced Respirator Fit Test Headform (Postprint)

DTIC Science & Technology

2012-11-01

needed to measure the fit of N95 filtering facepiece respirators (FFRs) for protection studies against viable airborne particles. The objective of...N95 filtering facepiece respirators (FFRs) for pro- tection studies against viable airborne particles. A Static (i.e., non-moving, non-speaking...The N95 class of filtering facepiece respirators (FFRs) is commonly used to re- duce exposure to airborne particles, including oil-free aerosols (dusts

FORTRAN programs to process Magsat data for lithospheric, external field, and residual core components

NASA Technical Reports Server (NTRS)

Alsdorf, Douglas E.; Vonfrese, Ralph R. B.

1994-01-01

The FORTRAN programs supplied in this document provide a complete processing package for statistically extracting residual core, external field and lithospheric components in Magsat observations. To process the individual passes: (1) orbits are separated into dawn and dusk local times and by altitude, (2) passes are selected based on the variance of the magnetic field observations after a least-squares fit of the core field is removed from each pass over the study area, and (3) spatially adjacent passes are processed with a Fourier correlation coefficient filter to separate coherent and non-coherent features between neighboring tracks. In the second state of map processing: (1) data from the passes are normalized to a common altitude and gridded into dawn and dusk maps with least squares collocation, (2) dawn and dusk maps are correlated with a Fourier correlation efficient filter to separate coherent and non-coherent features; the coherent features are averaged to produce a total field grid, (3) total field grids from all altitudes are continued to a common altitude, correlation filtered for coherent anomaly features, and subsequently averaged to produce the final total field grid for the study region, and (4) the total field map is differentially reduced to the pole.

Discriminative correlation filter tracking with occlusion detection

NASA Astrophysics Data System (ADS)

Zhang, Shuo; Chen, Zhong; Yu, XiPeng; Zhang, Ting; He, Jing

2018-03-01

Aiming at the problem that the correlation filter-based tracking algorithm can not track the target of severe occlusion, a target re-detection mechanism is proposed. First of all, based on the ECO, we propose the multi-peak detection model and the response value to distinguish the occlusion and deformation in the target tracking, which improve the success rate of tracking. And then we add the confidence model to update the mechanism to effectively prevent the model offset problem which due to similar targets or background during the tracking process. Finally, the redetection mechanism of the target is added, and the relocation is performed after the target is lost, which increases the accuracy of the target positioning. The experimental results demonstrate that the proposed tracker performs favorably against state-of-the-art methods in terms of robustness and accuracy.

A methodological approach to short-term tracking of youth physical fitness: the Oporto Growth, Health and Performance Study.

PubMed

Souza, Michele; Eisenmann, Joey; Chaves, Raquel; Santos, Daniel; Pereira, Sara; Forjaz, Cláudia; Maia, José

2016-10-01

In this paper, three different statistical approaches were used to investigate short-term tracking of cardiorespiratory and performance-related physical fitness among adolescents. Data were obtained from the Oporto Growth, Health and Performance Study and comprised 1203 adolescents (549 girls) divided into two age cohorts (10-12 and 12-14 years) followed for three consecutive years, with annual assessment. Cardiorespiratory fitness was assessed with 1-mile run/walk test; 50-yard dash, standing long jump, handgrip, and shuttle run test were used to rate performance-related physical fitness. Tracking was expressed in three different ways: auto-correlations, multilevel modelling with crude and adjusted model (for biological maturation, body mass index, and physical activity), and Cohen's Kappa (κ) computed in IBM SPSS 20.0, HLM 7.01 and Longitudinal Data Analysis software, respectively. Tracking of physical fitness components was (1) moderate-to-high when described by auto-correlations; (2) low-to-moderate when crude and adjusted models were used; and (3) low according to Cohen's Kappa (κ). These results demonstrate that when describing tracking, different methods should be considered since they provide distinct and more comprehensive views about physical fitness stability patterns.

A motion-compensated image filter for low-dose fluoroscopy in a real-time tumor-tracking radiotherapy system

PubMed Central

Miyamoto, Naoki; Ishikawa, Masayori; Sutherland, Kenneth; Suzuki, Ryusuke; Matsuura, Taeko; Toramatsu, Chie; Takao, Seishin; Nihongi, Hideaki; Shimizu, Shinichi; Umegaki, Kikuo; Shirato, Hiroki

2015-01-01

In the real-time tumor-tracking radiotherapy system, a surrogate fiducial marker inserted in or near the tumor is detected by fluoroscopy to realize respiratory-gated radiotherapy. The imaging dose caused by fluoroscopy should be minimized. In this work, an image processing technique is proposed for tracing a moving marker in low-dose imaging. The proposed tracking technique is a combination of a motion-compensated recursive filter and template pattern matching. The proposed image filter can reduce motion artifacts resulting from the recursive process based on the determination of the region of interest for the next frame according to the current marker position in the fluoroscopic images. The effectiveness of the proposed technique and the expected clinical benefit were examined by phantom experimental studies with actual tumor trajectories generated from clinical patient data. It was demonstrated that the marker motion could be traced in low-dose imaging by applying the proposed algorithm with acceptable registration error and high pattern recognition score in all trajectories, although some trajectories were not able to be tracked with the conventional spatial filters or without image filters. The positional accuracy is expected to be kept within ±2 mm. The total computation time required to determine the marker position is a few milliseconds. The proposed image processing technique is applicable for imaging dose reduction. PMID:25129556

Mercury's Crustal Magnetic Field from MESSENGER Data

NASA Astrophysics Data System (ADS)

Plattner, A.; Johnson, C.

2017-12-01

We present a regional spherical-harmonic based crustal magnetic field model for Mercury between latitudes 45° and 70° N, derived from MESSENGER magnetic field data. In addition to contributions from the core dynamo, the bow shock, and the magnetotail, Mercury's magnetic field is also influenced by interactions with the solar wind. The resulting field-aligned currents generate magnetic fields that are typically an order of magnitude stronger at spacecraft altitude than the field from sources within Mercury's crust. These current sources lie within the satellite path and so the resulting magnetic field can not be modeled using potential-field approaches. However, these fields are organized in the local-time frame and their spatial structure differs from that of the smaller-scale crustal field. We account for large-scale magnetic fields in the local-time reference frame by subtracting from the data a low-degree localized vector spherical-harmonic model including curl components fitted at satellite altitude. The residual data exhibit consistent signals across individual satellite tracks in the body fixed reference frame, similar to those obtained via more rudimentary along-track filtering approaches. We fit a regional internal-source spherical-harmonic model to the night-time radial component of the residual data, allowing a maximum spherical-harmonic degree of L = 150. Due to the cross-track spacing of the satellite tracks, spherical-harmonic degrees beyond L = 90 are damped. The strongest signals in the resulting model are in the region around the Caloris Basin and over Suisei Planitia, as observed previously. Regularization imposed in the modeling allows the field to be downward continued to the surface. The strongest surface fields are 30 nT. Furthermore, the regional power spectrum of the model shows a downward dipping slope between spherical-harmonic degrees 40 and 80, hinting that the main component of the crustal field lies deep within the crust.

Global navigation satellite system receiver for weak signals under all dynamic conditions

NASA Astrophysics Data System (ADS)

Ziedan, Nesreen Ibrahim

The ability of the Global Navigation Satellite System (GNSS) receiver to work under weak signal and various dynamic conditions is required in some applications. For example, to provide a positioning capability in wireless devices, or orbit determination of Geostationary and high Earth orbit satellites. This dissertation develops Global Positioning System (GPS) receiver algorithms for such applications. Fifteen algorithms are developed for the GPS C/A signal. They cover all the receiver main functions, which include acquisition, fine acquisition, bit synchronization, code and carrier tracking, and navigation message decoding. They are integrated together, and they can be used in any software GPS receiver. They also can be modified to fit any other GPS or GNSS signals. The algorithms have new capabilities. The processing and memory requirements are considered in the design to allow the algorithms to fit the limited resources of some applications; they do not require any assisting information. Weak signals can be acquired in the presence of strong interfering signals and under high dynamic conditions. The fine acquisition, bit synchronization, and tracking algorithms are based on the Viterbi algorithm and Extended Kalman filter approaches. The tracking algorithms capabilities increase the time to lose lock. They have the ability to adaptively change the integration length and the code delay separation. More than one code delay separation can be used in the same time. Large tracking errors can be detected and then corrected by a re-initialization and an acquisition-like algorithms. Detecting the navigation message is needed to increase the coherent integration; decoding it is needed to calculate the navigation solution. The decoding algorithm utilizes the message structure to enable its decoding for signals with high Bit Error Rate. The algorithms are demonstrated using simulated GPS C/A code signals, and TCXO clocks. The results have shown the algorithms ability to reliably work with 15 dB-Hz signals and acceleration over 6 g.

Multi-Target Tracking Using an Improved Gaussian Mixture CPHD Filter.

PubMed

Si, Weijian; Wang, Liwei; Qu, Zhiyu

2016-11-23

The cardinalized probability hypothesis density (CPHD) filter is an alternative approximation to the full multi-target Bayesian filter for tracking multiple targets. However, although the joint propagation of the posterior intensity and cardinality distribution in its recursion allows more reliable estimates of the target number than the PHD filter, the CPHD filter suffers from the spooky effect where there exists arbitrary PHD mass shifting in the presence of missed detections. To address this issue in the Gaussian mixture (GM) implementation of the CPHD filter, this paper presents an improved GM-CPHD filter, which incorporates a weight redistribution scheme into the filtering process to modify the updated weights of the Gaussian components when missed detections occur. In addition, an efficient gating strategy that can adaptively adjust the gate sizes according to the number of missed detections of each Gaussian component is also presented to further improve the computational efficiency of the proposed filter. Simulation results demonstrate that the proposed method offers favorable performance in terms of both estimation accuracy and robustness to clutter and detection uncertainty over the existing methods.

Worst-error analysis of batch filter and sequential filter in navigation problems. [in spacecraft trajectory estimation

NASA Technical Reports Server (NTRS)

Nishimura, T.

1975-01-01

This paper proposes a worst-error analysis for dealing with problems of estimation of spacecraft trajectories in deep space missions. Navigation filters in use assume either constant or stochastic (Markov) models for their estimated parameters. When the actual behavior of these parameters does not follow the pattern of the assumed model, the filters sometimes result in very poor performance. To prepare for such pathological cases, the worst errors of both batch and sequential filters are investigated based on the incremental sensitivity studies of these filters. By finding critical switching instances of non-gravitational accelerations, intensive tracking can be carried out around those instances. Also the worst errors in the target plane provide a measure in assignment of the propellant budget for trajectory corrections. Thus the worst-error study presents useful information as well as practical criteria in establishing the maneuver and tracking strategy of spacecraft's missions.

Research of maneuvering target prediction and tracking technology based on IMM algorithm

NASA Astrophysics Data System (ADS)

Cao, Zheng; Mao, Yao; Deng, Chao; Liu, Qiong; Chen, Jing

2016-09-01

Maneuvering target prediction and tracking technology is widely used in both military and civilian applications, the study of those technologies is all along the hotspot and difficulty. In the Electro-Optical acquisition-tracking-pointing system (ATP), the primary traditional maneuvering targets are ballistic target, large aircraft and other big targets. Those targets have the features of fast velocity and a strong regular trajectory and Kalman Filtering and polynomial fitting have good effects when they are used to track those targets. In recent years, the small unmanned aerial vehicles developed rapidly for they are small, nimble and simple operation. The small unmanned aerial vehicles have strong maneuverability in the observation system of ATP although they are close-in, slow and small targets. Moreover, those vehicles are under the manual operation, therefore, the acceleration of them changes greatly and they move erratically. So the prediction and tracking precision is low when traditional algorithms are used to track the maneuvering fly of those targets, such as speeding up, turning, climbing and so on. The interacting multiple model algorithm (IMM) use multiple models to match target real movement trajectory, there are interactions between each model. The IMM algorithm can switch model based on a Markov chain to adapt to the change of target movement trajectory, so it is suitable to solve the prediction and tracking problems of the small unmanned aerial vehicles because of the better adaptability of irregular movement. This paper has set up model set of constant velocity model (CV), constant acceleration model (CA), constant turning model (CT) and current statistical model. And the results of simulating and analyzing the real movement trajectory data of the small unmanned aerial vehicles show that the prediction and tracking technology based on the interacting multiple model algorithm can get relatively lower tracking error and improve tracking precision comparing with traditional algorithms.

Particle Filtering with Region-based Matching for Tracking of Partially Occluded and Scaled Targets*

PubMed Central

Nakhmani, Arie; Tannenbaum, Allen

2012-01-01

Visual tracking of arbitrary targets in clutter is important for a wide range of military and civilian applications. We propose a general framework for the tracking of scaled and partially occluded targets, which do not necessarily have prominent features. The algorithm proposed in the present paper utilizes a modified normalized cross-correlation as the likelihood for a particle filter. The algorithm divides the template, selected by the user in the first video frame, into numerous patches. The matching process of these patches by particle filtering allows one to handle the target’s occlusions and scaling. Experimental results with fixed rectangular templates show that the method is reliable for videos with nonstationary, noisy, and cluttered background, and provides accurate trajectories in cases of target translation, scaling, and occlusion. PMID:22506088

Fast and accurate fitting and filtering of noisy exponentials in Legendre space.

PubMed

Bao, Guobin; Schild, Detlev

2014-01-01

The parameters of experimentally obtained exponentials are usually found by least-squares fitting methods. Essentially, this is done by minimizing the mean squares sum of the differences between the data, most often a function of time, and a parameter-defined model function. Here we delineate a novel method where the noisy data are represented and analyzed in the space of Legendre polynomials. This is advantageous in several respects. First, parameter retrieval in the Legendre domain is typically two orders of magnitude faster than direct fitting in the time domain. Second, data fitting in a low-dimensional Legendre space yields estimates for amplitudes and time constants which are, on the average, more precise compared to least-squares-fitting with equal weights in the time domain. Third, the Legendre analysis of two exponentials gives satisfactory estimates in parameter ranges where least-squares-fitting in the time domain typically fails. Finally, filtering exponentials in the domain of Legendre polynomials leads to marked noise removal without the phase shift characteristic for conventional lowpass filters.

Skin dose mapping for non-uniform x-ray fields using a backscatter point spread function

NASA Astrophysics Data System (ADS)

Vijayan, Sarath; Xiong, Zhenyu; Shankar, Alok; Rudin, Stephen; Bednarek, Daniel R.

2017-03-01

Beam shaping devices like ROI attenuators and compensation filters modulate the intensity distribution of the xray beam incident on the patient. This results in a spatial variation of skin dose due to the variation of primary radiation and also a variation in backscattered radiation from the patient. To determine the backscatter component, backscatter point spread functions (PSF) are generated using EGS Monte-Carlo software. For this study, PSF's were determined by simulating a 1 mm beam incident on the lateral surface of an anthropomorphic head phantom and a 20 cm thick PMMA block phantom. The backscatter PSF's for the head phantom and PMMA phantom are curve fit with a Lorentzian function after being normalized to the primary dose intensity (PSFn). PSFn is convolved with the primary dose distribution to generate the scatter dose distribution, which is added to the primary to obtain the total dose distribution. The backscatter convolution technique is incorporated in the dose tracking system (DTS), which tracks skin dose during fluoroscopic procedures and provides a color map of the dose distribution on a 3D patient graphic model. A convolution technique is developed for the backscatter dose determination for the nonuniformly spaced graphic-model surface vertices. A Gafchromic film validation was performed for shaped x-ray beams generated with an ROI attenuator and with two compensation filters inserted into the field. The total dose distribution calculated by the backscatter convolution technique closely agreed with that measured with the film.

Distributed multi-sensor particle filter for bearings-only tracking

NASA Astrophysics Data System (ADS)

Zhang, Jungen; Ji, Hongbing

2012-02-01

In this article, the classical bearings-only tracking (BOT) problem for a single target is addressed, which belongs to the general class of non-linear filtering problems. Due to the fact that the radial distance observability of the target is poor, the algorithm-based sequential Monte-Carlo (particle filtering, PF) methods generally show instability and filter divergence. A new stable distributed multi-sensor PF method is proposed for BOT. The sensors process their measurements at their sites using a hierarchical PF approach, which transforms the BOT problem from Cartesian coordinate to the logarithmic polar coordinate and separates the observable components from the unobservable components of the target. In the fusion centre, the target state can be estimated by utilising the multi-sensor optimal information fusion rule. Furthermore, the computation of a theoretical Cramer-Rao lower bound is given for the multi-sensor BOT problem. Simulation results illustrate that the proposed tracking method can provide better performances than the traditional PF method.

Collaborative emitter tracking using Rao-Blackwellized random exchange diffusion particle filtering

NASA Astrophysics Data System (ADS)

Bruno, Marcelo G. S.; Dias, Stiven S.

2014-12-01

We introduce in this paper the fully distributed, random exchange diffusion particle filter (ReDif-PF) to track a moving emitter using multiple received signal strength (RSS) sensors. We consider scenarios with both known and unknown sensor model parameters. In the unknown parameter case, a Rao-Blackwellized (RB) version of the random exchange diffusion particle filter, referred to as the RB ReDif-PF, is introduced. In a simulated scenario with a partially connected network, the proposed ReDif-PF outperformed a PF tracker that assimilates local neighboring measurements only and also outperformed a linearized random exchange distributed extended Kalman filter (ReDif-EKF). Furthermore, the novel ReDif-PF matched the tracking error performance of alternative suboptimal distributed PFs based respectively on iterative Markov chain move steps and selective average gossiping with an inter-node communication cost that is roughly two orders of magnitude lower than the corresponding cost for the Markov chain and selective gossip filters. Compared to a broadcast-based filter which exactly mimics the optimal centralized tracker or its equivalent (exact) consensus-based implementations, ReDif-PF showed a degradation in steady-state error performance. However, compared to the optimal consensus-based trackers, ReDif-PF is better suited for real-time applications since it does not require iterative inter-node communication between measurement arrivals.

High resolution vertical profiles of wind, temperature and humidity obtained by computer processing and digital filtering of radiosonde and radar tracking data from the ITCZ experiment of 1977

NASA Technical Reports Server (NTRS)

Danielson, E. F.; Hipskind, R. S.; Gaines, S. E.

1980-01-01

Results are presented from computer processing and digital filtering of radiosonde and radar tracking data obtained during the ITCZ experiment when coordinated measurements were taken daily over a 16 day period across the Panama Canal Zone. The temperature relative humidity and wind velocity profiles are discussed.

Detection of micro gap weld joint by using magneto-optical imaging and Kalman filtering compensated with RBF neural network

NASA Astrophysics Data System (ADS)

Gao, Xiangdong; Chen, Yuquan; You, Deyong; Xiao, Zhenlin; Chen, Xiaohui

2017-02-01

An approach for seam tracking of micro gap weld whose width is less than 0.1 mm based on magneto optical (MO) imaging technique during butt-joint laser welding of steel plates is investigated. Kalman filtering(KF) technology with radial basis function(RBF) neural network for weld detection by an MO sensor was applied to track the weld center position. Because the laser welding system process noises and the MO sensor measurement noises were colored noises, the estimation accuracy of traditional KF for seam tracking was degraded by the system model with extreme nonlinearities and could not be solved by the linear state-space model. Also, the statistics characteristics of noises could not be accurately obtained in actual welding. Thus, a RBF neural network was applied to the KF technique to compensate for the weld tracking errors. The neural network can restrain divergence filter and improve the system robustness. In comparison of traditional KF algorithm, the RBF with KF was not only more effectively in improving the weld tracking accuracy but also reduced noise disturbance. Experimental results showed that magneto optical imaging technique could be applied to detect micro gap weld accurately, which provides a novel approach for micro gap seam tracking.

Tracking multiple particles in fluorescence time-lapse microscopy images via probabilistic data association.

PubMed

Godinez, William J; Rohr, Karl

2015-02-01

Tracking subcellular structures as well as viral structures displayed as 'particles' in fluorescence microscopy images yields quantitative information on the underlying dynamical processes. We have developed an approach for tracking multiple fluorescent particles based on probabilistic data association. The approach combines a localization scheme that uses a bottom-up strategy based on the spot-enhancing filter as well as a top-down strategy based on an ellipsoidal sampling scheme that uses the Gaussian probability distributions computed by a Kalman filter. The localization scheme yields multiple measurements that are incorporated into the Kalman filter via a combined innovation, where the association probabilities are interpreted as weights calculated using an image likelihood. To track objects in close proximity, we compute the support of each image position relative to the neighboring objects of a tracked object and use this support to recalculate the weights. To cope with multiple motion models, we integrated the interacting multiple model algorithm. The approach has been successfully applied to synthetic 2-D and 3-D images as well as to real 2-D and 3-D microscopy images, and the performance has been quantified. In addition, the approach was successfully applied to the 2-D and 3-D image data of the recent Particle Tracking Challenge at the IEEE International Symposium on Biomedical Imaging (ISBI) 2012.

Using Morphological Filters for Pupil Detection in Infrared Videos

DTIC Science & Technology

2010-01-05

detect the subjects’ pupils, with manual parameterization of the filter coefficients. False detections due to background clutter from eyeglasses and...5 c) Fitting the detected objects with ellipses...boundaries overlaid on the original infrared image..................... 7 Figure 8 – Fitting the elongated pupils with circles often failed

Tracking of multiple targets using online learning for reference model adaptation.

PubMed

Pernkopf, Franz

2008-12-01

Recently, much work has been done in multiple object tracking on the one hand and on reference model adaptation for a single-object tracker on the other side. In this paper, we do both tracking of multiple objects (faces of people) in a meeting scenario and online learning to incrementally update the models of the tracked objects to account for appearance changes during tracking. Additionally, we automatically initialize and terminate tracking of individual objects based on low-level features, i.e., face color, face size, and object movement. Many methods unlike our approach assume that the target region has been initialized by hand in the first frame. For tracking, a particle filter is incorporated to propagate sample distributions over time. We discuss the close relationship between our implemented tracker based on particle filters and genetic algorithms. Numerous experiments on meeting data demonstrate the capabilities of our tracking approach. Additionally, we provide an empirical verification of the reference model learning during tracking of indoor and outdoor scenes which supports a more robust tracking. Therefore, we report the average of the standard deviation of the trajectories over numerous tracking runs depending on the learning rate.

A Framework for Applying Point Clouds Grabbed by Multi-Beam LIDAR in Perceiving the Driving Environment

PubMed Central

Liu, Jian; Liang, Huawei; Wang, Zhiling; Chen, Xiangcheng

2015-01-01

The quick and accurate understanding of the ambient environment, which is composed of road curbs, vehicles, pedestrians, etc., is critical for developing intelligent vehicles. The road elements included in this work are road curbs and dynamic road obstacles that directly affect the drivable area. A framework for the online modeling of the driving environment using a multi-beam LIDAR, i.e., a Velodyne HDL-64E LIDAR, which describes the 3D environment in the form of a point cloud, is reported in this article. First, ground segmentation is performed via multi-feature extraction of the raw data grabbed by the Velodyne LIDAR to satisfy the requirement of online environment modeling. Curbs and dynamic road obstacles are detected and tracked in different manners. Curves are fitted for curb points, and points are clustered into bundles whose form and kinematics parameters are calculated. The Kalman filter is used to track dynamic obstacles, whereas the snake model is employed for curbs. Results indicate that the proposed framework is robust under various environments and satisfies the requirements for online processing. PMID:26404290

Calorie counting and fitness tracking technology: Associations with eating disorder symptomatology.

PubMed

Simpson, Courtney C; Mazzeo, Suzanne E

2017-08-01

The use of online calorie tracking applications and activity monitors is increasing exponentially. Anecdotal reports document the potential for these trackers to trigger, maintain, or exacerbate eating disorder symptomatology. Yet, research has not examined the relation between use of these devices and eating disorder-related attitudes and behaviors. This study explored associations between the use of calorie counting and fitness tracking devices and eating disorder symptomatology. Participants (N=493) were college students who reported their use of tracking technology and completed measures of eating disorder symptomatology. Individuals who reported using calorie trackers manifested higher levels of eating concern and dietary restraint, controlling for BMI. Additionally, fitness tracking was uniquely associated with ED symptomatology after adjusting for gender and bingeing and purging behavior within the past month. Findings highlight associations between use of calorie and fitness trackers and eating disorder symptomatology. Although preliminary, overall results suggest that for some individuals, these devices might do more harm than good. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking

PubMed Central

Wang, Xuedong; Sun, Shudong; Corchado, Juan M.

2017-01-01

We review some advances of the particle filtering (PF) algorithm that have been achieved in the last decade in the context of target tracking, with regard to either a single target or multiple targets in the presence of false or missing data. The first part of our review is on remarkable achievements that have been made for the single-target PF from several aspects including importance proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal systems. The second part of our review is on analyzing the intractable challenges raised within the general multitarget (multi-sensor) tracking due to random target birth and termination, false alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream multitarget PF approaches consist of two main classes, one based on M2T association approaches and the other not such as the finite set statistics-based PF. In either case, significant challenges remain due to unknown tracking scenarios and integrated tracking management. PMID:29168772

Successful Training of Filtering Mechanisms in Multiple Object Tracking Does Not Transfer to Filtering Mechanisms in a Visual Working Memory Task: Behavioral and Electrophysiological Evidence

ERIC Educational Resources Information Center

Arend, Anna M.; Zimmer, Hubert D.

2012-01-01

In this training study, we aimed to selectively train participants' filtering mechanisms to enhance visual working memory (WM) efficiency. The highly restricted nature of visual WM capacity renders efficient filtering mechanisms crucial for its successful functioning. Filtering efficiency in visual WM can be measured via the lateralized change…

A Distributed Multiobject Tracking Algorithm for Passive Sensor Networks

DTIC Science & Technology

1980-06-23

between the true acoustic azimuth and the filter estimate was computed for e4eii track. An overall aso was computed for each filter. The results of...h B sindB’ =°S6B (5.26) (Remember 6. is a negative quantity in this figure). Also, 1. tA- t = A (5.28)A t -t (5.29) From (5.25) and (5.26) we geL

An Unscented Kalman-Particle Hybrid Filter for Space Object Tracking

NASA Astrophysics Data System (ADS)

Raihan A. V, Dilshad; Chakravorty, Suman

2018-03-01

Optimal and consistent estimation of the state of space objects is pivotal to surveillance and tracking applications. However, probabilistic estimation of space objects is made difficult by the non-Gaussianity and nonlinearity associated with orbital mechanics. In this paper, we present an unscented Kalman-particle hybrid filtering framework for recursive Bayesian estimation of space objects. The hybrid filtering scheme is designed to provide accurate and consistent estimates when measurements are sparse without incurring a large computational cost. It employs an unscented Kalman filter (UKF) for estimation when measurements are available. When the target is outside the field of view (FOV) of the sensor, it updates the state probability density function (PDF) via a sequential Monte Carlo method. The hybrid filter addresses the problem of particle depletion through a suitably designed filter transition scheme. To assess the performance of the hybrid filtering approach, we consider two test cases of space objects that are assumed to undergo full three dimensional orbital motion under the effects of J 2 and atmospheric drag perturbations. It is demonstrated that the hybrid filters can furnish fast, accurate and consistent estimates outperforming standard UKF and particle filter (PF) implementations.

Respirator Fact Sheet

MedlinePlus

... the respirator. If I have the right cartridges/filters for a certain hazard, and my mask fits, ... use and maintenance are essential. Will my cartridge/filter and respirator mask protect forever? Cartridges, filters, and ...

Real-time acquisition and tracking system with multiple Kalman filters

NASA Astrophysics Data System (ADS)

Beard, Gary C.; McCarter, Timothy G.; Spodeck, Walter; Fletcher, James E.

1994-07-01

The design of a real-time, ground-based, infrared tracking system with proven field success in tracking boost vehicles through burnout is presented with emphasis on the software design. The system was originally developed to deliver relative angular positions during boost, and thrust termination time to a sensor fusion station in real-time. Autonomous target acquisition and angle-only tracking features were developed to ensure success under stressing conditions. A unique feature of the system is the incorporation of multiple copies of a Kalman filter tracking algorithm running in parallel in order to minimize run-time. The system is capable of updating the state vector for an object at measurement rates approaching 90 Hz. This paper will address the top-level software design, details of the algorithms employed, system performance history in the field, and possible future upgrades.

An Extended Kalman Filter-Based Attitude Tracking Algorithm for Star Sensors

PubMed Central

Li, Jian; Wei, Xinguo; Zhang, Guangjun

2017-01-01

Efficiency and reliability are key issues when a star sensor operates in tracking mode. In the case of high attitude dynamics, the performance of existing attitude tracking algorithms degenerates rapidly. In this paper an extended Kalman filtering-based attitude tracking algorithm is presented. The star sensor is modeled as a nonlinear stochastic system with the state estimate providing the three degree-of-freedom attitude quaternion and angular velocity. The star positions in the star image are predicted and measured to estimate the optimal attitude. Furthermore, all the cataloged stars observed in the sensor field-of-view according the predicted image motion are accessed using a catalog partition table to speed up the tracking, called star mapping. Software simulation and night-sky experiment are performed to validate the efficiency and reliability of the proposed method. PMID:28825684

An Extended Kalman Filter-Based Attitude Tracking Algorithm for Star Sensors.

PubMed

Li, Jian; Wei, Xinguo; Zhang, Guangjun

2017-08-21

Efficiency and reliability are key issues when a star sensor operates in tracking mode. In the case of high attitude dynamics, the performance of existing attitude tracking algorithms degenerates rapidly. In this paper an extended Kalman filtering-based attitude tracking algorithm is presented. The star sensor is modeled as a nonlinear stochastic system with the state estimate providing the three degree-of-freedom attitude quaternion and angular velocity. The star positions in the star image are predicted and measured to estimate the optimal attitude. Furthermore, all the cataloged stars observed in the sensor field-of-view according the predicted image motion are accessed using a catalog partition table to speed up the tracking, called star mapping. Software simulation and night-sky experiment are performed to validate the efficiency and reliability of the proposed method.

Human movement tracking based on Kalman filter

NASA Astrophysics Data System (ADS)

Zhang, Yi; Luo, Yuan

2006-11-01

During the rehabilitation process of the post-stroke patients is conducted, their movements need to be localized and learned so that incorrect movement can be instantly modified or tuned. Therefore, tracking these movement becomes vital and necessary for the rehabilitative course. In the technologies of human movement tracking, the position prediction of human movement is very important. In this paper, we first analyze the configuration of the human movement system and choice of sensors. Then, The Kalman filter algorithm and its modified algorithm are proposed and to be used to predict the position of human movement. In the end, on the basis of analyzing the performance of the method, it is clear that the method described can be used to the system of human movement tracking.

A-Track: Detecting Moving Objects in FITS images

NASA Astrophysics Data System (ADS)

Atay, T.; Kaplan, M.; Kilic, Y.; Karapinar, N.

2017-04-01

A-Track is a fast, open-source, cross-platform pipeline for detecting moving objects (asteroids and comets) in sequential telescope images in FITS format. The moving objects are detected using a modified line detection algorithm.

Fast and Accurate Fitting and Filtering of Noisy Exponentials in Legendre Space

PubMed Central

Bao, Guobin; Schild, Detlev

2014-01-01

The parameters of experimentally obtained exponentials are usually found by least-squares fitting methods. Essentially, this is done by minimizing the mean squares sum of the differences between the data, most often a function of time, and a parameter-defined model function. Here we delineate a novel method where the noisy data are represented and analyzed in the space of Legendre polynomials. This is advantageous in several respects. First, parameter retrieval in the Legendre domain is typically two orders of magnitude faster than direct fitting in the time domain. Second, data fitting in a low-dimensional Legendre space yields estimates for amplitudes and time constants which are, on the average, more precise compared to least-squares-fitting with equal weights in the time domain. Third, the Legendre analysis of two exponentials gives satisfactory estimates in parameter ranges where least-squares-fitting in the time domain typically fails. Finally, filtering exponentials in the domain of Legendre polynomials leads to marked noise removal without the phase shift characteristic for conventional lowpass filters. PMID:24603904

Analysis of Video-Based Microscopic Particle Trajectories Using Kalman Filtering

PubMed Central

Wu, Pei-Hsun; Agarwal, Ashutosh; Hess, Henry; Khargonekar, Pramod P.; Tseng, Yiider

2010-01-01

Abstract The fidelity of the trajectories obtained from video-based particle tracking determines the success of a variety of biophysical techniques, including in situ single cell particle tracking and in vitro motility assays. However, the image acquisition process is complicated by system noise, which causes positioning error in the trajectories derived from image analysis. Here, we explore the possibility of reducing the positioning error by the application of a Kalman filter, a powerful algorithm to estimate the state of a linear dynamic system from noisy measurements. We show that the optimal Kalman filter parameters can be determined in an appropriate experimental setting, and that the Kalman filter can markedly reduce the positioning error while retaining the intrinsic fluctuations of the dynamic process. We believe the Kalman filter can potentially serve as a powerful tool to infer a trajectory of ultra-high fidelity from noisy images, revealing the details of dynamic cellular processes. PMID:20550894

Quick-disconnect coupling/filter

NASA Technical Reports Server (NTRS)

Jankowski, F.

1977-01-01

Two-part coupling system for hose lines combines both connection and filter in one fitting. Flared fittings make coupling less prone to leakage, and reduced number of components speeds operation. These features may make coupler useful with liquid-bulk carriers, where materials (e.g., milk, cooking oil, and liquid sugar) must be transferred quickly from vehicle to storage facility.

Suppression of biodynamic interference in head-tracked teleoperation

NASA Technical Reports Server (NTRS)

Lifshitz, S.; Merhav, S. J.; Grunwald, A. J.; Tucker, G. E.; Tischler, M. B.

1991-01-01

The utility of helmet-tracked sights to provide pointing commands for teleoperation of cameras, lasers, or antennas in aircraft is degraded by the presence of uncommanded, involuntary heat motion, referred to as biodynamic interference. This interference limits the achievable precision required in pointing tasks. The noise contributions due to biodynamic interference consists of an additive component which is correlated with aircraft vibration and an uncorrelated, nonadditive component, referred to as remnant. An experimental simulation study is described which investigated the improvements achievable in pointing and tracking precision using dynamic display shifting in the helmet-mounted display. The experiment was conducted in a six degree of freedom motion base simulator with an emulated helmet-mounted display. Highly experienced pilot subjects performed precision head-pointing tasks while manually flying a visual flight-path tracking task. Four schemes using adaptive and low-pass filtering of the head motion were evaluated to determine their effects on task performance and pilot workload in the presence of whole-body vibration characteristic of helicopter flight. The results indicate that, for tracking tasks involving continuously moving targets, improvements of up to 70 percent can be achieved in percent on-target dwelling time and of up to 35 percent in rms tracking error, with the adaptive plus low-pass filter configuration. The results with the same filter configuration for the task of capturing randomly-positioned, stationary targets show an increase of up to 340 percent in the number of targets captured and an improvement of up to 24 percent in the average capture time. The adaptive plus low-pass filter combination was considered to exhibit the best overall display dynamics by each of the subjects.

Space debris tracking based on fuzzy running Gaussian average adaptive particle filter track-before-detect algorithm

NASA Astrophysics Data System (ADS)

Torteeka, Peerapong; Gao, Peng-Qi; Shen, Ming; Guo, Xiao-Zhang; Yang, Da-Tao; Yu, Huan-Huan; Zhou, Wei-Ping; Zhao, You

2017-02-01

Although tracking with a passive optical telescope is a powerful technique for space debris observation, it is limited by its sensitivity to dynamic background noise. Traditionally, in the field of astronomy, static background subtraction based on a median image technique has been used to extract moving space objects prior to the tracking operation, as this is computationally efficient. The main disadvantage of this technique is that it is not robust to variable illumination conditions. In this article, we propose an approach for tracking small and dim space debris in the context of a dynamic background via one of the optical telescopes that is part of the space surveillance network project, named the Asia-Pacific ground-based Optical Space Observation System or APOSOS. The approach combines a fuzzy running Gaussian average for robust moving-object extraction with dim-target tracking using a particle-filter-based track-before-detect method. The performance of the proposed algorithm is experimentally evaluated, and the results show that the scheme achieves a satisfactory level of accuracy for space debris tracking.

Effective Visual Tracking Using Multi-Block and Scale Space Based on Kernelized Correlation Filters

PubMed Central

Jeong, Soowoong; Kim, Guisik; Lee, Sangkeun

2017-01-01

Accurate scale estimation and occlusion handling is a challenging problem in visual tracking. Recently, correlation filter-based trackers have shown impressive results in terms of accuracy, robustness, and speed. However, the model is not robust to scale variation and occlusion. In this paper, we address the problems associated with scale variation and occlusion by employing a scale space filter and multi-block scheme based on a kernelized correlation filter (KCF) tracker. Furthermore, we develop a more robust algorithm using an appearance update model that approximates the change of state of occlusion and deformation. In particular, an adaptive update scheme is presented to make each process robust. The experimental results demonstrate that the proposed method outperformed 29 state-of-the-art trackers on 100 challenging sequences. Specifically, the results obtained with the proposed scheme were improved by 8% and 18% compared to those of the KCF tracker for 49 occlusion and 64 scale variation sequences, respectively. Therefore, the proposed tracker can be a robust and useful tool for object tracking when occlusion and scale variation are involved. PMID:28241475

Real-Time Motion Tracking for Indoor Moving Sphere Objects with a LiDAR Sensor.

PubMed

Huang, Lvwen; Chen, Siyuan; Zhang, Jianfeng; Cheng, Bang; Liu, Mingqing

2017-08-23

Object tracking is a crucial research subfield in computer vision and it has wide applications in navigation, robotics and military applications and so on. In this paper, the real-time visualization of 3D point clouds data based on the VLP-16 3D Light Detection and Ranging (LiDAR) sensor is achieved, and on the basis of preprocessing, fast ground segmentation, Euclidean clustering segmentation for outliers, View Feature Histogram (VFH) feature extraction, establishing object models and searching matching a moving spherical target, the Kalman filter and adaptive particle filter are used to estimate in real-time the position of a moving spherical target. The experimental results show that the Kalman filter has the advantages of high efficiency while adaptive particle filter has the advantages of high robustness and high precision when tested and validated on three kinds of scenes under the condition of target partial occlusion and interference, different moving speed and different trajectories. The research can be applied in the natural environment of fruit identification and tracking, robot navigation and control and other fields.

Effective Visual Tracking Using Multi-Block and Scale Space Based on Kernelized Correlation Filters.

PubMed

Jeong, Soowoong; Kim, Guisik; Lee, Sangkeun

2017-02-23

Accurate scale estimation and occlusion handling is a challenging problem in visual tracking. Recently, correlation filter-based trackers have shown impressive results in terms of accuracy, robustness, and speed. However, the model is not robust to scale variation and occlusion. In this paper, we address the problems associated with scale variation and occlusion by employing a scale space filter and multi-block scheme based on a kernelized correlation filter (KCF) tracker. Furthermore, we develop a more robust algorithm using an appearance update model that approximates the change of state of occlusion and deformation. In particular, an adaptive update scheme is presented to make each process robust. The experimental results demonstrate that the proposed method outperformed 29 state-of-the-art trackers on 100 challenging sequences. Specifically, the results obtained with the proposed scheme were improved by 8% and 18% compared to those of the KCF tracker for 49 occlusion and 64 scale variation sequences, respectively. Therefore, the proposed tracker can be a robust and useful tool for object tracking when occlusion and scale variation are involved.

Real-Time Motion Tracking for Indoor Moving Sphere Objects with a LiDAR Sensor

PubMed Central

Chen, Siyuan; Zhang, Jianfeng; Cheng, Bang; Liu, Mingqing

2017-01-01

Object tracking is a crucial research subfield in computer vision and it has wide applications in navigation, robotics and military applications and so on. In this paper, the real-time visualization of 3D point clouds data based on the VLP-16 3D Light Detection and Ranging (LiDAR) sensor is achieved, and on the basis of preprocessing, fast ground segmentation, Euclidean clustering segmentation for outliers, View Feature Histogram (VFH) feature extraction, establishing object models and searching matching a moving spherical target, the Kalman filter and adaptive particle filter are used to estimate in real-time the position of a moving spherical target. The experimental results show that the Kalman filter has the advantages of high efficiency while adaptive particle filter has the advantages of high robustness and high precision when tested and validated on three kinds of scenes under the condition of target partial occlusion and interference, different moving speed and different trajectories. The research can be applied in the natural environment of fruit identification and tracking, robot navigation and control and other fields. PMID:28832520

A fast ellipse extended target PHD filter using box-particle implementation

NASA Astrophysics Data System (ADS)

Zhang, Yongquan; Ji, Hongbing; Hu, Qi

2018-01-01

This paper presents a box-particle implementation of the ellipse extended target probability hypothesis density (ET-PHD) filter, called the ellipse extended target box particle PHD (EET-BP-PHD) filter, where the extended targets are described as a Poisson model developed by Gilholm et al. and the term "box" is here equivalent to the term "interval" used in interval analysis. The proposed EET-BP-PHD filter is capable of dynamically tracking multiple ellipse extended targets and estimating the target states and the number of targets, in the presence of clutter measurements, false alarms and missed detections. To derive the PHD recursion of the EET-BP-PHD filter, a suitable measurement likelihood is defined for a given partitioning cell, and the main implementation steps are presented along with the necessary box approximations and manipulations. The limitations and capabilities of the proposed EET-BP-PHD filter are illustrated by simulation examples. The simulation results show that a box-particle implementation of the ET-PHD filter can avoid the high number of particles and reduce computational burden, compared to a particle implementation of that for extended target tracking.

High Pass Filtering of Satellite Altimeter Data,

DTIC Science & Technology

1982-10-01

bathymetry [7] and filtered data tracks (N = 3, X = 200 km) near the Clipperton Fracture Zone just East of the Christmas Island Ridge. Along the multiple...We also notice a negative signature associated with the Clipperton Fracture Zone and extending over all the tracks. It may indicate a trough covered...in Mid-Pacific Seamount Province..Mid-Iat tic and near the Western Clipperton Fracture Zone respectively. These charts arc to he overlaid by Figures

Fusion of Low-Cost Imaging and Inertial Sensors for Navigation

DTIC Science & Technology

2007-01-01

an Integrated GPS/MEMS Inertial Navigation Pack- age. In Proceedings of ION GNSS 2004, pp. 825–832, September 2004. [3] R. G. Brown and P. Y. Hwang ...track- ing, with no a priori knowledge is provided in [13]. An on- line (Extended Kalman Filter-based) method for calculat- ing a trajectory by tracking...transformation, effectively constraining the resulting correspondence search space. The algorithm was incorporated into an extended Kalman filter and

Transforming War Fighting through the Use of Service Based Architecture (SBA) Technology

DTIC Science & Technology

2006-05-04

near-real-time video & telemetry to users on network using standard web-based protocols – Provides web-based access to archived video files MTI...Target Tracks Service Capabilities – Disseminates near-real-time MTI and Target Tracks to users on network based on consumer specified geographic...filter IBS SIGINT Service Capabilities – Disseminates near-real-time IBS SIGINT data to users on network based on consumer specified geographic filter

The importance of precision radar tracking data for the determination of density and winds from the high-altitude inflatable sphere

NASA Technical Reports Server (NTRS)

Schmidlin, F. J.; Michel, W. R.

1985-01-01

Analysis of inflatable sphere measurements obtained during the Energy Budget and MAP/WINE campaigns led to questions concerning the precision of the MPS-36 radar used for tracking the spheres; the compatibility of the sphere program with the MPS-36 radar tracking data; and the oversmoothing of derived parameters at high altitudes. Simulations, with winds having sinusoidal vertical wavelengths, were done with the sphere program (HIROBIN) to determine the resolving capability of various filters. It is concluded that given a precision radar and a perfectly performing sphere, the HIROBIN filters can be adjusted to provide small-scale perturbation information to 70 km (i.e., sinusoidal wavelengths of 2 km). It is recommended that the HIROBIN program be modified to enable it to use a variable length filter, that adjusts to fall velocity and accelerations to provide wind data with small perturbations.

Development of an optimal automatic control law and filter algorithm for steep glideslope capture and glideslope tracking

NASA Technical Reports Server (NTRS)

Halyo, N.

1976-01-01

A digital automatic control law to capture a steep glideslope and track the glideslope to a specified altitude is developed for the longitudinal/vertical dynamics of a CTOL aircraft using modern estimation and control techniques. The control law uses a constant gain Kalman filter to process guidance information from the microwave landing system, and acceleration from body mounted accelerometer data. The filter outputs navigation data and wind velocity estimates which are used in controlling the aircraft. Results from a digital simulation of the aircraft dynamics and the control law are presented for various wind conditions.

A real-time tracking system of infrared dim and small target based on FPGA and DSP

NASA Astrophysics Data System (ADS)

Rong, Sheng-hui; Zhou, Hui-xin; Qin, Han-lin; Wang, Bing-jian; Qian, Kun

2014-11-01

A core technology in the infrared warning system is the detection tracking of dim and small targets with complicated background. Consequently, running the detection algorithm on the hardware platform has highly practical value in the military field. In this paper, a real-time detection tracking system of infrared dim and small target which is used FPGA (Field Programmable Gate Array) and DSP (Digital Signal Processor) as the core was designed and the corresponding detection tracking algorithm and the signal flow is elaborated. At the first stage, the FPGA obtain the infrared image sequence from the sensor, then it suppresses background clutter by mathematical morphology method and enhances the target intensity by Laplacian of Gaussian operator. At the second stage, the DSP obtain both the original image and the filtered image form the FPGA via the video port. Then it segments the target from the filtered image by an adaptive threshold segmentation method and gets rid of false target by pipeline filter. Experimental results show that our system can achieve higher detection rate and lower false alarm rate.

Adaptive angular-velocity Vold-Kalman filter order tracking - Theoretical basis, numerical implementation and parameter investigation

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.

δ-Generalized Labeled Multi-Bernoulli Filter Using Amplitude Information of Neighboring Cells

PubMed Central

Liu, Chao; Lei, Peng; Qi, Yaolong

2018-01-01

The amplitude information (AI) of echoed signals plays an important role in radar target detection and tracking. A lot of research shows that the introduction of AI enables the tracking algorithm to distinguish targets from clutter better and then improves the performance of data association. The current AI-aided tracking algorithms only consider the signal amplitude in the range-azimuth cell where measurement exists. However, since radar echoes always contain backscattered signals from multiple cells, the useful information of neighboring cells would be lost if directly applying those existing methods. In order to solve this issue, a new δ-generalized labeled multi-Bernoulli (δ-GLMB) filter is proposed. It exploits the AI of radar echoes from neighboring cells to construct a united amplitude likelihood ratio, and then plugs it into the update process and the measurement-track assignment cost matrix of the δ-GLMB filter. Simulation results show that the proposed approach has better performance in target’s state and number estimation than that of the δ-GLMB only using single-cell AI in low signal-to-clutter-ratio (SCR) environment. PMID:29642595

Track Reconstruction and the Proton Radius Puzzle

NASA Astrophysics Data System (ADS)

Clark, Steven; Cline, Ethan; Gilman, Ron; MUSE Collaboration

2017-09-01

In 2010, Pohl et al. measured the proton charge radius to be 0.84184(67) fm using muonic hydrogen spectroscopy. This value differs about 5 σ from the CODATA proton radius from measurements with electrons. Other experiments with muons and electrons have confirmed the difference and the discrepancy has been termed the `Proton Radius Puzzle.' Currently there are no explanations for the puzzle. The MUon proton Scattering Experiment (MUSE) will make a significant measurement of the proton radius with muon scattering for the first time. The experiment tracks elastic scattering of electrons and muons off of liquid hydrogen. Particle tracks are reconstructed with track fitting software GenFit. Using a simulation of MUSE, GenFit has been determined to be proficient at track reconstruction. This project has been supported by funding from National Science Foundation Grant PHY-1560077.

Disclosure and Fit Capability of the Filtering Facepiece Respirator.

PubMed

Lofgren, Don J

2018-05-01

The filtering facepiece air-purifying respirator is annually purchased in the tens of millions and widely used for worker protection from harmful airborne particulates. The workplace consumers of this safety product, i.e., employers, workers, and safety and health professionals, have assurances of its effectiveness through the respirator certification and disclosure requirements of the National Institute for Occupational Safety and Health. However, the certification of a critical performance requirement has been missing for the approved filtering facepiece respirator since 1995: fit capability. Without this certification, consumers continue to be at risk of purchasing a respirator model that may fit a small percentage of the intended users. This commentary updates and expands an earlier one by this author, addresses the consequences of poorly fitting certified models on the market and lack of disclosure, and calls for further action by National Institute for Occupational Safety and Health to meet the needs and expectations of the consumer.

Parameter estimation of a three-axis spacecraft simulator using recursive least-squares approach with tracking differentiator and Extended Kalman Filter

NASA Astrophysics Data System (ADS)

Xu, Zheyao; Qi, Naiming; Chen, Yukun

2015-12-01

Spacecraft simulators are widely used to study the dynamics, guidance, navigation, and control of a spacecraft on the ground. A spacecraft simulator can have three rotational degrees of freedom by using a spherical air-bearing to simulate a frictionless and micro-gravity space environment. The moment of inertia and center of mass are essential for control system design of ground-based three-axis spacecraft simulators. Unfortunately, they cannot be known precisely. This paper presents two approaches, i.e. a recursive least-squares (RLS) approach with tracking differentiator (TD) and Extended Kalman Filter (EKF) method, to estimate inertia parameters. The tracking differentiator (TD) filter the noise coupled with the measured signals and generate derivate of the measured signals. Combination of two TD filters in series obtains the angular accelerations that are required in RLS (TD-TD-RLS). Another method that does not need to estimate the angular accelerations is using the integrated form of dynamics equation. An extended TD (ETD) filter which can also generate the integration of the function of signals is presented for RLS (denoted as ETD-RLS). States and inertia parameters are estimated simultaneously using EKF. The observability is analyzed. All proposed methods are illustrated by simulations and experiments.

Tracking Architecture Based on Dual-Filter with State Feedback and Its Application in Ultra-Tight GPS/INS Integration

PubMed Central

Zhang, Xi; Miao, Lingjuan; Shao, Haijun

2016-01-01

If a Kalman Filter (KF) is applied to Global Positioning System (GPS) baseband signal preprocessing, the estimates of signal phase and frequency can have low variance, even in highly dynamic situations. This paper presents a novel preprocessing scheme based on a dual-filter structure. Compared with the traditional model utilizing a single KF, this structure avoids carrier tracking being subjected to code tracking errors. Meanwhile, as the loop filters are completely removed, state feedback values are adopted to generate local carrier and code. Although local carrier frequency has a wide fluctuation, the accuracy of Doppler shift estimation is improved. In the ultra-tight GPS/Inertial Navigation System (INS) integration, the carrier frequency derived from the external navigation information is not viewed as the local carrier frequency directly. That facilitates retaining the design principle of state feedback. However, under harsh conditions, the GPS outputs may still bear large errors which can destroy the estimation of INS errors. Thus, an innovative integrated navigation filter is constructed by modeling the non-negligible errors in the estimated Doppler shifts, to ensure INS is properly calibrated. Finally, field test and semi-physical simulation based on telemetered missile trajectory validate the effectiveness of methods proposed in this paper. PMID:27144570

Tracking Architecture Based on Dual-Filter with State Feedback and Its Application in Ultra-Tight GPS/INS Integration.

PubMed

Zhang, Xi; Miao, Lingjuan; Shao, Haijun

2016-05-02

If a Kalman Filter (KF) is applied to Global Positioning System (GPS) baseband signal preprocessing, the estimates of signal phase and frequency can have low variance, even in highly dynamic situations. This paper presents a novel preprocessing scheme based on a dual-filter structure. Compared with the traditional model utilizing a single KF, this structure avoids carrier tracking being subjected to code tracking errors. Meanwhile, as the loop filters are completely removed, state feedback values are adopted to generate local carrier and code. Although local carrier frequency has a wide fluctuation, the accuracy of Doppler shift estimation is improved. In the ultra-tight GPS/Inertial Navigation System (INS) integration, the carrier frequency derived from the external navigation information is not viewed as the local carrier frequency directly. That facilitates retaining the design principle of state feedback. However, under harsh conditions, the GPS outputs may still bear large errors which can destroy the estimation of INS errors. Thus, an innovative integrated navigation filter is constructed by modeling the non-negligible errors in the estimated Doppler shifts, to ensure INS is properly calibrated. Finally, field test and semi-physical simulation based on telemetered missile trajectory validate the effectiveness of methods proposed in this paper.

Orbital State Uncertainty Realism

NASA Astrophysics Data System (ADS)

Horwood, J.; Poore, A. B.

2012-09-01

Fundamental to the success of the space situational awareness (SSA) mission is the rigorous inclusion of uncertainty in the space surveillance network. The *proper characterization of uncertainty* in the orbital state of a space object is a common requirement to many SSA functions including tracking and data association, resolution of uncorrelated tracks (UCTs), conjunction analysis and probability of collision, sensor resource management, and anomaly detection. While tracking environments, such as air and missile defense, make extensive use of Gaussian and local linearity assumptions within algorithms for uncertainty management, space surveillance is inherently different due to long time gaps between updates, high misdetection rates, nonlinear and non-conservative dynamics, and non-Gaussian phenomena. The latter implies that "covariance realism" is not always sufficient. SSA also requires "uncertainty realism"; the proper characterization of both the state and covariance and all non-zero higher-order cumulants. In other words, a proper characterization of a space object's full state *probability density function (PDF)* is required. In order to provide a more statistically rigorous treatment of uncertainty in the space surveillance tracking environment and to better support the aforementioned SSA functions, a new class of multivariate PDFs are formulated which more accurately characterize the uncertainty of a space object's state or orbit. The new distribution contains a parameter set controlling the higher-order cumulants which gives the level sets a distinctive "banana" or "boomerang" shape and degenerates to a Gaussian in a suitable limit. Using the new class of PDFs within the general Bayesian nonlinear filter, the resulting filter prediction step (i.e., uncertainty propagation) is shown to have the *same computational cost as the traditional unscented Kalman filter* with the former able to maintain a proper characterization of the uncertainty for up to *ten times as long* as the latter. The filter correction step also furnishes a statistically rigorous *prediction error* which appears in the likelihood ratios for scoring the association of one report or observation to another. Thus, the new filter can be used to support multi-target tracking within a general multiple hypothesis tracking framework. Additionally, the new distribution admits a distance metric which extends the classical Mahalanobis distance (chi^2 statistic). This metric provides a test for statistical significance and facilitates single-frame data association methods with the potential to easily extend the covariance-based track association algorithm of Hill, Sabol, and Alfriend. The filtering, data fusion, and association methods using the new class of orbital state PDFs are shown to be mathematically tractable and operationally viable.

An Unscented Kalman Filter Approach to the Estimation of Nonlinear Dynamical Systems Models

ERIC Educational Resources Information Center

Chow, Sy-Miin; Ferrer, Emilio; Nesselroade, John R.

2007-01-01

In the past several decades, methodologies used to estimate nonlinear relationships among latent variables have been developed almost exclusively to fit cross-sectional models. We present a relatively new estimation approach, the unscented Kalman filter (UKF), and illustrate its potential as a tool for fitting nonlinear dynamic models in two ways:…

Multi-filter spectrophotometry of quasar environments

NASA Technical Reports Server (NTRS)

Craven, Sally E.; Hickson, Paul; Yee, Howard K. C.

1993-01-01

A many-filter photometric technique for determining redshifts and morphological types, by fitting spectral templates to spectral energy distributions, has good potential for application in surveys. Despite success in studies performed on simulated data, the results have not been fully reliable when applied to real, low signal-to-noise data. We are investigating techniques to improve the fitting process.

Detection and tracking of a moving target using SAR images with the particle filter-based track-before-detect algorithm.

PubMed

Gao, Han; Li, Jingwen

2014-06-19

A novel approach to detecting and tracking a moving target using synthetic aperture radar (SAR) images is proposed in this paper. Achieved with the particle filter (PF) based track-before-detect (TBD) algorithm, the approach is capable of detecting and tracking the low signal-to-noise ratio (SNR) moving target with SAR systems, which the traditional track-after-detect (TAD) approach is inadequate for. By incorporating the signal model of the SAR moving target into the algorithm, the ambiguity in target azimuth position and radial velocity is resolved while tracking, which leads directly to the true estimation. With the sub-area substituted for the whole area to calculate the likelihood ratio and a pertinent choice of the number of particles, the computational efficiency is improved with little loss in the detection and tracking performance. The feasibility of the approach is validated and the performance is evaluated with Monte Carlo trials. It is demonstrated that the proposed approach is capable to detect and track a moving target with SNR as low as 7 dB, and outperforms the traditional TAD approach when the SNR is below 14 dB.

Detection and Tracking of a Moving Target Using SAR Images with the Particle Filter-Based Track-Before-Detect Algorithm

PubMed Central

Gao, Han; Li, Jingwen

2014-01-01

A novel approach to detecting and tracking a moving target using synthetic aperture radar (SAR) images is proposed in this paper. Achieved with the particle filter (PF) based track-before-detect (TBD) algorithm, the approach is capable of detecting and tracking the low signal-to-noise ratio (SNR) moving target with SAR systems, which the traditional track-after-detect (TAD) approach is inadequate for. By incorporating the signal model of the SAR moving target into the algorithm, the ambiguity in target azimuth position and radial velocity is resolved while tracking, which leads directly to the true estimation. With the sub-area substituted for the whole area to calculate the likelihood ratio and a pertinent choice of the number of particles, the computational efficiency is improved with little loss in the detection and tracking performance. The feasibility of the approach is validated and the performance is evaluated with Monte Carlo trials. It is demonstrated that the proposed approach is capable to detect and track a moving target with SNR as low as 7 dB, and outperforms the traditional TAD approach when the SNR is below 14 dB. PMID:24949640

Filter for third order phase locked loops

NASA Technical Reports Server (NTRS)

Crow, R. B.; Tausworthe, R. C. (Inventor)

1973-01-01

Filters for third-order phase-locked loops are used in receivers to acquire and track carrier signals, particularly signals subject to high doppler-rate changes in frequency. A loop filter with an open-loop transfer function and set of loop constants, setting the damping factor equal to unity are provided.

Two new methods to increase the contrast of track-etch neutron radiographs

NASA Technical Reports Server (NTRS)

Morley, J.

1973-01-01

In one method, fluorescent dye is deposited into tracks of radiograph and viewed under ultraviolet light. In second method, track-etch radiograph is placed between crossed polaroid filters, exposed to diffused light and resulting image is projected onto photographic film.

Long-term scale adaptive tracking with kernel correlation filters

NASA Astrophysics Data System (ADS)

Wang, Yueren; Zhang, Hong; Zhang, Lei; Yang, Yifan; Sun, Mingui

2018-04-01

Object tracking in video sequences has broad applications in both military and civilian domains. However, as the length of input video sequence increases, a number of problems arise, such as severe object occlusion, object appearance variation, and object out-of-view (some portion or the entire object leaves the image space). To deal with these problems and identify the object being tracked from cluttered background, we present a robust appearance model using Speeded Up Robust Features (SURF) and advanced integrated features consisting of the Felzenszwalb's Histogram of Oriented Gradients (FHOG) and color attributes. Since re-detection is essential in long-term tracking, we develop an effective object re-detection strategy based on moving area detection. We employ the popular kernel correlation filters in our algorithm design, which facilitates high-speed object tracking. Our evaluation using the CVPR2013 Object Tracking Benchmark (OTB2013) dataset illustrates that the proposed algorithm outperforms reference state-of-the-art trackers in various challenging scenarios.

Application of unscented Kalman filter for robust pose estimation in image-guided surgery

NASA Astrophysics Data System (ADS)

Vaccarella, Alberto; De Momi, Elena; Valenti, Marta; Ferrigno, Giancarlo; Enquobahrie, Andinet

2012-02-01

Image-guided surgery (IGS) allows clinicians to view current, intra-operative scenes superimposed on preoperative images (typically MRI or CT scans). IGS systems use localization systems to track and visualize surgical tools overlaid on top of preoperative images of the patient during surgery. The most commonly used localization systems in the Operating Rooms (OR) are optical tracking systems (OTS) due to their ease of use and cost effectiveness. However, OTS' suffer from the major drawback of line-of-sight requirements. State space approaches based on different implementations of the Kalman filter have recently been investigated in order to compensate for short line-of-sight occlusion. However, the proposed parameterizations for the rigid body orientation suffer from singularities at certain values of rotation angles. The purpose of this work is to develop a quaternion-based Unscented Kalman Filter (UKF) for robust optical tracking of both position and orientation of surgical tools in order to compensate marker occlusion issues. This paper presents preliminary results towards a Kalman-based Sensor Management Engine (SME). The engine will filter and fuse multimodal tracking streams of data. This work was motivated by our experience working in robot-based applications for keyhole neurosurgery (ROBOCAST project). The algorithm was evaluated using real data from NDI Polaris tracker. The results show that our estimation technique is able to compensate for marker occlusion with a maximum error of 2.5° for orientation and 2.36 mm for position. The proposed approach will be useful in over-crowded state-of-the-art ORs where achieving continuous visibility of all tracked objects will be difficult.

A sensor fusion method for tracking vertical velocity and height based on inertial and barometric altimeter measurements.

PubMed

Sabatini, Angelo Maria; Genovese, Vincenzo

2014-07-24

A sensor fusion method was developed for vertical channel stabilization by fusing inertial measurements from an Inertial Measurement Unit (IMU) and pressure altitude measurements from a barometric altimeter integrated in the same device (baro-IMU). An Extended Kalman Filter (EKF) estimated the quaternion from the sensor frame to the navigation frame; the sensed specific force was rotated into the navigation frame and compensated for gravity, yielding the vertical linear acceleration; finally, a complementary filter driven by the vertical linear acceleration and the measured pressure altitude produced estimates of height and vertical velocity. A method was also developed to condition the measured pressure altitude using a whitening filter, which helped to remove the short-term correlation due to environment-dependent pressure changes from raw pressure altitude. The sensor fusion method was implemented to work on-line using data from a wireless baro-IMU and tested for the capability of tracking low-frequency small-amplitude vertical human-like motions that can be critical for stand-alone inertial sensor measurements. Validation tests were performed in different experimental conditions, namely no motion, free-fall motion, forced circular motion and squatting. Accurate on-line tracking of height and vertical velocity was achieved, giving confidence to the use of the sensor fusion method for tracking typical vertical human motions: velocity Root Mean Square Error (RMSE) was in the range 0.04-0.24 m/s; height RMSE was in the range 5-68 cm, with statistically significant performance gains when the whitening filter was used by the sensor fusion method to track relatively high-frequency vertical motions.

Two-Dimensional Stochastic Projections for Tight Integration of Optical and Inertial Sensors for Navigation

DTIC Science & Technology

2007-01-01

Intelligent Robots and Systems, vol- ume 1, pp. 123–128, September 2002. [2] R. G. Brown and P. Y. Hwang . Introduction to Ran- dom Signals and Applied... Kalman Filter-based) method for calculat- ing a trajectory by tracking features at an unknown location on the Earth’s surface, provided the topography...Extended Kalman Filter (EKF) and an automatic target tracking algorithm. In the following section, the integration architecture is presented, which in

Arbitrary-step randomly delayed robust filter with application to boost phase tracking

NASA Astrophysics Data System (ADS)

Qin, Wutao; Wang, Xiaogang; Bai, Yuliang; Cui, Naigang

2018-04-01

The conventional filters such as extended Kalman filter, unscented Kalman filter and cubature Kalman filter assume that the measurement is available in real-time and the measurement noise is Gaussian white noise. But in practice, both two assumptions are invalid. To solve this problem, a novel algorithm is proposed by taking the following four steps. At first, the measurement model is modified by the Bernoulli random variables to describe the random delay. Then, the expression of predicted measurement and covariance are reformulated, which could get rid of the restriction that the maximum number of delay must be one or two and the assumption that probabilities of Bernoulli random variables taking the value one are equal. Next, the arbitrary-step randomly delayed high-degree cubature Kalman filter is derived based on the 5th-degree spherical-radial rule and the reformulated expressions. Finally, the arbitrary-step randomly delayed high-degree cubature Kalman filter is modified to the arbitrary-step randomly delayed high-degree cubature Huber-based filter based on the Huber technique, which is essentially an M-estimator. Therefore, the proposed filter is not only robust to the randomly delayed measurements, but robust to the glint noise. The application to the boost phase tracking example demonstrate the superiority of the proposed algorithms.

An analysis of neural receptive field plasticity by point process adaptive filtering

PubMed Central

Brown, Emery N.; Nguyen, David P.; Frank, Loren M.; Wilson, Matthew A.; Solo, Victor

2001-01-01

Neural receptive fields are plastic: with experience, neurons in many brain regions change their spiking responses to relevant stimuli. Analysis of receptive field plasticity from experimental measurements is crucial for understanding how neural systems adapt their representations of relevant biological information. Current analysis methods using histogram estimates of spike rate functions in nonoverlapping temporal windows do not track the evolution of receptive field plasticity on a fine time scale. Adaptive signal processing is an established engineering paradigm for estimating time-varying system parameters from experimental measurements. We present an adaptive filter algorithm for tracking neural receptive field plasticity based on point process models of spike train activity. We derive an instantaneous steepest descent algorithm by using as the criterion function the instantaneous log likelihood of a point process spike train model. We apply the point process adaptive filter algorithm in a study of spatial (place) receptive field properties of simulated and actual spike train data from rat CA1 hippocampal neurons. A stability analysis of the algorithm is sketched in the Appendix. The adaptive algorithm can update the place field parameter estimates on a millisecond time scale. It reliably tracked the migration, changes in scale, and changes in maximum firing rate characteristic of hippocampal place fields in a rat running on a linear track. Point process adaptive filtering offers an analytic method for studying the dynamics of neural receptive fields. PMID:11593043

The combined use of order tracking techniques for enhanced Fourier analysis of order components

NASA Astrophysics Data System (ADS)

Wang, K. S.; Heyns, P. S.

2011-04-01

Order tracking is one of the most important vibration analysis techniques for diagnosing faults in rotating machinery. It can be performed in many different ways, each of these with distinct advantages and disadvantages. However, in the end the analyst will often use Fourier analysis to transform the data from a time series to frequency or order spectra. It is therefore surprising that the study of the Fourier analysis of order-tracked systems seems to have been largely ignored in the literature. This paper considers the frequently used Vold-Kalman filter-based order tracking and computed order tracking techniques. The main pros and cons of each technique for Fourier analysis are discussed and the sequential use of Vold-Kalman filtering and computed order tracking is proposed as a novel idea to enhance the results of Fourier analysis for determining the order components. The advantages of the combined use of these order tracking techniques are demonstrated numerically on an SDOF rotor simulation model. Finally, the approach is also demonstrated on experimental data from a real rotating machine.

Lidar inversion of atmospheric backscatter and extinction-to-backscatter ratios by use of a Kalman filter.

PubMed

Rocadenbosch, F; Soriano, C; Comerón, A; Baldasano, J M

1999-05-20

A first inversion of the backscatter profile and extinction-to-backscatter ratio from pulsed elastic-backscatter lidar returns is treated by means of an extended Kalman filter (EKF). The EKF approach enables one to overcome the intrinsic limitations of standard straightforward nonmemory procedures such as the slope method, exponential curve fitting, and the backward inversion algorithm. Whereas those procedures are inherently not adaptable because independent inversions are performed for each return signal and neither the statistics of the signals nor a priori uncertainties (e.g., boundary calibrations) are taken into account, in the case of the Kalman filter the filter updates itself because it is weighted by the imbalance between the a priori estimates of the optical parameters (i.e., past inversions) and the new estimates based on a minimum-variance criterion, as long as there are different lidar returns. Calibration errors and initialization uncertainties can be assimilated also. The study begins with the formulation of the inversion problem and an appropriate atmospheric stochastic model. Based on extensive simulation and realistic conditions, it is shown that the EKF approach enables one to retrieve the optical parameters as time-range-dependent functions and hence to track the atmospheric evolution; the performance of this approach is limited only by the quality and availability of the a priori information and the accuracy of the atmospheric model used. The study ends with an encouraging practical inversion of a live scene measured at the Nd:YAG elastic-backscatter lidar station at our premises at the Polytechnic University of Catalonia, Barcelona.

A recursive solution for a fading memory filter derived from Kalman filter theory

NASA Technical Reports Server (NTRS)

Statman, J. I.

1986-01-01

A simple recursive solution for a class of fading memory tracking filters is presented. A fading memory filter provides estimates of filter states based on past measurements, similar to a traditional Kalman filter. Unlike a Kalman filter, an exponentially decaying weight is applied to older measurements, discounting their effect on present state estimates. It is shown that Kalman filters and fading memory filters are closely related solutions to a general least squares estimator problem. Closed form filter transfer functions are derived for a time invariant, steady state, fading memory filter. These can be applied in loop filter implementation of the Deep Space Network (DSN) Advanced Receiver carrier phase locked loop (PLL).

Multiuser Transmit Beamforming for Maximum Sum Capacity in Tactical Wireless Multicast Networks

DTIC Science & Technology

2006-08-01

commonly used extended Kalman filter . See [2, 5, 6] for recent tutorial overviews. In particle filtering , continuous distributions are approximated by...signals (using and developing associated particle filtering tools). Our work on these topics has been reported in seven (IEEE, SIAM) journal papers and...multidimensional scaling, tracking, intercept, particle filters . 16. PRICE CODE 17. SECURITY CLASSIFICATION OF REPORT 18. SECURITY CLASSIFICATION OF

Calibration of scintillation-light filters for neutron time-of-flight spectrometers at the National Ignition Facility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sayre, D. B., E-mail: sayre4@llnl.gov; Barbosa, F.; Caggiano, J. A.

Sixty-four neutral density filters constructed of metal plates with 88 apertures of varying diameter have been radiographed with a soft x-ray source and CCD camera at National Security Technologies, Livermore. An analysis of the radiographs fits the radial dependence of the apertures’ image intensities to sigmoid functions, which can describe the rapidly decreasing intensity towards the apertures’ edges. The fitted image intensities determine the relative attenuation value of each filter. Absolute attenuation values of several imaged filters, measured in situ during calibration experiments, normalize the relative quantities which are now used in analyses of neutron spectrometer data at the Nationalmore » Ignition Facility.« less

Calibration of scintillation-light filters for neutron time-of-flight spectrometers at the National Ignition Facility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sayre, D. B.; Barbosa, F.; Caggiano, J. A.

Sixty-four neutral density filters constructed of metal plates with 88 apertures of varying diameter have been radiographed with a soft x-ray source and CCD camera at National Security Technologies, Livermore. An analysis of the radiographs fits the radial dependence of the apertures’ image intensities to sigmoid functions, which can describe the rapidly decreasing intensity towards the apertures’ edges. Here, the fitted image intensities determine the relative attenuation value of each filter. Absolute attenuation values of several imaged filters, measured in situ during calibration experiments, normalize the relative quantities which are now used in analyses of neutron spectrometer data at themore » National Ignition Facility.« less

Calibration of scintillation-light filters for neutron time-of-flight spectrometers at the National Ignition Facility

DOE PAGES

Sayre, D. B.; Barbosa, F.; Caggiano, J. A.; ...

2016-07-26

Sixty-four neutral density filters constructed of metal plates with 88 apertures of varying diameter have been radiographed with a soft x-ray source and CCD camera at National Security Technologies, Livermore. An analysis of the radiographs fits the radial dependence of the apertures’ image intensities to sigmoid functions, which can describe the rapidly decreasing intensity towards the apertures’ edges. Here, the fitted image intensities determine the relative attenuation value of each filter. Absolute attenuation values of several imaged filters, measured in situ during calibration experiments, normalize the relative quantities which are now used in analyses of neutron spectrometer data at themore » National Ignition Facility.« less

Calibration of scintillation-light filters for neutron time-of-flight spectrometers at the National Ignition Facility.

PubMed

Sayre, D B; Barbosa, F; Caggiano, J A; DiPuccio, V N; Eckart, M J; Grim, G P; Hartouni, E P; Hatarik, R; Weber, F A

2016-11-01

Sixty-four neutral density filters constructed of metal plates with 88 apertures of varying diameter have been radiographed with a soft x-ray source and CCD camera at National Security Technologies, Livermore. An analysis of the radiographs fits the radial dependence of the apertures' image intensities to sigmoid functions, which can describe the rapidly decreasing intensity towards the apertures' edges. The fitted image intensities determine the relative attenuation value of each filter. Absolute attenuation values of several imaged filters, measured in situ during calibration experiments, normalize the relative quantities which are now used in analyses of neutron spectrometer data at the National Ignition Facility.

Application of a novel Kalman filter based block matching method to ultrasound images for hand tendon displacement estimation.

PubMed

Lai, Ting-Yu; Chen, Hsiao-I; Shih, Cho-Chiang; Kuo, Li-Chieh; Hsu, Hsiu-Yun; Huang, Chih-Chung

2016-01-01

Information about tendon displacement is important for allowing clinicians to not only quantify preoperative tendon injuries but also to identify any adhesive scaring between tendon and adjacent tissue. The Fisher-Tippett (FT) similarity measure has recently been shown to be more accurate than the Laplacian sum of absolute differences (SAD) and Gaussian sum of squared differences (SSD) similarity measures for tracking tendon displacement in ultrasound B-mode images. However, all of these similarity measures can easily be influenced by the quality of the ultrasound image, particularly its signal-to-noise ratio. Ultrasound images of injured hands are unfortunately often of poor quality due to the presence of adhesive scars. The present study investigated a novel Kalman-filter scheme for overcoming this problem. Three state-of-the-art tracking methods (FT, SAD, and SSD) were used to track the displacements of phantom and cadaver tendons, while FT was used to track human tendons. These three tracking methods were combined individually with the proposed Kalman-filter (K1) scheme and another Kalman-filter scheme used in a previous study to optimize the displacement trajectories of the phantom and cadaver tendons. The motion of the human extensor digitorum communis tendon was measured in the present study using the FT-K1 scheme. The experimental results indicated that SSD exhibited better accuracy in the phantom experiments, whereas FT exhibited better performance for tracking real tendon motion in the cadaver experiments. All three tracking methods were influenced by the signal-to-noise ratio of the images. On the other hand, the K1 scheme was able to optimize the tracking trajectory of displacement in all experiments, even from a location with a poor image quality. The human experimental data indicated that the normal tendons were displaced more than the injured tendons, and that the motion ability of the injured tendon was restored after appropriate rehabilitation sessions. The obtained results show the potential for applying the proposed FT-K1 method in clinical applications for evaluating the tendon injury level after metacarpal fractures and assessing the recovery of an injured tendon during rehabilitation.

Uncertainty quantification of seabed parameters for large data volumes along survey tracks with a tempered particle filter

NASA Astrophysics Data System (ADS)

Dettmer, J.; Quijano, J. E.; Dosso, S. E.; Holland, C. W.; Mandolesi, E.

2016-12-01

Geophysical seabed properties are important for the detection and classification of unexploded ordnance. However, current surveying methods such as vertical seismic profiling, coring, or inversion are of limited use when surveying large areas with high spatial sampling density. We consider surveys based on a source and receiver array towed by an autonomous vehicle which produce large volumes of seabed reflectivity data that contain unprecedented and detailed seabed information. The data are analyzed with a particle filter, which requires efficient reflection-coefficient computation, efficient inversion algorithms and efficient use of computer resources. The filter quantifies information content of multiple sequential data sets by considering results from previous data along the survey track to inform the importance sampling at the current point. Challenges arise from environmental changes along the track where the number of sediment layers and their properties change. This is addressed by a trans-dimensional model in the filter which allows layering complexity to change along a track. Efficiency is improved by likelihood tempering of various particle subsets and including exchange moves (parallel tempering). The filter is implemented on a hybrid computer that combines central processing units (CPUs) and graphics processing units (GPUs) to exploit three levels of parallelism: (1) fine-grained parallel computation of spherical reflection coefficients with a GPU implementation of Levin integration; (2) updating particles by concurrent CPU processes which exchange information using automatic load balancing (coarse grained parallelism); (3) overlapping CPU-GPU communication (a major bottleneck) with GPU computation by staggering CPU access to the multiple GPUs. The algorithm is applied to spherical reflection coefficients for data sets along a 14-km track on the Malta Plateau, Mediterranean Sea. We demonstrate substantial efficiency gains over previous methods. [This research was supported in part by the U.S. Dept of Defense, thought the Strategic Environmental Research and Development Program (SERDP).

The performance of a sampled data delay lock loop implemented with a Kalman loop filter

NASA Astrophysics Data System (ADS)

Eilts, H. S.

1980-01-01

The purpose of this study is to evaluate the steady-state and transient (lock-up) performance of a tracking loop implemented with a Kalman filter. Steady-state performance criteria are errors due to measurement noise (jitter) and Doppler errors due to motion of the tracking loop. Trade-offs exist between the two criteria such that increasing performance with respect to either one will cause performance decrease with respect to the other. It is shown that by carefully selecting filter parameters reasonable performance can be obtained for both criteria simultaneously. It is also shown that lock-up performance for the loop is acceptable when these parameters are used.

Comparison of a new inorganic membrane filter (Anopore) with a track-etched polycarbonate membrane filter (Nuclepore) for direct counting of bacteria.

PubMed Central

Jones, S E; Ditner, S A; Freeman, C; Whitaker, C J; Lock, M A

1989-01-01

Bacterial counts obtained by using a new Anopore inorganic membrane filter were 21 to 33% higher than those obtained by using a Nuclepore polycarbonate membrane filter. In addition, the inorganic filter had higher flow rates, permitting lower vacuum pressures to be used, while the intrinsically flat, rigid surface resulted in easier focusing and sharp definition of bacteria across the whole field of view. Images PMID:2655539

Indicator Expansion with Analysis Pipeline

DTIC Science & Technology

2015-01-13

INTERNAL FILTER trackInfectedHosts FILTER badTraffic SIP infectedHosts 1 DAY END INTERNAL FILTER 11 Step 3 watch where infected hosts go FILTER...nonWhiteListPostInfected SIP IN LIST infectedHosts DIP NOT IN LIST safePopularIPs.set END FILTER 12 Step 4 & 5: Count Hosts Per IP and Alert EVALUATION...CHECK THRESHOLD DISTINCT SIP > 50 TIME WINDOW 36 HOURS END CHECK END EVALUATION 13 Step 6: Report Expanded Indicators LIST CONFIGURATION secondLevelIPs

Tracking of Ball and Players in Beach Volleyball Videos

PubMed Central

Gomez, Gabriel; Herrera López, Patricia; Link, Daniel; Eskofier, Bjoern

2014-01-01

This paper presents methods for the determination of players' positions and contact time points by tracking the players and the ball in beach volleyball videos. Two player tracking methods are compared, a classical particle filter and a rigid grid integral histogram tracker. Due to mutual occlusion of the players and the camera perspective, results are best for the front players, with 74,6% and 82,6% of correctly tracked frames for the particle method and the integral histogram method, respectively. Results suggest an improved robustness against player confusion between different particle sets when tracking with a rigid grid approach. Faster processing and less player confusions make this method superior to the classical particle filter. Two different ball tracking methods are used that detect ball candidates from movement difference images using a background subtraction algorithm. Ball trajectories are estimated and interpolated from parabolic flight equations. The tracking accuracy of the ball is 54,2% for the trajectory growth method and 42,1% for the Hough line detection method. Tracking results of over 90% from the literature could not be confirmed. Ball contact frames were estimated from parabolic trajectory intersection, resulting in 48,9% of correctly estimated ball contact points. PMID:25426936

Neural network fusion capabilities for efficient implementation of tracking algorithms

NASA Astrophysics Data System (ADS)

Sundareshan, Malur K.; Amoozegar, Farid

1996-05-01

The ability to efficiently fuse information of different forms for facilitating intelligent decision-making is one of the major capabilities of trained multilayer neural networks that is being recognized int eh recent times. While development of innovative adaptive control algorithms for nonlinear dynamical plants which attempt to exploit these capabilities seems to be more popular, a corresponding development of nonlinear estimation algorithms using these approaches, particularly for application in target surveillance and guidance operations, has not received similar attention. In this paper we describe the capabilities and functionality of neural network algorithms for data fusion and implementation of nonlinear tracking filters. For a discussion of details and for serving as a vehicle for quantitative performance evaluations, the illustrative case of estimating the position and velocity of surveillance targets is considered. Efficient target tracking algorithms that can utilize data from a host of sensing modalities and are capable of reliably tracking even uncooperative targets executing fast and complex maneuvers are of interest in a number of applications. The primary motivation for employing neural networks in these applications comes form the efficiency with which more features extracted from different sensor measurements can be utilized as inputs for estimating target maneuvers. Such an approach results in an overall nonlinear tracking filter which has several advantages over the popular efforts at designing nonlinear estimation algorithms for tracking applications, the principle one being the reduction of mathematical and computational complexities. A system architecture that efficiently integrates the processing capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described in this paper.

Online Variational Bayesian Filtering-Based Mobile Target Tracking in Wireless Sensor Networks

PubMed Central

Zhou, Bingpeng; Chen, Qingchun; Li, Tiffany Jing; Xiao, Pei

2014-01-01

The received signal strength (RSS)-based online tracking for a mobile node in wireless sensor networks (WSNs) is investigated in this paper. Firstly, a multi-layer dynamic Bayesian network (MDBN) is introduced to characterize the target mobility with either directional or undirected movement. In particular, it is proposed to employ the Wishart distribution to approximate the time-varying RSS measurement precision's randomness due to the target movement. It is shown that the proposed MDBN offers a more general analysis model via incorporating the underlying statistical information of both the target movement and observations, which can be utilized to improve the online tracking capability by exploiting the Bayesian statistics. Secondly, based on the MDBN model, a mean-field variational Bayesian filtering (VBF) algorithm is developed to realize the online tracking of a mobile target in the presence of nonlinear observations and time-varying RSS precision, wherein the traditional Bayesian filtering scheme cannot be directly employed. Thirdly, a joint optimization between the real-time velocity and its prior expectation is proposed to enable online velocity tracking in the proposed online tacking scheme. Finally, the associated Bayesian Cramer–Rao Lower Bound (BCRLB) analysis and numerical simulations are conducted. Our analysis unveils that, by exploiting the potential state information via the general MDBN model, the proposed VBF algorithm provides a promising solution to the online tracking of a mobile node in WSNs. In addition, it is shown that the final tracking accuracy linearly scales with its expectation when the RSS measurement precision is time-varying. PMID:25393784

Sensor Fusion of Gaussian Mixtures for Ballistic Target Tracking in the Re-Entry Phase

PubMed Central

Lu, Kelin; Zhou, Rui

2016-01-01

A sensor fusion methodology for the Gaussian mixtures model is proposed for ballistic target tracking with unknown ballistic coefficients. To improve the estimation accuracy, a track-to-track fusion architecture is proposed to fuse tracks provided by the local interacting multiple model filters. During the fusion process, the duplicate information is removed by considering the first order redundant information between the local tracks. With extensive simulations, we show that the proposed algorithm improves the tracking accuracy in ballistic target tracking in the re-entry phase applications. PMID:27537883

Sensor Fusion of Gaussian Mixtures for Ballistic Target Tracking in the Re-Entry Phase.

PubMed

Lu, Kelin; Zhou, Rui

2016-08-15

A sensor fusion methodology for the Gaussian mixtures model is proposed for ballistic target tracking with unknown ballistic coefficients. To improve the estimation accuracy, a track-to-track fusion architecture is proposed to fuse tracks provided by the local interacting multiple model filters. During the fusion process, the duplicate information is removed by considering the first order redundant information between the local tracks. With extensive simulations, we show that the proposed algorithm improves the tracking accuracy in ballistic target tracking in the re-entry phase applications.

Performance Evaluation Within CASE_ATTI of MHT and JVC Association Algorithms for COMDAT TD

DTIC Science & Technology

2007-05-01

les résultats du travail effectué dans le cadre de l’analyse de sensibilité des algorithmes uti- lisés dans COMDAT, comparativement à ceux...is also very important in tracking system. Neverthe- less, tracking performance with even the best designed filter may become very degraded in the...for completeness. 2.2 IMM Some practical model of target motion is assumed for the design of the Kalman filter. This target kinematics model is

Evaluating Stream Filtering for Entity Profile Updates for TREC 2013 (KBA Track Overview)

DTIC Science & Technology

2013-11-01

Paul McCartney, who confirmed in a BBC interview that he might start a new band called “ Beatles II” or “ The Beatles -- The Next Generation.” The ...Gaithersburg, MD ian.soboroff@nist.gov   Abstract   The Knowledge Base Acceleration (KBA) track in TREC 2013 expanded the entity-centric filtering...entity profile in a predefined list of entities. We doubled the size of the KBA streamcorpus to twelve thousand contiguous hours and a billion

Toward a Real-Time Measurement-Based System for Estimation of Helicopter Engine Degradation Due to Compressor Erosion

NASA Technical Reports Server (NTRS)

Litt, Jonathan S.; Simo, Donald L.

2007-01-01

This paper presents a preliminary demonstration of an automated health assessment tool, capable of real-time on-board operation using existing engine control hardware. The tool allows operators to discern how rapidly individual turboshaft engines are degrading. As the compressor erodes, performance is lost, and with it the ability to generate power. Thus, such a tool would provide an instant assessment of the engine s fitness to perform a mission, and would help to pinpoint any abnormal wear or performance anomalies before they became serious, thereby decreasing uncertainty and enabling improved maintenance scheduling. The research described in the paper utilized test stand data from a T700-GE-401 turboshaft engine that underwent sand-ingestion testing to scale a model-based compressor efficiency degradation estimation algorithm. This algorithm was then applied to real-time Health Usage and Monitoring System (HUMS) data from a T700-GE-701C to track compressor efficiency on-line. The approach uses an optimal estimator called a Kalman filter. The filter is designed to estimate the compressor efficiency using only data from the engine s sensors as input.

Model-based extended quaternion Kalman filter to inertial orientation tracking of arbitrary kinematic chains.

PubMed

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.

A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity.

PubMed

Rakhimberdiev, Eldar; Winkler, David W; Bridge, Eli; Seavy, Nathaniel E; Sheldon, Daniel; Piersma, Theunis; Saveliev, Anatoly

2015-01-01

Solar archival tags (henceforth called geolocators) are tracking devices deployed on animals to reconstruct their long-distance movements on the basis of locations inferred post hoc with reference to the geographical and seasonal variations in the timing and speeds of sunrise and sunset. The increased use of geolocators has created a need for analytical tools to produce accurate and objective estimates of migration routes that are explicit in their uncertainty about the position estimates. We developed a hidden Markov chain model for the analysis of geolocator data. This model estimates tracks for animals with complex migratory behaviour by combining: (1) a shading-insensitive, template-fit physical model, (2) an uncorrelated random walk movement model that includes migratory and sedentary behavioural states, and (3) spatially explicit behavioural masks. The model is implemented in a specially developed open source R package FLightR. We used the particle filter (PF) algorithm to provide relatively fast model posterior computation. We illustrate our modelling approach with analysis of simulated data for stationary tags and of real tracks of both a tree swallow Tachycineta bicolor migrating along the east and a golden-crowned sparrow Zonotrichia atricapilla migrating along the west coast of North America. We provide a model that increases accuracy in analyses of noisy data and movements of animals with complicated migration behaviour. It provides posterior distributions for the positions of animals, their behavioural states (e.g., migrating or sedentary), and distance and direction of movement. Our approach allows biologists to estimate locations of animals with complex migratory behaviour based on raw light data. This model advances the current methods for estimating migration tracks from solar geolocation, and will benefit a fast-growing number of tracking studies with this technology.

Joint compensation scheme of polarization crosstalk, intersymbol interference, frequency offset, and phase noise based on cascaded Kalman filter

NASA Astrophysics Data System (ADS)

Zhang, Qun; Yang, Yanfu; Xiang, Qian; Zhou, Zhongqing; Yao, Yong

2018-02-01

A joint compensation scheme based on cascaded Kalman filter is proposed, which can implement polarization tracking, channel equalization, frequency offset, and phase noise compensation simultaneously. The experimental results show that the proposed algorithm can not only compensate multiple channel impairments simultaneously but also improve the polarization tracking capacity and accelerate the convergence speed. The scheme has up to eight times faster convergence speed compared with radius-directed equalizer (RDE) + Max-FFT (maximum fast Fourier transform) + BPS (blind phase search) and can track up polarization rotation 60 times and 15 times faster than that of RDE + Max-FFT + BPS and CMMA (cascaded multimodulus algorithm) + Max-FFT + BPS, respectively.

Tracks detection from high-orbit space objects

NASA Astrophysics Data System (ADS)

Shumilov, Yu. P.; Vygon, V. G.; Grishin, E. A.; Konoplev, A. O.; Semichev, O. P.; Shargorodskii, V. D.

2017-05-01

The paper presents studies results of a complex algorithm for the detection of highly orbital space objects. Before the implementation of the algorithm, a series of frames with weak tracks of space objects, which can be discrete, is recorded. The algorithm includes pre-processing, classical for astronomy, consistent filtering of each frame and its threshold processing, shear transformation, median filtering of the transformed series of frames, repeated threshold processing and detection decision making. Modeling of space objects weak tracks on of the night starry sky real frames obtained in the regime of a stationary telescope was carried out. It is shown that the permeability of an optoelectronic device has increased by almost 2m.

Micromachined Radio Frequency (RF) Switches and Tunable Capacitors for Higher Performance Secure Communications Systems

DTIC Science & Technology

2003-04-01

range filters implemented with traditional semiconductor varactor diodes can require complex series-parallel circuit constructions to achieve sufficient...filter slice of the AIU and the varactor array modules are shown in Fig. 6.2. The complexity of the varactor array is clearly apparent. Further, it is...38 Fig. 6.2: Schematic of F-22 AIU UHF tracking filter, 2-pole filter, and varactor diode assembly

Sequential Bayesian geoacoustic inversion for mobile and compact source-receiver configuration.

PubMed

Carrière, Olivier; Hermand, Jean-Pierre

2012-04-01

Geoacoustic characterization of wide areas through inversion requires easily deployable configurations including free-drifting platforms, underwater gliders and autonomous vehicles, typically performing repeated transmissions during their course. In this paper, the inverse problem is formulated as sequential Bayesian filtering to take advantage of repeated transmission measurements. Nonlinear Kalman filters implement a random-walk model for geometry and environment and an acoustic propagation code in the measurement model. Data from MREA/BP07 sea trials are tested consisting of multitone and frequency-modulated signals (bands: 0.25-0.8 and 0.8-1.6 kHz) received on a shallow vertical array of four hydrophones 5-m spaced drifting over 0.7-1.6 km range. Space- and time-coherent processing are applied to the respective signal types. Kalman filter outputs are compared to a sequence of global optimizations performed independently on each received signal. For both signal types, the sequential approach is more accurate but also more efficient. Due to frequency diversity, the processing of modulated signals produces a more stable tracking. Although an extended Kalman filter provides comparable estimates of the tracked parameters, the ensemble Kalman filter is necessary to properly assess uncertainty. In spite of mild range dependence and simplified bottom model, all tracked geoacoustic parameters are consistent with high-resolution seismic profiling, core logging P-wave velocity, and previous inversion results with fixed geometries.

Beam energy tracking system on Optima XEx high energy ion implanter

DOE Office of Scientific and Technical Information (OSTI.GOV)

David, Jonathan; Satoh, Shu; Wu Xiangyang

2012-11-06

The Axcelis Optima XEx high energy implanter is an RF linac-based implanter with 12 RF resonators for beam acceleration. Even though each acceleration field is an alternating, sinusoidal RF field, the well known phase-focusing principle produces a beam with a sharp quasi-monoenergetic energy spectrum. A magnetic energy filter after the linac further attenuates the low energy continuum in the energy spectrum often associated with RF acceleration. The final beam energy is a function of the phase and amplitude of the 12 resonators in the linac. When tuning a beam, the magnetic energy filter is set to the desired energy, andmore » each linac parameter is tuned to maximize the transmission through the filter. Once a beam is set up, all the parameters are stored in a recipe, which can be easily tuned and has proven to be quite repeatable. The magnetic field setting of the energy filter selects the beam energy from the RF Linac accelerator, and in-situ verification of beam energy in addition to the magnetic energy filter setting has long been desired. An independent energy tracking system was developed for this purpose, using the existing electrostatic beam scanner as a deflector to construct an in-situ electrostatic energy analyzer. This paper will describe the system and performance of the beam energy tracking system.« less

A myocontrolled neuroprosthesis integrated with a passive exoskeleton to support upper limb activities.

PubMed

Ambrosini, Emilia; Ferrante, Simona; Schauer, Thomas; Klauer, Christian; Gaffuri, Marina; Ferrigno, Giancarlo; Pedrocchi, Alessandra

2014-04-01

This work aimed at designing a myocontrolled arm neuroprosthesis for both assistive and rehabilitative purposes. The performance of an adaptive linear prediction filter and a high-pass filter to estimate the volitional EMG was evaluated on healthy subjects (N=10) and neurological patients (N=8) during dynamic hybrid biceps contractions. A significant effect of filter (p=0.017 for healthy; p<0.001 for patients) was obtained. The post hoc analysis revealed that for both groups only the adaptive filter was able to reliably detect the presence of a small volitional contribution. An on/off non-linear controller integrated with an exoskeleton for weight support was developed. The controller allowed the patient to activate/deactivate the stimulation intensity based on the residual EMG estimated by the adaptive filter. Two healthy subjects and 3 people with Spinal Cord Injury were asked to flex the elbow while tracking a trapezoidal target with and without myocontrolled-NMES support. Both healthy subjects and patients easily understood how to use the controller in a single session. Two patients reduced their tracking error by more than 60% with NMES support, while the last patient obtained a tracking error always comparable to the healthy subjects performance (<4°). This study proposes a reliable and feasible solution to combine NMES with voluntary effort. Copyright © 2014 Elsevier Ltd. All rights reserved.

Combining Particle Filters and Consistency-Based Approaches for Monitoring and Diagnosis of Stochastic Hybrid Systems

NASA Technical Reports Server (NTRS)

Narasimhan, Sriram; Dearden, Richard; Benazera, Emmanuel

2004-01-01

Fault detection and isolation are critical tasks to ensure correct operation of systems. When we consider stochastic hybrid systems, diagnosis algorithms need to track both the discrete mode and the continuous state of the system in the presence of noise. Deterministic techniques like Livingstone cannot deal with the stochasticity in the system and models. Conversely Bayesian belief update techniques such as particle filters may require many computational resources to get a good approximation of the true belief state. In this paper we propose a fault detection and isolation architecture for stochastic hybrid systems that combines look-ahead Rao-Blackwellized Particle Filters (RBPF) with the Livingstone 3 (L3) diagnosis engine. In this approach RBPF is used to track the nominal behavior, a novel n-step prediction scheme is used for fault detection and L3 is used to generate a set of candidates that are consistent with the discrepant observations which then continue to be tracked by the RBPF scheme.

An All-Digital Fast Tracking Switching Converter with a Programmable Order Loop Controller for Envelope Tracking RF Power Amplifiers

PubMed Central

Anabtawi, Nijad; Ferzli, Rony; Harmanani, Haidar M.

2017-01-01

This paper presents a step down, switched mode power converter for use in multi-standard envelope tracking radio frequency power amplifiers (RFPA). The converter is based on a programmable order sigma delta modulator that can be configured to operate with either 1st, 2nd, 3rd or 4th order loop filters, eliminating the need for a bulky passive output filter. Output ripple, sideband noise and spectral emission requirements of different wireless standards can be met by configuring the modulator’s filter order and converter’s sampling frequency. The proposed converter is entirely digital and is implemented in 14nm bulk CMOS process for post layout verification. For an input voltage of 3.3V, the converter’s output can be regulated to any voltage level from 0.5V to 2.5V, at a nominal switching frequency of 150MHz. It achieves a maximum efficiency of 94% at 1.5 W output power. PMID:28919657

Real-Time Robust Tracking for Motion Blur and Fast Motion via Correlation Filters.

PubMed

Xu, Lingyun; Luo, Haibo; Hui, Bin; Chang, Zheng

2016-09-07

Visual tracking has extensive applications in intelligent monitoring and guidance systems. Among state-of-the-art tracking algorithms, Correlation Filter methods perform favorably in robustness, accuracy and speed. However, it also has shortcomings when dealing with pervasive target scale variation, motion blur and fast motion. In this paper we proposed a new real-time robust scheme based on Kernelized Correlation Filter (KCF) to significantly improve performance on motion blur and fast motion. By fusing KCF and STC trackers, our algorithm also solve the estimation of scale variation in many scenarios. We theoretically analyze the problem for CFs towards motions and utilize the point sharpness function of the target patch to evaluate the motion state of target. Then we set up an efficient scheme to handle the motion and scale variation without much time consuming. Our algorithm preserves the properties of KCF besides the ability to handle special scenarios. In the end extensive experimental results on benchmark of VOT datasets show our algorithm performs advantageously competed with the top-rank trackers.

Design, implementation and flight testing of PIF autopilots for general aviation aircraft

NASA Technical Reports Server (NTRS)

Broussard, J. R.

1983-01-01

The designs of Proportional-Integrated-Filter (PIF) auto-pilots for a General Aviation (NAVION) aircraft are presented. The PIF autopilot uses the sampled-data regulator and command generator tracking to determine roll select, pitch select, heading select, altitude select and localizer/glideslope capture and hold autopilot modes. The PIF control law uses typical General Aviation sensors for state feedback, command error integration for command tracking, digital complementary filtering and analog prefiltering for sensor noise suppression, a control filter for computation delay accommodation and the incremental form to eliminate trim values in implementation. Theoretical developments described in detail, were needed to combine the sampled-data regulator with command generator tracking for use as a digital flight control system. The digital PIF autopilots are evaluated using closed-loop eigenvalues and linear simulations. The implementation of the PIF autopilots in a digital flight computer using a high order language (FORTRAN) is briefly described. The successful flight test results for each PIF autopilot mode is presented.

A Sensor Fusion Method for Tracking Vertical Velocity and Height Based on Inertial and Barometric Altimeter Measurements

PubMed Central

Sabatini, Angelo Maria; Genovese, Vincenzo

2014-01-01

A sensor fusion method was developed for vertical channel stabilization by fusing inertial measurements from an Inertial Measurement Unit (IMU) and pressure altitude measurements from a barometric altimeter integrated in the same device (baro-IMU). An Extended Kalman Filter (EKF) estimated the quaternion from the sensor frame to the navigation frame; the sensed specific force was rotated into the navigation frame and compensated for gravity, yielding the vertical linear acceleration; finally, a complementary filter driven by the vertical linear acceleration and the measured pressure altitude produced estimates of height and vertical velocity. A method was also developed to condition the measured pressure altitude using a whitening filter, which helped to remove the short-term correlation due to environment-dependent pressure changes from raw pressure altitude. The sensor fusion method was implemented to work on-line using data from a wireless baro-IMU and tested for the capability of tracking low-frequency small-amplitude vertical human-like motions that can be critical for stand-alone inertial sensor measurements. Validation tests were performed in different experimental conditions, namely no motion, free-fall motion, forced circular motion and squatting. Accurate on-line tracking of height and vertical velocity was achieved, giving confidence to the use of the sensor fusion method for tracking typical vertical human motions: velocity Root Mean Square Error (RMSE) was in the range 0.04–0.24 m/s; height RMSE was in the range 5–68 cm, with statistically significant performance gains when the whitening filter was used by the sensor fusion method to track relatively high-frequency vertical motions. PMID:25061835

Surface Fitting Filtering of LIDAR Point Cloud with Waveform Information

NASA Astrophysics Data System (ADS)

Xing, S.; Li, P.; Xu, Q.; Wang, D.; Li, P.

2017-09-01

Full-waveform LiDAR is an active technology of photogrammetry and remote sensing. It provides more detailed information about objects along the path of a laser pulse than discrete-return topographic LiDAR. The point cloud and waveform information with high quality can be obtained by waveform decomposition, which could make contributions to accurate filtering. The surface fitting filtering method with waveform information is proposed to present such advantage. Firstly, discrete point cloud and waveform parameters are resolved by global convergent Levenberg Marquardt decomposition. Secondly, the ground seed points are selected, of which the abnormal ones are detected by waveform parameters and robust estimation. Thirdly, the terrain surface is fitted and the height difference threshold is determined in consideration of window size and mean square error. Finally, the points are classified gradually with the rising of window size. The filtering process is finished until window size is larger than threshold. The waveform data in urban, farmland and mountain areas from "WATER (Watershed Allied Telemetry Experimental Research)" are selected for experiments. Results prove that compared with traditional method, the accuracy of point cloud filtering is further improved and the proposed method has highly practical value.

Nonstationary EO/IR Clutter Suppression and Dim Object Tracking

DTIC Science & Technology

2010-01-01

Brown, A., and Brown, J., Enhanced Algorithms for EO /IR Electronic Stabilization, Clutter Suppression, and Track - Before - Detect for Multiple Low...estimation-suppression and nonlinear filtering-based multiple-object track - before - detect . These algorithms are suitable for integration into...In such cases, it is imperative to develop efficient real or near-real time tracking before detection methods. This paper continues the work started

Comparison of performance of three different types of respiratory protection devices.

PubMed

Lawrence, Robert B; Duling, Matthew G; Calvert, Catherine A; Coffey, Christopher C

2006-09-01

Respiratory protection is offered to American workers in a variety of ways to guard against potential inhalation hazards. Two of the most common ways are elastomeric N95 respirators and N95 filtering-facepiece respirators. Some in the health care industry feel that surgical masks provide an acceptable level of protection in certain situations against particular hazards. This study compared the performance of these types of respiratory protection during a simulated workplace test that measured both filter penetration and face-seal leakage. A panel of 25 test subjects with varying face sizes tested 15 models of elastomeric N95 respirators, 15 models of N95 filtering-facepiece respirators, and 6 models of surgical masks. Simulated workplace testing was conducted using a TSI PORTACOUNT Plus model 8020, and consisted of a series of seven exercises. Six simulated workplace tests were performed with redonning of the respirator/mask occurring between each test. The results of these tests produced a simulated workplace protection factor (SWPF). The geometric mean (GM) and the 5th percentile values of the SWPFs were computed by category of respiratory protection using the six overall SWPF values. The level of protection provided by each of the three respiratory protection types was compared. The GM and 5th percentile SWPF values without fit testing were used for the comparison, as surgical masks were not intended to be fit tested. The GM values were 36 for elastomeric N95 respirators, 21 for N95 filtering-facepiece respirators, and 3 for surgical masks. An analysis of variance demonstrated a statistically significant difference between all three. Elastomeric N95 respirators had the highest 5th percentile SWPF of 7. N95 filtering-facepiece respirators and surgical masks had 5th percentile SWPFs of 3 and 1, respectively. A Fisher Exact Test revealed that the 5th percentile SWPFs for all three types of respiratory protection were statistically different. In addition, both qualitative (Bitrex and saccharin) and quantitative (N95-Companion) fit testing were performed on the N95 filtering- and elastomeric-facepiece respirators. It was found that passing a fit test generally improves the protection afforded the wearer. Passing the Bitrex fit test resulted in 5th percentile SWPFs of 11.1 and 7.9 for elastomeric and filtering-facepiece respirators, respectively. After passing the saccharin tests, the elastomeric respirators provided a 5th percentile of 11.7, and the filtering-facepiece respirators provided a 5th percentile of 11.0. The 5th percentiles after passing the N95-Companion were 13.0 for the elastomeric respirators and 20.5 for the filtering-facepiece respirators. The data supports fit testing as an essential element of a complete respiratory protection program.

GEO Optical Data Association with Concurrent Metric and Photometric Information

NASA Astrophysics Data System (ADS)

Dao, P.; Monet, D.

Data association in a congested area of the GEO belt with occasional visits by non-resident objects can be treated as a Multi-Target-Tracking (MTT) problem. For a stationary sensor surveilling the GEO belt, geosynchronous and near GEO objects are not completely motionless in the earth-fixed frame and can be observed as moving targets. In some clusters, metric or positional information is insufficiently accurate or up-to-date to associate the measurements. In the presence of measurements with uncertain origin, star tracks (residuals) and other sensor artifacts, heuristic techniques based on hard decision assignment do not perform adequately. In the MMT community, Bar-Shalom [2009 Bar-Shalom] was first in introducing the use of measurements to update the state of the target of interest in the tracking filter, e.g. Kalman filter. Following Bar-Shalom’s idea, we use the Probabilistic Data Association Filter (PDAF) but to make use of all information obtainable in the measurement of three-axis-stabilized GEO satellites, we combine photometric with metric measurements to update the filter. Therefore, our technique Concurrent Spatio- Temporal and Brightness (COSTB) has the stand-alone ability of associating a track with its identity –for resident objects. That is possible because the light curve of a stabilized GEO satellite changes minimally from night to night. We exercised COSTB on camera cadence data to associate measurements, correct mistags and detect non-residents in a simulated near real time cadence. Data on GEO clusters were used.

PMHT Approach for Multi-Target Multi-Sensor Sonar Tracking in Clutter.

PubMed

Li, Xiaohua; Li, Yaan; Yu, Jing; Chen, Xiao; Dai, Miao

2015-11-06

Multi-sensor sonar tracking has many advantages, such as the potential to reduce the overall measurement uncertainty and the possibility to hide the receiver. However, the use of multi-target multi-sensor sonar tracking is challenging because of the complexity of the underwater environment, especially the low target detection probability and extremely large number of false alarms caused by reverberation. In this work, to solve the problem of multi-target multi-sensor sonar tracking in the presence of clutter, a novel probabilistic multi-hypothesis tracker (PMHT) approach based on the extended Kalman filter (EKF) and unscented Kalman filter (UKF) is proposed. The PMHT can efficiently handle the unknown measurements-to-targets and measurements-to-transmitters data association ambiguity. The EKF and UKF are used to deal with the high degree of nonlinearity in the measurement model. The simulation results show that the proposed algorithm can improve the target tracking performance in a cluttered environment greatly, and its computational load is low.

Comparison of two quantitative fit-test methods using N95 filtering facepiece respirators.

PubMed

Sietsema, Margaret; Brosseau, Lisa M

2016-08-01

Current regulations require annual fit testing before an employee can wear a respirator during work activities. The goal of this research is to determine whether respirator fit measured with two TSI Portacount instruments simultaneously sampling ambient particle concentrations inside and outside of the respirator facepiece is similar to fit measured during an ambient aerosol condensation nuclei counter quantitative fit test. Sixteen subjects (ten female; six male) were recruited for a range of facial sizes. Each subject donned an N95 filtering facepiece respirator, completed two fit tests in random order (ambient aerosol condensation nuclei counter quantitative fit test and two-instrument real-time fit test) without removing or adjusting the respirator between tests. Fit tests were compared using Spearman's rank correlation coefficients. The real-time two-instrument method fit factors were similar to those measured with the single-instrument quantitative fit test. The first four exercises were highly correlated (r > 0.7) between the two protocols. Respirator fit was altered during the talking or grimace exercise, both of which involve facial movements that could dislodge the facepiece. Our analyses suggest that the new real-time two-instrument methodology can be used in future studies to evaluate fit before and during work activities.

Improvements to Filter Debris Analysis in Aviation Propulsion Systems

DTIC Science & Technology

2012-12-01

been applied to the external scavenge filter fitted to the T56 -A-14 and T-56-A-15 engines (powering the P3C and C130-H aircraft respectively...15 3. AUTOMATED ANALYSIS OF T56 ENGINE OIL FILTER DEBRIS...................... 15 3.1 Historical Practices...for T56 .................................................................................... 15 3.2 FilterCHECK 290

Multiple object tracking with non-unique data-to-object association via generalized hypothesis testing. [tracking several aircraft near each other or ships at sea

NASA Technical Reports Server (NTRS)

Porter, D. W.; Lefler, R. M.

1979-01-01

A generalized hypothesis testing approach is applied to the problem of tracking several objects where several different associations of data with objects are possible. Such problems occur, for instance, when attempting to distinctly track several aircraft maneuvering near each other or when tracking ships at sea. Conceptually, the problem is solved by first, associating data with objects in a statistically reasonable fashion and then, tracking with a bank of Kalman filters. The objects are assumed to have motion characterized by a fixed but unknown deterministic portion plus a random process portion modeled by a shaping filter. For example, the object might be assumed to have a mean straight line path about which it maneuvers in a random manner. Several hypothesized associations of data with objects are possible because of ambiguity as to which object the data comes from, false alarm/detection errors, and possible uncertainty in the number of objects being tracked. The statistical likelihood function is computed for each possible hypothesized association of data with objects. Then the generalized likelihood is computed by maximizing the likelihood over parameters that define the deterministic motion of the object.

Space-based IR tracking bias removal using background star observations

NASA Astrophysics Data System (ADS)

Clemons, T. M., III; Chang, K. C.

2009-05-01

This paper provides the results of a proposed methodology for removing sensor bias from a space-based infrared (IR) tracking system through the use of stars detected in the background field of the tracking sensor. The tracking system consists of two satellites flying in a lead-follower formation tracking a ballistic target. Each satellite is equipped with a narrow-view IR sensor that provides azimuth and elevation to the target. The tracking problem is made more difficult due to a constant, non-varying or slowly varying bias error present in each sensor's line of sight measurements. As known stars are detected during the target tracking process, the instantaneous sensor pointing error can be calculated as the difference between star detection reading and the known position of the star. The system then utilizes a separate bias filter to estimate the bias value based on these detections and correct the target line of sight measurements to improve the target state vector. The target state vector is estimated through a Linearized Kalman Filter (LKF) for the highly non-linear problem of tracking a ballistic missile. Scenarios are created using Satellite Toolkit(C) for trajectories with associated sensor observations. Mean Square Error results are given for tracking during the period when the target is in view of the satellite IR sensors. The results of this research provide a potential solution to bias correction while simultaneously tracking a target.

Super-resolution pupil filtering for visual performance enhancement using adaptive optics

NASA Astrophysics Data System (ADS)

Zhao, Lina; Dai, Yun; Zhao, Junlei; Zhou, Xiaojun

2018-05-01

Ocular aberration correction can significantly improve visual function of the human eye. However, even under ideal aberration correction conditions, pupil diffraction restricts the resolution of retinal images. Pupil filtering is a simple super-resolution (SR) method that can overcome this diffraction barrier. In this study, a 145-element piezoelectric deformable mirror was used as a pupil phase filter because of its programmability and high fitting accuracy. Continuous phase-only filters were designed based on Zernike polynomial series and fitted through closed-loop adaptive optics. SR results were validated using double-pass point spread function images. Contrast sensitivity was further assessed to verify the SR effect on visual function. An F-test was conducted for nested models to statistically compare different CSFs. These results indicated CSFs for the proposed SR filter were significantly higher than the diffraction correction (p < 0.05). As such, the proposed filter design could provide useful guidance for supernormal vision optical correction of the human eye.

A novel track-before-detect algorithm based on optimal nonlinear filtering for detecting and tracking infrared dim target

NASA Astrophysics Data System (ADS)

Tian, Yuexin; Gao, Kun; Liu, Ying; Han, Lu

2015-08-01

Aiming at the nonlinear and non-Gaussian features of the real infrared scenes, an optimal nonlinear filtering based algorithm for the infrared dim target tracking-before-detecting application is proposed. It uses the nonlinear theory to construct the state and observation models and uses the spectral separation scheme based Wiener chaos expansion method to resolve the stochastic differential equation of the constructed models. In order to improve computation efficiency, the most time-consuming operations independent of observation data are processed on the fore observation stage. The other observation data related rapid computations are implemented subsequently. Simulation results show that the algorithm possesses excellent detection performance and is more suitable for real-time processing.

Analysis of signal to noise enhancement using a highly selective modulation tracking filter

NASA Technical Reports Server (NTRS)

Haden, C. R.; Alworth, C. W.

1972-01-01

Experiments are reported which utilize photodielectric effects in semiconductor loaded superconducting resonant circuits for suppressing noise in RF communication systems. The superconducting tunable cavity acts as a narrow band tracking filter for detecting conventional RF signals. Analytical techniques were developed which lead to prediction of signal-to-noise improvements. Progress is reported in optimization of the experimental variables. These include improved Q, new semiconductors, improved optics, and simplification of the electronics. Information bearing signals were passed through the system, and noise was introduced into the computer model.

Two new methods to increase the contrast of track-etch neutron radiographs

NASA Technical Reports Server (NTRS)

Morley, J.

1971-01-01

Methods for increasing the (optical density span) of radiographs were evaluated. In one method, fluorescent dye was deposited in the tracks of the radiograph. The radiograph was then examined under ultraviolet light. The second method was a crossed Polaroid filter technique. The radiograph was placed between the filters and then illuminated with a diffuse white-light source. An increase in the optical density span from .10 to .37 was obtained with the dye method. With the Polaroid method, the increase obtained was from .10 to 2.4.

Control, Filtering and Prediction for Phased Arrays in Directed Energy Systems

DTIC Science & Technology

2016-04-30

adaptive optics. 15. SUBJECT TERMS control, filtering, prediction, system identification, adaptive optics, laser beam pointing, target tracking, phase... laser beam control; furthermore, wavefront sensors are plagued by the difficulty of maintaining the required alignment and focusing in dynamic mission...developed new methods for filtering, prediction and system identification in adaptive optics for high energy laser systems including phased arrays. The

Inversion of atmospheric optical parameters from elastic-backscatter lidar returns using a Kalman filter

NASA Astrophysics Data System (ADS)

Rocadenbosch, Francesc; Comeron, Adolfo; Vazquez, Gregori; Rodriguez-Gomez, Alejandro; Soriano, Cecilia; Baldasano, Jose M.

1998-12-01

Up to now, retrieval of the atmospheric extinction and backscatter has mainly relied on standard straightforward non-memory procedures such as slope-method, exponential- curve fitting and Klett's method. Yet, their performance becomes ultimately limited by the inherent lack of adaptability as they only work with present returns and neither past estimations, nor the statistics of the signals or a prior uncertainties are taken into account. In this work, a first inversion of the backscatter and extinction- to-backscatter ratio from pulsed elastic-backscatter lidar returns is tackled by means of an extended Kalman filter (EKF), which overcomes these limitations. Thus, as long as different return signals income,the filter updates itself weighted by the unbalance between the a priori estimates of the optical parameters and the new ones based on a minimum variance criterion. Calibration errors or initialization uncertainties can be assimilated also. The study begins with the formulation of the inversion problem and an appropriate stochastic model. Based on extensive simulation and realistic conditions, it is shown that the EKF approach enables to retrieve the sought-after optical parameters as time-range-dependent functions and hence, to track the atmospheric evolution, its performance being only limited by the quality and availability of the 'a priori' information and the accuracy of the atmospheric model assumed. The study ends with an encouraging practical inversion of a live-scene measured with the Nd:YAG elastic-backscatter lidar station at our premises in Barcelona.

Low level determination of (226)Ra in water using a micro-precipitate track method for large-scale environmental monitoring.

PubMed

Taheri, M; Sohrabi, M; Jaleh, B; Hosseini, T; Montazer Rahmati, M M

2009-12-01

In the present paper a method has been developed for the determination of (226)Ra in water by the detection, using a solid-state nuclear track detector (SSNTD), of alpha particles from (226)Ra in equilibrium with (222)Rn in micro-precipitates collected on a filter. The micro-precipitates were prepared from environmental water samples by collection of radium with lead as Pb/RaSO(4). Several factors affect the (226)Ra precipitation on the filter and its recovery, in particular the filter pore size. Therefore in this experiment Whatman #42 and Millipore filters with different pore sizes were used. Using a 0.45 microm Millipore filter, the recovery efficiency was increased up to 96%, and the alpha self-absorption and scattering decreased remarkably. For efficient detection of alphas from (226)Ra/(222)Rn in equilibrium, three types of SSNTD were used-polycarbonate (PC) electrochemically etched (ECE), CR-39 and LR-115 chemically etched (CE). By preparing a standard micro-precipitate on a filter with known (226)Ra/(222)Rn characteristics, the calibration response of each detector and its minimum detection limit (MDL) were determined.

Log-polar mapping-based scale space tracking with adaptive target response

NASA Astrophysics Data System (ADS)

Li, Dongdong; Wen, Gongjian; Kuai, Yangliu; Zhang, Ximing

2017-05-01

Correlation filter-based tracking has exhibited impressive robustness and accuracy in recent years. Standard correlation filter-based trackers are restricted to translation estimation and equipped with fixed target response. These trackers produce an inferior performance when encountered with a significant scale variation or appearance change. We propose a log-polar mapping-based scale space tracker with an adaptive target response. This tracker transforms the scale variation of the target in the Cartesian space into a shift along the logarithmic axis in the log-polar space. A one-dimensional scale correlation filter is learned online to estimate the shift along the logarithmic axis. With the log-polar representation, scale estimation is achieved accurately without a multiresolution pyramid. To achieve an adaptive target response, a variance of the Gaussian function is computed from the response map and updated online with a learning rate parameter. Our log-polar mapping-based scale correlation filter and adaptive target response can be combined with any correlation filter-based trackers. In addition, the scale correlation filter can be extended to a two-dimensional correlation filter to achieve joint estimation of the scale variation and in-plane rotation. Experiments performed on an OTB50 benchmark demonstrate that our tracker achieves superior performance against state-of-the-art trackers.

Radar tracking with an interacting multiple model and probabilistic data association filter for civil aviation applications.

PubMed

Jan, Shau-Shiun; Kao, Yu-Chun

2013-05-17

The current trend of the civil aviation technology is to modernize the legacy air traffic control (ATC) system that is mainly supported by many ground based navigation aids to be the new air traffic management (ATM) system that is enabled by global positioning system (GPS) technology. Due to the low receiving power of GPS signal, it is a major concern to aviation authorities that the operation of the ATM system might experience service interruption when the GPS signal is jammed by either intentional or unintentional radio-frequency interference. To maintain the normal operation of the ATM system during the period of GPS outage, the use of the current radar system is proposed in this paper. However, the tracking performance of the current radar system could not meet the required performance of the ATM system, and an enhanced tracking algorithm, the interacting multiple model and probabilistic data association filter (IMMPDAF), is therefore developed to support the navigation and surveillance services of the ATM system. The conventional radar tracking algorithm, the nearest neighbor Kalman filter (NNKF), is used as the baseline to evaluate the proposed radar tracking algorithm, and the real flight data is used to validate the IMMPDAF algorithm. As shown in the results, the proposed IMMPDAF algorithm could enhance the tracking performance of the current aviation radar system and meets the required performance of the new ATM system. Thus, the current radar system with the IMMPDAF algorithm could be used as an alternative system to continue aviation navigation and surveillance services of the ATM system during GPS outage periods.

Radar Tracking with an Interacting Multiple Model and Probabilistic Data Association Filter for Civil Aviation Applications

PubMed Central

Jan, Shau-Shiun; Kao, Yu-Chun

2013-01-01

The current trend of the civil aviation technology is to modernize the legacy air traffic control (ATC) system that is mainly supported by many ground based navigation aids to be the new air traffic management (ATM) system that is enabled by global positioning system (GPS) technology. Due to the low receiving power of GPS signal, it is a major concern to aviation authorities that the operation of the ATM system might experience service interruption when the GPS signal is jammed by either intentional or unintentional radio-frequency interference. To maintain the normal operation of the ATM system during the period of GPS outage, the use of the current radar system is proposed in this paper. However, the tracking performance of the current radar system could not meet the required performance of the ATM system, and an enhanced tracking algorithm, the interacting multiple model and probabilistic data association filter (IMMPDAF), is therefore developed to support the navigation and surveillance services of the ATM system. The conventional radar tracking algorithm, the nearest neighbor Kalman filter (NNKF), is used as the baseline to evaluate the proposed radar tracking algorithm, and the real flight data is used to validate the IMMPDAF algorithm. As shown in the results, the proposed IMMPDAF algorithm could enhance the tracking performance of the current aviation radar system and meets the required performance of the new ATM system. Thus, the current radar system with the IMMPDAF algorithm could be used as an alternative system to continue aviation navigation and surveillance services of the ATM system during GPS outage periods. PMID:23686142

Orbit Determination and Maneuver Detection Using Event Representation with Thrust-Fourier-Coefficients

NASA Astrophysics Data System (ADS)

Lubey, D.; Ko, H.; Scheeres, D.

The classical orbit determination (OD) method of dealing with unknown maneuvers is to restart the OD process with post-maneuver observations. However, it is also possible to continue the OD process through such unknown maneuvers by representing those unknown maneuvers with an appropriate event representation. It has been shown in previous work (Ko & Scheeres, JGCD 2014) that any maneuver performed by a satellite transitioning between two arbitrary orbital states can be represented as an equivalent maneuver connecting those two states using Thrust-Fourier-Coefficients (TFCs). Event representation using TFCs rigorously provides a unique control law that can generate the desired secular behavior for a given unknown maneuver. This paper presents applications of this representation approach to orbit prediction and maneuver detection problem across unknown maneuvers. The TFCs are appended to a sequential filter as an adjoint state to compensate unknown perturbing accelerations and the modified filter estimates the satellite state and thrust coefficients by processing OD across the time of an unknown maneuver. This modified sequential filter with TFCs is capable of fitting tracking data and maintaining an OD solution in the presence of unknown maneuvers. Also, the modified filter is found effective in detecting a sudden change in TFC values which indicates a maneuver. In order to illustrate that the event representation approach with TFCs is robust and sufficiently general to be easily adjustable, different types of measurement data are processed with the filter in a realistic LEO setting. Further, cases with mis-modeling of non-gravitational force are included in our study to verify the versatility and efficiency of our presented algorithm. Simulation results show that the modified sequential filter with TFCs can detect and estimate the orbit and thrust parameters in the presence of unknown maneuvers with or without measurement data during maneuvers. With no measurement data during maneuvers, the modified filter with TFCs uses an existing pre-maneuver orbit solution to compute a post-maneuver orbit solution by forcing TFCs to compensate for an unknown maneuver. With observation data available during maneuvers, maneuver start time and stop time is determined

SU-G-JeP1-12: Head-To-Head Performance Characterization of Two Multileaf Collimator Tracking Algorithms for Radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Caillet, V; Colvill, E; Royal North Shore Hospital, St Leonards, Sydney

2016-06-15

Purpose: Multi-leaf collimator (MLC) tracking is being clinically pioneered to continuously compensate for thoracic and abdominal motion during radiotherapy. The purpose of this work is to characterize the performance of two MLC tracking algorithms for cancer radiotherapy, based on a direct optimization and a piecewise leaf fitting approach respectively. Methods: To test the algorithms, both physical and in silico experiments were performed. Previously published high and low modulation VMAT plans for lung and prostate cancer cases were used along with eight patient-measured organ-specific trajectories. For both MLC tracking algorithm, the plans were run with their corresponding patient trajectories. The physicalmore » experiments were performed on a Trilogy Varian linac and a programmable phantom (HexaMotion platform). For each MLC tracking algorithm, plan and patient trajectory, the tracking accuracy was quantified as the difference in aperture area between ideal and fitted MLC. To compare algorithms, the average cumulative tracking error area for each experiment was calculated. The two-sample Kolmogorov-Smirnov (KS) test was used to evaluate the cumulative tracking errors between algorithms. Results: Comparison of tracking errors for the physical and in silico experiments showed minor differences between the two algorithms. The KS D-statistics for the physical experiments were below 0.05 denoting no significant differences between the two distributions pattern and the average error area (direct optimization/piecewise leaf-fitting) were comparable (66.64 cm2/65.65 cm2). For the in silico experiments, the KS D-statistics were below 0.05 and the average errors area were also equivalent (49.38 cm2/48.98 cm2). Conclusion: The comparison between the two leaf fittings algorithms demonstrated no significant differences in tracking errors, neither in a clinically realistic environment nor in silico. The similarities in the two independent algorithms give confidence in the use of either algorithm for clinical implementation.« less

Engineering cell-fluorescent ion track hybrid detectors.

PubMed

Niklas, Martin; Greilich, Steffen; Melzig, Claudius; Akselrod, Mark S; Debus, Jürgen; Jäkel, Oliver; Abdollahi, Amir

2013-06-11

The lack of sensitive biocompatible particle track detectors has so far limited parallel detection of physical energy deposition and biological response. Fluorescent nuclear track detectors (FNTDs) based on Al₂O₃:C,Mg single crystals combined with confocal laser scanning microscopy (CLSM) provide 3D information on ion tracks with a resolution limited by light diffraction. Here we report the development of next generation cell-fluorescent ion track hybrid detectors (Cell-Fit-HD). The biocompatibility of FNTDs was tested using six different cell lines, i.e. human non-small cell lung carcinoma (A549), glioblastoma (U87), androgen independent prostate cancer (PC3), epidermoid cancer (A431) and murine (VmDk) glioma SMA-560. To evaluate cell adherence, viability and conformal coverage of the crystals different seeding densities and alternative coating with extracellular matrix (fibronectin) was tested. Carbon irradiation was performed in Bragg peak (initial 270.55 MeV u⁻¹). A series of cell compartment specific fluorescence stains including nuclear (HOECHST), membrane (Glut-1), cytoplasm (Calcein AM, CM-DiI) were tested on Cell-Fit-HDs and a single CLSM was employed to co-detect the physical (crystal) as well as the biological (cell layer) information. The FNTD provides a biocompatible surface. Among the cells tested, A549 cells formed the most uniform, viable, tightly packed epithelial like monolayer. The ion track information was not compromised in Cell-Fit-HD as compared to the FNTD alone. Neither cell coating and culturing, nor additional staining procedures affected the properties of the FNTD surface to detect ion tracks. Standard immunofluorescence and live staining procedures could be employed to co-register cell biology and ion track information. The Cell-Fit-Hybrid Detector system is a promising platform for a multitude of studies linking biological response to energy deposition at high level of optical microscopy resolution.

Description of gas/particle sorption kinetics with an intraparticle diffusion model: Desorption experiments

USGS Publications Warehouse

Rounds, S.A.; Tiffany, B.A.; Pankow, J.F.

1993-01-01

Aerosol particles from a highway tunnel were collected on a Teflon membrane filter (TMF) using standard techniques. Sorbed organic compounds were then desorbed for 28 days by passing clean nitrogen through the filter. Volatile n-alkanes and polycyclic aromatic hydrocarbons (PAHs) were liberated from the filter quickly; only a small fraction of the less volatile ra-alkanes and PAHs were desorbed. A nonlinear least-squares method was used to fit an intraparticle diffusion model to the experimental data. Two fitting parameters were used: the gas/particle partition coefficient (Kp and an effective intraparticle diffusion coefficient (Oeff). Optimized values of Kp are in agreement with previously reported values. The slope of a correlation between the fitted values of Deff and Kp agrees well with theory, but the absolute values of Deff are a factor of ???106 smaller than predicted for sorption-retarded, gaseous diffusion. Slow transport through an organic or solid phase within the particles or preferential flow through the bed of particulate matter on the filter might be the cause of these very small effective diffusion coefficients. ?? 1993 American Chemical Society.

Guided filter and convolutional network based tracking for infrared dim moving target

NASA Astrophysics Data System (ADS)

Qian, Kun; Zhou, Huixin; Qin, Hanlin; Rong, Shenghui; Zhao, Dong; Du, Juan

2017-09-01

The dim moving target usually submerges in strong noise, and its motion observability is debased by numerous false alarms for low signal-to-noise ratio. A tracking algorithm that integrates the Guided Image Filter (GIF) and the Convolutional neural network (CNN) into the particle filter framework is presented to cope with the uncertainty of dim targets. First, the initial target template is treated as a guidance to filter incoming templates depending on similarities between the guidance and candidate templates. The GIF algorithm utilizes the structure in the guidance and performs as an edge-preserving smoothing operator. Therefore, the guidance helps to preserve the detail of valuable templates and makes inaccurate ones blurry, alleviating the tracking deviation effectively. Besides, the two-layer CNN method is adopted to obtain a powerful appearance representation. Subsequently, a Bayesian classifier is trained with these discriminative yet strong features. Moreover, an adaptive learning factor is introduced to prevent the update of classifier's parameters when a target undergoes sever background. At last, classifier responses of particles are utilized to generate particle importance weights and a re-sample procedure preserves samples according to the weight. In the predication stage, a 2-order transition model considers the target velocity to estimate current position. Experimental results demonstrate that the presented algorithm outperforms several relative algorithms in the accuracy.

Position Tracking During Human Walking Using an Integrated Wearable Sensing System.

PubMed

Zizzo, Giulio; Ren, Lei

2017-12-10

Progress has been made enabling expensive, high-end inertial measurement units (IMUs) to be used as tracking sensors. However, the cost of these IMUs is prohibitive to their widespread use, and hence the potential of low-cost IMUs is investigated in this study. A wearable low-cost sensing system consisting of IMUs and ultrasound sensors was developed. Core to this system is an extended Kalman filter (EKF), which provides both zero-velocity updates (ZUPTs) and Heuristic Drift Reduction (HDR). The IMU data was combined with ultrasound range measurements to improve accuracy. When a map of the environment was available, a particle filter was used to impose constraints on the possible user motions. The system was therefore composed of three subsystems: IMUs, ultrasound sensors, and a particle filter. A Vicon motion capture system was used to provide ground truth information, enabling validation of the sensing system. Using only the IMU, the system showed loop misclosure errors of 1% with a maximum error of 4-5% during walking. The addition of the ultrasound sensors resulted in a 15% reduction in the total accumulated error. Lastly, the particle filter was capable of providing noticeable corrections, which could keep the tracking error below 2% after the first few steps.

Flying Qualities Design Requirements for Sidestick Controllers

DTIC Science & Technology

1979-10-01

Harper vs wc - Fine Tracking 111 56. Cooper-Harper vs wc - Fine Tracking 111 57. Cooper-Harper ve wc - Landing 112 58. Cooper-Harper vs wc - Lending 112...c - Fine Tracking 119 62. Preference Rating vs wc - Fine Tracking 119 64. Preference Rating vs wc - Landing 120 65. Preference Rating vs w(c...a "fast airplane" to make up for the filtering effect of his arm and the stick. In fine tracking with a center stick the pilot will rest his arm

Correlation of Particle Traversals with Clonogenic Survival Using Cell-Fluorescent Ion Track Hybrid Detector.

PubMed

Dokic, Ivana; Niklas, Martin; Zimmermann, Ferdinand; Mairani, Andrea; Seidel, Philipp; Krunic, Damir; Jäkel, Oliver; Debus, Jürgen; Greilich, Steffen; Abdollahi, Amir

2015-01-01

Development of novel approaches linking the physical characteristics of particles with biological responses are of high relevance for the field of particle therapy. In radiobiology, the clonogenic survival of cells is considered the gold standard assay for the assessment of cellular sensitivity to ionizing radiation. Toward further development of next generation biodosimeters in particle therapy, cell-fluorescent ion track hybrid detector (Cell-FIT-HD) was recently engineered by our group and successfully employed to study physical particle track information in correlation with irradiation-induced DNA damage in cell nuclei. In this work, we investigated the feasibility of Cell-FIT-HD as a tool to study the effects of clinical beams on cellular clonogenic survival. Tumor cells were grown on the fluorescent nuclear track detector as cell culture, mimicking the standard procedures for clonogenic assay. Cell-FIT-HD was used to detect the spatial distribution of particle tracks within colony-initiating cells. The physical data were associated with radiation-induced foci as surrogates for DNA double-strand breaks, the hallmark of radiation-induced cell lethality. Long-term cell fate was monitored to determine the ability of cells to form colonies. We report the first successful detection of particle traversal within colony-initiating cells at subcellular resolution using Cell-FIT-HD.

Robust visual tracking based on deep convolutional neural networks and kernelized correlation filters

NASA Astrophysics Data System (ADS)

Yang, Hua; Zhong, Donghong; Liu, Chenyi; Song, Kaiyou; Yin, Zhouping

2018-03-01

Object tracking is still a challenging problem in computer vision, as it entails learning an effective model to account for appearance changes caused by occlusion, out of view, plane rotation, scale change, and background clutter. This paper proposes a robust visual tracking algorithm called deep convolutional neural network (DCNNCT) to simultaneously address these challenges. The proposed DCNNCT algorithm utilizes a DCNN to extract the image feature of a tracked target, and the full range of information regarding each convolutional layer is used to express the image feature. Subsequently, the kernelized correlation filters (CF) in each convolutional layer are adaptively learned, the correlation response maps of that are combined to estimate the location of the tracked target. To avoid the case of tracking failure, an online random ferns classifier is employed to redetect the tracked target, and a dual-threshold scheme is used to obtain the final target location by comparing the tracking result with the detection result. Finally, the change in scale of the target is determined by building scale pyramids and training a CF. Extensive experiments demonstrate that the proposed algorithm is effective at tracking, especially when evaluated using an index called the overlap rate. The DCNNCT algorithm is also highly competitive in terms of robustness with respect to state-of-the-art trackers in various challenging scenarios.

Smoothing and Predicting Celestial Pole Offsets using a Kalman Filter and Smoother

NASA Astrophysics Data System (ADS)

Nastula, J.; Chin, T. M.; Gross, R. S.; Winska, M.; Winska, J.

2017-12-01

Since the early days of interplanetary spaceflight, accounting for changes in the Earth's rotation is recognized to be critical for accurate navigation. In the 1960s, tracking anomalies during the Ranger VII and VIII lunar missions were traced to errors in the Earth orientation parameters. As a result, Earth orientation calibration methods were improved to support the Mariner IV and V planetary missions. Today, accurate Earth orientation parameters are used to track and navigate every interplanetary spaceflight mission. The interplanetary spacecraft tracking and navigation teams at JPL require the UT1 and polar motion parameters, and these Earth orientation parameters are estimated by the use of a Kalman filter to combine past measurements of these parameters and predict their future evolution. A model was then used to provide the nutation/precession components of the Earth's orientation separately. As a result, variations caused by the free core nutation were not taken into account. But for the highest accuracy, these variations must be considered. So JPL recently developed an approach based upon the use of a Kalman filter and smoother to provide smoothed and predicted celestial pole offsets (CPOs) to the interplanetary spacecraft tracking and navigation teams. The approach used at JPL to do this and an evaluation of the accuracy of the predicted CPOs will be given here.

Tracking a Non-Cooperative Target Using Real-Time Stereovision-Based Control: An Experimental Study.

PubMed

Shtark, Tomer; Gurfil, Pini

2017-03-31

Tracking a non-cooperative target is a challenge, because in unfamiliar environments most targets are unknown and unspecified. Stereovision is suited to deal with this issue, because it allows to passively scan large areas and estimate the relative position, velocity and shape of objects. This research is an experimental effort aimed at developing, implementing and evaluating a real-time non-cooperative target tracking methods using stereovision measurements only. A computer-vision feature detection and matching algorithm was developed in order to identify and locate the target in the captured images. Three different filters were designed for estimating the relative position and velocity, and their performance was compared. A line-of-sight control algorithm was used for the purpose of keeping the target within the field-of-view. Extensive analytical and numerical investigations were conducted on the multi-view stereo projection equations and their solutions, which were used to initialize the different filters. This research shows, using an experimental and numerical evaluation, the benefits of using the unscented Kalman filter and the total least squares technique in the stereovision-based tracking problem. These findings offer a general and more accurate method for solving the static and dynamic stereovision triangulation problems and the concomitant line-of-sight control.

Tracking a Non-Cooperative Target Using Real-Time Stereovision-Based Control: An Experimental Study

PubMed Central

Shtark, Tomer; Gurfil, Pini

2017-01-01

Tracking a non-cooperative target is a challenge, because in unfamiliar environments most targets are unknown and unspecified. Stereovision is suited to deal with this issue, because it allows to passively scan large areas and estimate the relative position, velocity and shape of objects. This research is an experimental effort aimed at developing, implementing and evaluating a real-time non-cooperative target tracking methods using stereovision measurements only. A computer-vision feature detection and matching algorithm was developed in order to identify and locate the target in the captured images. Three different filters were designed for estimating the relative position and velocity, and their performance was compared. A line-of-sight control algorithm was used for the purpose of keeping the target within the field-of-view. Extensive analytical and numerical investigations were conducted on the multi-view stereo projection equations and their solutions, which were used to initialize the different filters. This research shows, using an experimental and numerical evaluation, the benefits of using the unscented Kalman filter and the total least squares technique in the stereovision-based tracking problem. These findings offer a general and more accurate method for solving the static and dynamic stereovision triangulation problems and the concomitant line-of-sight control. PMID:28362338

Ultrasound thermography: A new temperature reconstruction model and in vivo results

NASA Astrophysics Data System (ADS)

Bayat, Mahdi; Ballard, John R.; Ebbini, Emad S.

2017-03-01

The recursive echo strain filter (RESF) model is presented as a new echo shift-based ultrasound temperature estimation model. The model is shown to have an infinite impulse response (IIR) filter realization of a differentitor-integrator operator. This model is then used for tracking sub-therapeutic temperature changes due to high intensity focused ultrasound (HIFU) shots in the hind limb of the Copenhagen rats in vivo. In addition to the reconstruction filter, a motion compensation method is presented which takes advantage of the deformation field outside the region of interest to correct the motion errors during temperature tracking. The combination of the RESF model and motion compensation algorithm is shown to greatly enhance the accuracy of the in vivo temperature estimation using ultrasound echo shifts.

Real-time object tracking based on scale-invariant features employing bio-inspired hardware.

PubMed

Yasukawa, Shinsuke; Okuno, Hirotsugu; Ishii, Kazuo; Yagi, Tetsuya

2016-09-01

We developed a vision sensor system that performs a scale-invariant feature transform (SIFT) in real time. To apply the SIFT algorithm efficiently, we focus on a two-fold process performed by the visual system: whole-image parallel filtering and frequency-band parallel processing. The vision sensor system comprises an active pixel sensor, a metal-oxide semiconductor (MOS)-based resistive network, a field-programmable gate array (FPGA), and a digital computer. We employed the MOS-based resistive network for instantaneous spatial filtering and a configurable filter size. The FPGA is used to pipeline process the frequency-band signals. The proposed system was evaluated by tracking the feature points detected on an object in a video. Copyright © 2016 Elsevier Ltd. All rights reserved.

Silicosis: Learn the Facts!

MedlinePlus

... during water tank construction. He wore a charcoal filter respirator while sandblasting, but it was the wrong ... properly fitted and selected respirator (e.g. particulate filter or airline supplied air respirator) designated for protection ...

Application of Multi-Hypothesis Sequential Monte Carlo for Breakup Analysis

NASA Astrophysics Data System (ADS)

Faber, W. R.; Zaidi, W.; Hussein, I. I.; Roscoe, C. W. T.; Wilkins, M. P.; Schumacher, P. W., Jr.

As more objects are launched into space, the potential for breakup events and space object collisions is ever increasing. These events create large clouds of debris that are extremely hazardous to space operations. Providing timely, accurate, and statistically meaningful Space Situational Awareness (SSA) data is crucial in order to protect assets and operations in space. The space object tracking problem, in general, is nonlinear in both state dynamics and observations, making it ill-suited to linear filtering techniques such as the Kalman filter. Additionally, given the multi-object, multi-scenario nature of the problem, space situational awareness requires multi-hypothesis tracking and management that is combinatorially challenging in nature. In practice, it is often seen that assumptions of underlying linearity and/or Gaussianity are used to provide tractable solutions to the multiple space object tracking problem. However, these assumptions are, at times, detrimental to tracking data and provide statistically inconsistent solutions. This paper details a tractable solution to the multiple space object tracking problem applicable to space object breakup events. Within this solution, simplifying assumptions of the underlying probability density function are relaxed and heuristic methods for hypothesis management are avoided. This is done by implementing Sequential Monte Carlo (SMC) methods for both nonlinear filtering as well as hypothesis management. This goal of this paper is to detail the solution and use it as a platform to discuss computational limitations that hinder proper analysis of large breakup events.

Inspection Robot Based Mobile Sensing and Power Line Tracking for Smart Grid

PubMed Central

Byambasuren, Bat-erdene; Kim, Donghan; Oyun-Erdene, Mandakh; Bold, Chinguun; Yura, Jargalbaatar

2016-01-01

Smart sensing and power line tracking is very important in a smart grid system. Illegal electricity usage can be detected by remote current measurement on overhead power lines using an inspection robot. There is a need for accurate detection methods of illegal electricity usage. Stable and correct power line tracking is a very prominent issue. In order to correctly track and make accurate measurements, the swing path of a power line should be previously fitted and predicted by a mathematical function using an inspection robot. After this, the remote inspection robot can follow the power line and measure the current. This paper presents a new power line tracking method using parabolic and circle fitting algorithms for illegal electricity detection. We demonstrate the effectiveness of the proposed tracking method by simulation and experimental results. PMID:26907274

Inspection Robot Based Mobile Sensing and Power Line Tracking for Smart Grid.

PubMed

Byambasuren, Bat-Erdene; Kim, Donghan; Oyun-Erdene, Mandakh; Bold, Chinguun; Yura, Jargalbaatar

2016-02-19

Smart sensing and power line tracking is very important in a smart grid system. Illegal electricity usage can be detected by remote current measurement on overhead power lines using an inspection robot. There is a need for accurate detection methods of illegal electricity usage. Stable and correct power line tracking is a very prominent issue. In order to correctly track and make accurate measurements, the swing path of a power line should be previously fitted and predicted by a mathematical function using an inspection robot. After this, the remote inspection robot can follow the power line and measure the current. This paper presents a new power line tracking method using parabolic and circle fitting algorithms for illegal electricity detection. We demonstrate the effectiveness of the proposed tracking method by simulation and experimental results.

Effects of measurement unobservability on neural extended Kalman filter tracking

NASA Astrophysics Data System (ADS)

Stubberud, Stephen C.; Kramer, Kathleen A.

2009-05-01

An important component of tracking fusion systems is the ability to fuse various sensors into a coherent picture of the scene. When multiple sensor systems are being used in an operational setting, the types of data vary. A significant but often overlooked concern of multiple sensors is the incorporation of measurements that are unobservable. An unobservable measurement is one that may provide information about the state, but cannot recreate a full target state. A line of bearing measurement, for example, cannot provide complete position information. Often, such measurements come from passive sensors such as a passive sonar array or an electronic surveillance measure (ESM) system. Unobservable measurements will, over time, result in the measurement uncertainty to grow without bound. While some tracking implementations have triggers to protect against the detrimental effects, many maneuver tracking algorithms avoid discussing this implementation issue. One maneuver tracking technique is the neural extended Kalman filter (NEKF). The NEKF is an adaptive estimation algorithm that estimates the target track as it trains a neural network on line to reduce the error between the a priori target motion model and the actual target dynamics. The weights of neural network are trained in a similar method to the state estimation/parameter estimation Kalman filter techniques. The NEKF has been shown to improve target tracking accuracy through maneuvers and has been use to predict target behavior using the new model that consists of the a priori model and the neural network. The key to the on-line adaptation of the NEKF is the fact that the neural network is trained using the same residuals as the Kalman filter for the tracker. The neural network weights are treated as augmented states to the target track. Through the state-coupling function, the weights are coupled to the target states. Thus, if the measurements cause the states of the target track to be unobservable, then the weights of the neural network have unobservable modes as well. In recent analysis, the NEKF was shown to have a significantly larger growth in the eigenvalues of the error covariance matrix than the standard EKF tracker when the measurements were purely bearings-only. This caused detrimental effects to the ability of the NEKF to model the target dynamics. In this work, the analysis is expanded to determine the detrimental effects of bearings-only measurements of various uncertainties on the performance of the NEKF when these unobservable measurements are interlaced with completely observable measurements. This analysis provides the ability to put implementation limitations on the NEKF when bearings-only sensors are present.

Real-Time Robust Tracking for Motion Blur and Fast Motion via Correlation Filters

PubMed Central

Xu, Lingyun; Luo, Haibo; Hui, Bin; Chang, Zheng

2016-01-01

Visual tracking has extensive applications in intelligent monitoring and guidance systems. Among state-of-the-art tracking algorithms, Correlation Filter methods perform favorably in robustness, accuracy and speed. However, it also has shortcomings when dealing with pervasive target scale variation, motion blur and fast motion. In this paper we proposed a new real-time robust scheme based on Kernelized Correlation Filter (KCF) to significantly improve performance on motion blur and fast motion. By fusing KCF and STC trackers, our algorithm also solve the estimation of scale variation in many scenarios. We theoretically analyze the problem for CFs towards motions and utilize the point sharpness function of the target patch to evaluate the motion state of target. Then we set up an efficient scheme to handle the motion and scale variation without much time consuming. Our algorithm preserves the properties of KCF besides the ability to handle special scenarios. In the end extensive experimental results on benchmark of VOT datasets show our algorithm performs advantageously competed with the top-rank trackers. PMID:27618046

Joint passive radar tracking and target classification using radar cross section

NASA Astrophysics Data System (ADS)

Herman, Shawn M.

2004-01-01

We present a recursive Bayesian solution for the problem of joint tracking and classification of airborne targets. In our system, we allow for complications due to multiple targets, false alarms, and missed detections. More importantly, though, we utilize the full benefit of a joint approach by implementing our tracker using an aerodynamically valid flight model that requires aircraft-specific coefficients such as wing area and vehicle mass, which are provided by our classifier. A key feature that bridges the gap between tracking and classification is radar cross section (RCS). By modeling the true deterministic relationship that exists between RCS and target aspect, we are able to gain both valuable class information and an estimate of target orientation. However, the lack of a closed-form relationship between RCS and target aspect prevents us from using the Kalman filter or its variants. Instead, we rely upon a sequential Monte Carlo-based approach known as particle filtering. In addition to allowing us to include RCS as a measurement, the particle filter also simplifies the implementation of our nonlinear non-Gaussian flight model.

Joint passive radar tracking and target classification using radar cross section

NASA Astrophysics Data System (ADS)

Herman, Shawn M.

2003-12-01

We present a recursive Bayesian solution for the problem of joint tracking and classification of airborne targets. In our system, we allow for complications due to multiple targets, false alarms, and missed detections. More importantly, though, we utilize the full benefit of a joint approach by implementing our tracker using an aerodynamically valid flight model that requires aircraft-specific coefficients such as wing area and vehicle mass, which are provided by our classifier. A key feature that bridges the gap between tracking and classification is radar cross section (RCS). By modeling the true deterministic relationship that exists between RCS and target aspect, we are able to gain both valuable class information and an estimate of target orientation. However, the lack of a closed-form relationship between RCS and target aspect prevents us from using the Kalman filter or its variants. Instead, we rely upon a sequential Monte Carlo-based approach known as particle filtering. In addition to allowing us to include RCS as a measurement, the particle filter also simplifies the implementation of our nonlinear non-Gaussian flight model.

Infrared dim-small target tracking via singular value decomposition and improved Kernelized correlation filter

NASA Astrophysics Data System (ADS)

Qian, Kun; Zhou, Huixin; Rong, Shenghui; Wang, Bingjian; Cheng, Kuanhong

2017-05-01

Infrared small target tracking plays an important role in applications including military reconnaissance, early warning and terminal guidance. In this paper, an effective algorithm based on the Singular Value Decomposition (SVD) and the improved Kernelized Correlation Filter (KCF) is presented for infrared small target tracking. Firstly, the super performance of the SVD-based algorithm is that it takes advantage of the target's global information and obtains a background estimation of an infrared image. A dim target is enhanced by subtracting the corresponding estimated background with update from the original image. Secondly, the KCF algorithm is combined with Gaussian Curvature Filter (GCF) to eliminate the excursion problem. The GCF technology is adopted to preserve the edge and eliminate the noise of the base sample in the KCF algorithm, helping to calculate the classifier parameter for a small target. At last, the target position is estimated with a response map, which is obtained via the kernelized classifier. Experimental results demonstrate that the presented algorithm performs favorably in terms of efficiency and accuracy, compared with several state-of-the-art algorithms.

Comparison and Comparability: Fitness Tracking between Youths with Different Physical Activity Levels

ERIC Educational Resources Information Center

Liu, Wenhao; Nichols, Randall A.; Zillifro, Traci D.

2013-01-01

This study compared a three-year tracking of health-related physical fitness between two comparable samples of six graders that enrolled either in a PE4life middle school ("n"?=?154) or another school with a traditional PE program ("n?"=?93) in the United States. For the cohort, the FITNESSGRAM[TM] (Cooper Institute for…

Tracking variations in the alpha activity in an electroencephalogram

NASA Technical Reports Server (NTRS)

Prabhu, K. S.

1971-01-01

The problem of tracking Alpha voltage variations in an electroencephalogram is discussed. This problem is important in encephalographic studies of sleep and effects of different stimuli on the brain. Very often the Alpha voltage is tracked by passing the EEG signal through a bandpass filter centered at the Alpha frequency, which hopefully will filter out unwanted noise from the Alpha activity. Some alternative digital techniques are suggested and their performance is compared with the standard technique. These digital techniques can be used in an environment where an electroencephalograph is interfaced with a small digital computer via an A/D convertor. They have the advantage that statistical statements about their variability can sometimes be made so that the effect sought can be assessed correctly in the presence of random fluctuations.

Application of analytical redundancy management to Shuttle crafts. [computerized simulation of microelectronic implementation

NASA Technical Reports Server (NTRS)

Montgomery, R. C.; Tabak, D.

1979-01-01

The study involves the bank of filters approach to analytical redundancy management since this is amenable to microelectronic implementation. Attention is given to a study of the UD factorized filter to determine if it gives more accurate estimates than the standard Kalman filter when data processing word size is reduced. It is reported that, as the word size is reduced, the effect of modeling error dominates the filter performance of the two filters. However, the UD filter is shown to maintain a slight advantage in tracking performance. It is concluded that because of the UD filter's stability in the serial processing mode, it remains the leading candidate for microelectronic implementation.

A comparative study of sensor fault diagnosis methods based on observer for ECAS system

NASA Astrophysics Data System (ADS)

Xu, Xing; Wang, Wei; Zou, Nannan; Chen, Long; Cui, Xiaoli

2017-03-01

The performance and practicality of electronically controlled air suspension (ECAS) system are highly dependent on the state information supplied by kinds of sensors, but faults of sensors occur frequently. Based on a non-linearized 3-DOF 1/4 vehicle model, different methods of fault detection and isolation (FDI) are used to diagnose the sensor faults for ECAS system. The considered approaches include an extended Kalman filter (EKF) with concise algorithm, a strong tracking filter (STF) with robust tracking ability, and the cubature Kalman filter (CKF) with numerical precision. We propose three filters of EKF, STF, and CKF to design a state observer of ECAS system under typical sensor faults and noise. Results show that three approaches can successfully detect and isolate faults respectively despite of the existence of environmental noise, FDI time delay and fault sensitivity of different algorithms are different, meanwhile, compared with EKF and STF, CKF method has best performing FDI of sensor faults for ECAS system.

False star detection and isolation during star tracking based on improved chi-square tests.

PubMed

Zhang, Hao; Niu, Yanxiong; Lu, Jiazhen; Yang, Yanqiang; Su, Guohua

2017-08-01

The star sensor is a precise attitude measurement device for a spacecraft. Star tracking is the main and key working mode for a star sensor. However, during star tracking, false stars become an inevitable interference for star sensor applications, which may result in declined measurement accuracy. A false star detection and isolation algorithm in star tracking based on improved chi-square tests is proposed in this paper. Two estimations are established based on a Kalman filter and a priori information, respectively. The false star detection is operated through adopting the global state chi-square test in a Kalman filter. The false star isolation is achieved using a local state chi-square test. Semi-physical experiments under different trajectories with various false stars are designed for verification. Experiment results show that various false stars can be detected and isolated from navigation stars during star tracking, and the attitude measurement accuracy is hardly influenced by false stars. The proposed algorithm is proved to have an excellent performance in terms of speed, stability, and robustness.

Digital Filters for Digital Phase-locked Loops

NASA Technical Reports Server (NTRS)

Simon, M.; Mileant, A.

1985-01-01

An s/z hybrid model for a general phase locked loop is proposed. The impact of the loop filter on the stability, gain margin, noise equivalent bandwidth, steady state error and time response is investigated. A specific digital filter is selected which maximizes the overall gain margin of the loop. This filter can have any desired number of integrators. Three integrators are sufficient in order to track a phase jerk with zero steady state error at loop update instants. This filter has one zero near z = 1.0 for each integrator. The total number of poles of the filter is equal to the number of integrators plus two.

Adaptive optics system performance approximations for atmospheric turbulence correction

NASA Astrophysics Data System (ADS)

Tyson, Robert K.

1990-10-01

Analysis of adaptive optics system behavior often can be reduced to a few approximations and scaling laws. For atmospheric turbulence correction, the deformable mirror (DM) fitting error is most often used to determine a priori the interactuator spacing and the total number of correction zones required. This paper examines the mirror fitting error in terms of its most commonly used exponential form. The explicit constant in the error term is dependent on deformable mirror influence function shape and actuator geometry. The method of least squares fitting of discrete influence functions to the turbulent wavefront is compared to the linear spatial filtering approximation of system performance. It is found that the spatial filtering method overstimates the correctability of the adaptive optics system by a small amount. By evaluating fitting error for a number of DM configurations, actuator geometries, and influence functions, fitting error constants verify some earlier investigations.

40 CFR 721.10411 - Alkanenitrile, bis(cyanoalkyl)amino (generic) (P-07-537).

Code of Federal Regulations, 2012 CFR

2012-07-01

... with N100 (if oil aerosols absent), R100, or P100 filters; NIOSH-certified powered air-purifying respirator equipped with a loose- fitting hood or helmet and high efficiency particulate air (HEPA) filters... HEPA filters; or NIOSH-certified supplied-air respirator operated in pressure demand or continuous flow...

40 CFR 721.10411 - Alkanenitrile, bis(cyanoalkyl)amino (generic) (P-07-537).

Code of Federal Regulations, 2013 CFR

2013-07-01

... with N100 (if oil aerosols absent), R100, or P100 filters; NIOSH-certified powered air-purifying respirator equipped with a loose- fitting hood or helmet and high efficiency particulate air (HEPA) filters... HEPA filters; or NIOSH-certified supplied-air respirator operated in pressure demand or continuous flow...

40 CFR 721.10411 - Alkanenitrile, bis(cyanoalkyl)amino (generic) (P-07-537).

Code of Federal Regulations, 2014 CFR

2014-07-01

... with N100 (if oil aerosols absent), R100, or P100 filters; NIOSH-certified powered air-purifying respirator equipped with a loose- fitting hood or helmet and high efficiency particulate air (HEPA) filters... HEPA filters; or NIOSH-certified supplied-air respirator operated in pressure demand or continuous flow...

Two techniques enable sampling of filtered and unfiltered molten metals

NASA Technical Reports Server (NTRS)

Burris, L., Jr.; Pierce, R. D.; Tobias, K. R.; Winsch, I. O.

1967-01-01

Filtered samples of molten metals are obtained by filtering through a plug of porous material fitted in the end of a sample tube, and unfiltered samples are obtained by using a capillary-tube extension rod with a perforated bucket. With these methods there are no sampling errors or loss of liquid.

A moving hum filter to suppress rotor noise in high-resolution airborne magnetic data

USGS Publications Warehouse

Xia, J.; Doll, W.E.; Miller, R.D.; Gamey, T.J.; Emond, A.M.

2005-01-01

A unique filtering approach is developed to eliminate helicopter rotor noise. It is designed to suppress harmonic noise from a rotor that varies slightly in amplitude, phase, and frequency and that contaminates aero-magnetic data. The filter provides a powerful harmonic noise-suppression tool for data acquired with modern large-dynamic-range recording systems. This three-step approach - polynomial fitting, bandpass filtering, and rotor-noise synthesis - significantly reduces rotor noise without altering the spectra of signals of interest. Two steps before hum filtering - polynomial fitting and bandpass filtering - are critical to accurately model the weak rotor noise. During rotor-noise synthesis, amplitude, phase, and frequency are determined. Data are processed segment by segment so that there is no limit on the length of data. The segment length changes dynamically along a line based on modeling results. Modeling the rotor noise is stable and efficient. Real-world data examples demonstrate that this method can suppress rotor noise by more than 95% when implemented in an aeromagnetic data-processing flow. ?? 2005 Society of Exploration Geophysicists. All rights reserved.

Real-time optical multiple object recognition and tracking system and method

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin (Inventor); Liu, Hua Kuang (Inventor)

1987-01-01

The invention relates to an apparatus and associated methods for the optical recognition and tracking of multiple objects in real time. Multiple point spatial filters are employed that pre-define the objects to be recognized at run-time. The system takes the basic technology of a Vander Lugt filter and adds a hololens. The technique replaces time, space and cost-intensive digital techniques. In place of multiple objects, the system can also recognize multiple orientations of a single object. This later capability has potential for space applications where space and weight are at a premium.

TrackArt: the user friendly interface for single molecule tracking data analysis and simulation applied to complex diffusion in mica supported lipid bilayers.

PubMed

Matysik, Artur; Kraut, Rachel S

2014-05-01

Single molecule tracking (SMT) analysis of fluorescently tagged lipid and protein probes is an attractive alternative to ensemble averaged methods such as fluorescence correlation spectroscopy (FCS) or fluorescence recovery after photobleaching (FRAP) for measuring diffusion in artificial and plasma membranes. The meaningful estimation of diffusion coefficients and their errors is however not straightforward, and is heavily dependent on sample type, acquisition method, and equipment used. Many approaches require advanced computing and programming skills for their implementation. Here we present TrackArt software, an accessible graphic interface for simulation and complex analysis of multiple particle paths. Imported trajectories can be filtered to eliminate spurious or corrupted tracks, and are then analyzed using several previously described methodologies, to yield single or multiple diffusion coefficients, their population fractions, and estimated errors. We use TrackArt to analyze the single-molecule diffusion behavior of a sphingolipid analog SM-Atto647N, in mica supported DOPC (1,2-dioleoyl-sn-glycero-3-phosphocholine) bilayers. Fitting with a two-component diffusion model confirms the existence of two separate populations of diffusing particles in these bilayers on mica. As a demonstration of the TrackArt workflow, we characterize and discuss the effective activation energies required to increase the diffusion rates of these populations, obtained from Arrhenius plots of temperature-dependent diffusion. Finally, TrackArt provides a simulation module, allowing the user to generate models with multiple particle trajectories, diffusing with different characteristics. Maps of domains, acting as impermeable or permeable obstacles for particles diffusing with given rate constants and diffusion coefficients, can be simulated or imported from an image. Importantly, this allows one to use simulated data with a known diffusion behavior as a comparison for results acquired using particular algorithms on actual, "natural" samples whose diffusion behavior is to be extracted. It can also serve as a tool for demonstrating diffusion principles. TrackArt is an open source, platform-independent, Matlab-based graphical user interface, and is easy to use even for those unfamiliar with the Matlab programming environment. TrackArt can be used for accurate simulation and analysis of complex diffusion data, such as diffusion in lipid bilayers, providing publication-quality formatted results.

SiC: filter for extreme ultraviolet

NASA Astrophysics Data System (ADS)

Mitrofanov, Alexander V.; Pudonin, Fedor A.; Zhitnik, Igor A.

1994-09-01

It is proposed to use thin films of silicon carbide as Extreme Ultraviolet bandpass filters transparent within 135-304 A band and with excellent cutoff blocking of the strong L(subscript (alpha) ) 1216 A line radiation. Mesh or particle track porous membrane supporting 200-800 A thickness SiC filters have been made by RF sputtering techniques. We describe the design and performance of these filters. Such type SiC filter was used in front of the microchannel plate detector of the TEREK X-Ray Telescope mounted on the Solar Observatory CORONAS-I which was successfully launched on March 2, 1994.

An improved algorithm of laser spot center detection in strong noise background

NASA Astrophysics Data System (ADS)

Zhang, Le; Wang, Qianqian; Cui, Xutai; Zhao, Yu; Peng, Zhong

2018-01-01

Laser spot center detection is demanded in many applications. The common algorithms for laser spot center detection such as centroid and Hough transform method have poor anti-interference ability and low detection accuracy in the condition of strong background noise. In this paper, firstly, the median filtering was used to remove the noise while preserving the edge details of the image. Secondly, the binarization of the laser facula image was carried out to extract target image from background. Then the morphological filtering was performed to eliminate the noise points inside and outside the spot. At last, the edge of pretreated facula image was extracted and the laser spot center was obtained by using the circle fitting method. In the foundation of the circle fitting algorithm, the improved algorithm added median filtering, morphological filtering and other processing methods. This method could effectively filter background noise through theoretical analysis and experimental verification, which enhanced the anti-interference ability of laser spot center detection and also improved the detection accuracy.

Volcano monitoring using the Global Positioning System: Filtering strategies

USGS Publications Warehouse

Larson, K.M.; Cervelli, Peter; Lisowski, M.; Miklius, Asta; Segall, P.; Owen, S.

2001-01-01

Permanent Global Positioning System (GPS) networks are routinely used for producing improved orbits and monitoring secular tectonic deformation. For these applications, data are transferred to an analysis center each day and routinely processed in 24-hour segments. To use GPS for monitoring volcanic events, which may last only a few hours, real-time or near real-time data processing and subdaily position estimates are valuable. Strategies have been researched for obtaining station coordinates every 15 min using a Kalman filter; these strategies have been tested on data collected by a GPS network on Kilauea Volcano. Data from this network are tracked continuously, recorded every 30 s, and telemetered hourly to the Hawaiian Volcano Observatory. A white noise model is heavily impacted by data outages and poor satellite geometry, but a properly constrained random walk model fits the data well. Using a borehole tiltmeter at Kilauea's summit as ground-truth, solutions using different random walk constraints were compared. This study indicates that signals on the order of 5 mm/h are resolvable using a random walk standard deviation of 0.45 cm/???h. Values lower than this suppress small signals, and values greater than this have significantly higher noise at periods of 1-6 hours. Copyright 2001 by the American Geophysical Union.

Evaluation of particulate filtering respirators using inward leakage (IL) or total inward leakage (TIL) testing--Korean experience.

PubMed

Han, Don-Hee; Lee, Jinheon

2005-10-01

Korean certification regulation for particulate filtering respirators requires inward leakage (IL) or total inward leakage (TIL) testing according to European Standard EN 13274-1, and the standard levels of compliance are similar to those of the European Standard. This study was conducted to evaluate particulate filtering respirators being commercially used in the Korean market using an IL or TIL test and the validity of standard level in Korea. Three half masks and 10 filtering facepieces (two top class, four 1st class and four 2nd class)-a total of 13 brand name respirators-were selected for the test with panels of 10 subjects. Each subject was classified with nine facial dimension grid squares in accordance with face length and lip length. IL or TIL testing was conducted at the laboratory of the 3M Innovation Center in which the experimental instruments and systems were established in compliance with European standards. The testing procedure followed EN 13274-1 (2001). As expected, leakages of half masks were less than those of filtering facepieces and the latter were significantly different among brands. TILs of the 1st class filtering facepieces were found to be much more than those of the 2nd class and the result may cause a wearer to get confused when selecting a mask. The main route leakage for filtering facepieces may not be the filter medium but the face seal. Therefore, it is necessary to develop well-fitting filtering facepieces for Koreans. Because leakages were significantly different for different facial dimensions, a defined test panel for IL or TIL testing according to country or race should be developed. A more precise method to demonstrate fit, for example, fit testing such as in the US regulations, will be needed before IL or TIL testing or when selecting a respirator. Another finding implies that geometric mean of five exercises for IL or TIL may be better than arithmetic mean to establish a standard individual subject mean.

Eye Detection and Tracking for Intelligent Human Computer Interaction

DTIC Science & Technology

2006-02-01

Meer and I. Weiss, “Smoothed Differentiation Filters for Images”, Journal of Visual Communication and Image Representation, 3(1):58-72, 1992. [13...25] P. Meer and I. Weiss. “Smoothed differentiation filters for images”. Journal of Visual Communication and Image Representation, 3(1), 1992

An experimental test of fitness variation across a hydrologic gradient predicts willow and poplar species distributions.

PubMed

Wei, Xiaojing; Savage, Jessica A; Riggs, Charlotte E; Cavender-Bares, Jeannine

2017-05-01

Environmental filtering is an important community assembly process influencing species distributions. Contrasting species abundance patterns along environmental gradients are commonly used to provide evidence for environmental filtering. However, the same abundance patterns may result from alternative or concurrent assembly processes. Experimental tests are an important means to decipher whether species fitness varies with environment, in the absence of dispersal constraints and biotic interactions, and to draw conclusions about the importance of environmental filtering in community assembly. We performed an experimental test of environmental filtering in 14 closely related willow and poplar species (family Salicaceae) by transplanting cuttings of each species into 40 common gardens established along a natural hydrologic gradient in the field, where competition was minimized and herbivory was controlled. We analyzed species fitness responses to the hydrologic environment based on cumulative growth and survival over two years using aster fitness models. We also examined variation in nine drought and flooding tolerance traits expected to contribute to performance based on a priori understanding of plant function in relation to water availability and stress. We found substantial evidence that environmental filtering along the hydrologic gradient played a critical role in determining species distributions. Fitness variation of each species in the field experiment was used to model their water table depth optima. These optima predicted 68% of the variation in species realized hydrologic niches based on peak abundance in naturally assembled communities in the surrounding region. Multiple traits associated with water transport efficiency and water stress tolerance were correlated with species hydrologic niches, but they did not necessarily covary with each other. As a consequence, species occupying similar hydrologic niches had different combinations of trait values. Moreover, individual traits were less phylogenetically conserved than species hydrologic niches and integrated water stress tolerance as determined by multiple traits. We conclude that differential fitness among species along the hydrologic gradient was the consequence of multiple traits associated with water transport and water stress tolerance, expressed in different combinations by different species. Varying environmental tolerance, in turn, played a critical role in driving niche segregation among close relatives along the hydrologic gradient. © 2017 by the Ecological Society of America.

An Optically Implemented Kalman Filter Algorithm.

DTIC Science & Technology

1983-12-01

8b. OFFICE SYMOOL 9. PROCUREMENT INSTRUMENT IDENTIFICATION NUMBER 8c. ADDRESS (City, State and ZIP Code ) 10. SOURCE OF FUNDING NOS.______ PROGRAM...are completely speci- fied for the correlation stage to perform the required corre- lation in real time, and the filter stage to perform the lin- ear...performance analy- ses indicated an enhanced ability of the nonadaptive filter to track a realistic distant point source target with an error standard

77 FR 28353 - Foreign-Trade Zone 45-Portland, OR, Notification of Proposed Production Activity, Shimadzu USA...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-14

...; filter paper; technical books and manuals; textile-covered foam shielding; ceramic hardware and fittings... cables (including fiber optic cable); insulators; filters; lenses; mirrors; prisms; other optical...

Sequential bearings-only-tracking initiation with particle filtering method.

PubMed

Liu, Bin; Hao, Chengpeng

2013-01-01

The tracking initiation problem is examined in the context of autonomous bearings-only-tracking (BOT) of a single appearing/disappearing target in the presence of clutter measurements. In general, this problem suffers from a combinatorial explosion in the number of potential tracks resulted from the uncertainty in the linkage between the target and the measurement (a.k.a the data association problem). In addition, the nonlinear measurements lead to a non-Gaussian posterior probability density function (pdf) in the optimal Bayesian sequential estimation framework. The consequence of this nonlinear/non-Gaussian context is the absence of a closed-form solution. This paper models the linkage uncertainty and the nonlinear/non-Gaussian estimation problem jointly with solid Bayesian formalism. A particle filtering (PF) algorithm is derived for estimating the model's parameters in a sequential manner. Numerical results show that the proposed solution provides a significant benefit over the most commonly used methods, IPDA and IMMPDA. The posterior Cramér-Rao bounds are also involved for performance evaluation.

A High Performance Computing Study of a Scalable FISST-Based Approach to Multi-Target, Multi-Sensor Tracking

NASA Astrophysics Data System (ADS)

Hussein, I.; Wilkins, M.; Roscoe, C.; Faber, W.; Chakravorty, S.; Schumacher, P.

2016-09-01

Finite Set Statistics (FISST) is a rigorous Bayesian multi-hypothesis management tool for the joint detection, classification and tracking of multi-sensor, multi-object systems. Implicit within the approach are solutions to the data association and target label-tracking problems. The full FISST filtering equations, however, are intractable. While FISST-based methods such as the PHD and CPHD filters are tractable, they require heavy moment approximations to the full FISST equations that result in a significant loss of information contained in the collected data. In this paper, we review Smart Sampling Markov Chain Monte Carlo (SSMCMC) that enables FISST to be tractable while avoiding moment approximations. We study the effect of tuning key SSMCMC parameters on tracking quality and computation time. The study is performed on a representative space object catalog with varying numbers of RSOs. The solution is implemented in the Scala computing language at the Maui High Performance Computing Center (MHPCC) facility.

Real time tracking by LOPF algorithm with mixture model

NASA Astrophysics Data System (ADS)

Meng, Bo; Zhu, Ming; Han, Guangliang; Wu, Zhiguo

2007-11-01

A new particle filter-the Local Optimum Particle Filter (LOPF) algorithm is presented for tracking object accurately and steadily in visual sequences in real time which is a challenge task in computer vision field. In order to using the particles efficiently, we first use Sobel algorithm to extract the profile of the object. Then, we employ a new Local Optimum algorithm to auto-initialize some certain number of particles from these edge points as centre of the particles. The main advantage we do this in stead of selecting particles randomly in conventional particle filter is that we can pay more attentions on these more important optimum candidates and reduce the unnecessary calculation on those negligible ones, in addition we can overcome the conventional degeneracy phenomenon in a way and decrease the computational costs. Otherwise, the threshold is a key factor that affecting the results very much. So here we adapt an adaptive threshold choosing method to get the optimal Sobel result. The dissimilarities between the target model and the target candidates are expressed by a metric derived from the Bhattacharyya coefficient. Here, we use both the counter cue to select the particles and the color cur to describe the targets as the mixture target model. The effectiveness of our scheme is demonstrated by real visual tracking experiments. Results from simulations and experiments with real video data show the improved performance of the proposed algorithm when compared with that of the standard particle filter. The superior performance is evident when the target encountering the occlusion in real video where the standard particle filter usually fails.

Sensor Management for Fighter Applications

DTIC Science & Technology

2006-06-01

has consistently shown that by directly estimating the prob- ability density of a target state using a track - before - detect scheme, weak and densely... track - before - detect nonlinear filter was constructed to estimate the joint density of all state variables. A simulation that emulates estimator...targets in clutter and noise from sensed kinematic and identity data. Among the most capable is track - before - detect (TBD), which delivers

Multi-Sensor Information Integration and Automatic Understanding

DTIC Science & Technology

2008-11-01

also produced a real-time implementation of the tracking and anomalous behavior detection system that runs on real- world data – either using real-time...surveillance and airborne IED detection . 15. SUBJECT TERMS Multi-hypothesis tracking , particle filters, anomalous behavior detection , Bayesian...analyst to support decision making with large data sets. A key feature of the real-time tracking and behavior detection system developed is that the

Investigating MAI's Precision: Single Interferogram and Time Series Filtering

NASA Astrophysics Data System (ADS)

Bechor Ben Dov, N.; Herring, T.

2010-12-01

Multiple aperture InSAR (MAI) is a technique to obtain along-track displacements from InSAR phase data. Because InSAR measurements are insensitive to along-track displacements, it is only possible to retrieve them using none-interferometric approaches, either pixel-offset tracking or using data from different orbital configurations and assuming continuity/ displacement model. These approaches are limited by precision and data acquisition conflicts, respectively. MAI is promising in this respect as its precision is better than the former and its data is available whether additional acquisitions are there or not. Here we study the MAI noise and develop a filter to reduce it. We test the filtering with empirical noise and simulated signal data. Below we describe the filtered results single interferogram precision, and a Kalman filter approach for MAI time series. We use 14 interferograms taken over the larger Los Angeles/San Gabrial Mountains area in CA. The interferograms include a variety of decorrelation sources, both terrain-related (topographic variations, vegetation and agriculture), and imaging-related (spatial and temporal baselines of 200-500m and 1-12 months, respectively). Most of the pixels are in the low to average coherence range (below 0.7). The data were collected by ESA and made available by the WInSAR consortium. We assume the data contain “zero” along-track signal (less then the theoretical 4 cm for our coherence range), and use the images as 14 dependent realizations of the MAI noise. We find a wide distribution of phase values σ = 2-3 radians (wrapped). We superimpose a signal on our MAI noise interferograms using along-track displacement (-88 - 143 cm) calculated for the 1812 Wrightwood earthquake. To analyze single MAI interferograms, we design an iterative quantile-based filter and test it on the noise+signal MAI interferograms. The residuals reveal the following MAI noise characteristics: (1) a constant noise term, up to 90 cm (2) a displacement gradient term, up to 0.75cm/km (3) a coherence dependent root residuals sum of squares (RRSS), down to 5 cm at 0.8 coherence In the figure we present two measures of the MAI rmse. Prior to phase gradient correction the RRSS follows the circled line. With the correction, the RRSS follows the solid line. We next evaluate MAI's precision given a time series. We use a Kalman Filter to estimate the spatially and temporally correlated components of the MAI data. We reference the displacements to a given area in the interferograms, weight the data with coherence, and model the reminder terms of the spatially correlated noise as a quadratic phase gradient across the image. The results (not displayed) again vary with coherence. MAI single interferogram precision

Performance Enhancement for a GPS Vector-Tracking Loop Utilizing an Adaptive Iterated Extended Kalman Filter

PubMed Central

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

Performance enhancement for a GPS vector-tracking loop utilizing an adaptive iterated extended Kalman filter.

PubMed

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.

Multimodal Pilot Behavior in Multi-Axis Tracking Tasks with Time-Varying Motion Cueing Gains

NASA Technical Reports Server (NTRS)

Zaal, P. M. T; Pool, D. M.

2014-01-01

In a large number of motion-base simulators, adaptive motion filters are utilized to maximize the use of the available motion envelope of the motion system. However, not much is known about how the time-varying characteristics of such adaptive filters affect pilots when performing manual aircraft control. This paper presents the results of a study investigating the effects of time-varying motion filter gains on pilot control behavior and performance. An experiment was performed in a motion-base simulator where participants performed a simultaneous roll and pitch tracking task, while the roll and/or pitch motion filter gains changed over time. Results indicate that performance increases over time with increasing motion gains. This increase is a result of a time-varying adaptation of pilots' equalization dynamics, characterized by increased visual and motion response gains and decreased visual lead time constants. Opposite trends are found for decreasing motion filter gains. Even though the trends in both controlled axes are found to be largely the same, effects are less significant in roll. In addition, results indicate minor cross-coupling effects between pitch and roll, where a cueing variation in one axis affects the behavior adopted in the other axis.

Computational segmentation of collagen fibers from second-harmonic generation images of breast cancer

NASA Astrophysics Data System (ADS)

Bredfeldt, Jeremy S.; Liu, Yuming; Pehlke, Carolyn A.; Conklin, Matthew W.; Szulczewski, Joseph M.; Inman, David R.; Keely, Patricia J.; Nowak, Robert D.; Mackie, Thomas R.; Eliceiri, Kevin W.

2014-01-01

Second-harmonic generation (SHG) imaging can help reveal interactions between collagen fibers and cancer cells. Quantitative analysis of SHG images of collagen fibers is challenged by the heterogeneity of collagen structures and low signal-to-noise ratio often found while imaging collagen in tissue. The role of collagen in breast cancer progression can be assessed post acquisition via enhanced computation. To facilitate this, we have implemented and evaluated four algorithms for extracting fiber information, such as number, length, and curvature, from a variety of SHG images of collagen in breast tissue. The image-processing algorithms included a Gaussian filter, SPIRAL-TV filter, Tubeness filter, and curvelet-denoising filter. Fibers are then extracted using an automated tracking algorithm called fiber extraction (FIRE). We evaluated the algorithm performance by comparing length, angle and position of the automatically extracted fibers with those of manually extracted fibers in twenty-five SHG images of breast cancer. We found that the curvelet-denoising filter followed by FIRE, a process we call CT-FIRE, outperforms the other algorithms under investigation. CT-FIRE was then successfully applied to track collagen fiber shape changes over time in an in vivo mouse model for breast cancer.

Multi-Complementary Model for Long-Term Tracking

PubMed Central

Zhang, Deng; Zhang, Junchang; Xia, Chenyang

2018-01-01

In recent years, video target tracking algorithms have been widely used. However, many tracking algorithms do not achieve satisfactory performance, especially when dealing with problems such as object occlusions, background clutters, motion blur, low illumination color images, and sudden illumination changes in real scenes. In this paper, we incorporate an object model based on contour information into a Staple tracker that combines the correlation filter model and color model to greatly improve the tracking robustness. Since each model is responsible for tracking specific features, the three complementary models combine for more robust tracking. In addition, we propose an efficient object detection model with contour and color histogram features, which has good detection performance and better detection efficiency compared to the traditional target detection algorithm. Finally, we optimize the traditional scale calculation, which greatly improves the tracking execution speed. We evaluate our tracker on the Object Tracking Benchmarks 2013 (OTB-13) and Object Tracking Benchmarks 2015 (OTB-15) benchmark datasets. With the OTB-13 benchmark datasets, our algorithm is improved by 4.8%, 9.6%, and 10.9% on the success plots of OPE, TRE and SRE, respectively, in contrast to another classic LCT (Long-term Correlation Tracking) algorithm. On the OTB-15 benchmark datasets, when compared with the LCT algorithm, our algorithm achieves 10.4%, 12.5%, and 16.1% improvement on the success plots of OPE, TRE, and SRE, respectively. At the same time, it needs to be emphasized that, due to the high computational efficiency of the color model and the object detection model using efficient data structures, and the speed advantage of the correlation filters, our tracking algorithm could still achieve good tracking speed. PMID:29425170

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sutcliffe, G. D.; Milanese, L. M.; Orozco, D.

CR-39 detectors are used routinely in inertial confinement fusion (ICF) experiments as a part of nuclear diagnostics. CR-39 is filtered to stop fast ablator ions which have been accelerated from an ICF implosion due to electric fields caused by laser-plasma interactions. In some experiments, the filtering is insufficient to block these ions and the fusion-product signal tracks are lost in the large background of accelerated ion tracks. A technique for recovering signal in these scenarios has been developed, tested, and implemented successfully. The technique involves removing material from the surface of the CR-39 to a depth beyond the endpoint ofmore » the ablator ion tracks. The technique preserves signal magnitude (yield) as well as structure in radiograph images. The technique is effective when signal particle range is at least 10 μm deeper than the necessary bulk material removal.« less

An algorithm of adaptive scale object tracking in occlusion

NASA Astrophysics Data System (ADS)

Zhao, Congmei

2017-05-01

Although the correlation filter-based trackers achieve the competitive results both on accuracy and robustness, there are still some problems in handling scale variations, object occlusion, fast motions and so on. In this paper, a multi-scale kernel correlation filter algorithm based on random fern detector was proposed. The tracking task was decomposed into the target scale estimation and the translation estimation. At the same time, the Color Names features and HOG features were fused in response level to further improve the overall tracking performance of the algorithm. In addition, an online random fern classifier was trained to re-obtain the target after the target was lost. By comparing with some algorithms such as KCF, DSST, TLD, MIL, CT and CSK, experimental results show that the proposed approach could estimate the object state accurately and handle the object occlusion effectively.

Formation Flying Satellite Control Around the L2 Sun-Earth Libration Point

NASA Technical Reports Server (NTRS)

Hamilton, Nicholas H.; Folta, David; Carpenter, Russell; Bauer, Frank (Technical Monitor)

2002-01-01

This paper discusses the development of a linear control algorithm for formations in the vicinity of the L2 sun-Earth libration point. The development of a simplified extended Kalman filter is included as well. Simulations are created for the analysis of the stationkeeping and various formation maneuvers of the Stellar Imager mission. The simulations provide tracking error, estimation error, and control effort results. For formation maneuvering, the formation spacecraft track to within 4 meters of their desired position and within 1.5 millimeters per second of their desired zero velocity. The filter, with few exceptions, keeps the estimation errors within their three-sigma values. Without noise, the controller performs extremely well, with the formation spacecraft tracking to within several micrometers. Each spacecraft uses around 1 to 2 grams of propellant per maneuver, depending on the circumstances.

Research on infrared small-target tracking technology under complex background

NASA Astrophysics Data System (ADS)

Liu, Lei; Wang, Xin; Chen, Jilu; Pan, Tao

2012-10-01

In this paper, some basic principles and the implementing flow charts of a series of algorithms for target tracking are described. On the foundation of above works, a moving target tracking software base on the OpenCV is developed by the software developing platform MFC. Three kinds of tracking algorithms are integrated in this software. These two tracking algorithms are Kalman Filter tracking method and Camshift tracking method. In order to explain the software clearly, the framework and the function are described in this paper. At last, the implementing processes and results are analyzed, and those algorithms for tracking targets are evaluated from the two aspects of subjective and objective. This paper is very significant in the application of the infrared target tracking technology.

Are American Children and Youth Fit?: It's Time We Learned

ERIC Educational Resources Information Center

Morrow, James R., Jr.

2005-01-01

The current state of physical fitness in American youth is unknown. While evidence exists that obesity levels are increasing in children and youth, data on declines in physical fitness measures (i.e., cardiorespiratory and musculoskeletal fitness) are lacking. Tracking of physical fitness components has been poorly done. Surveillance of behaviors…

Radar range data signal enhancement tracker

NASA Technical Reports Server (NTRS)

1975-01-01

The design, fabrication, and performance characteristics are described of two digital data signal enhancement filters which are capable of being inserted between the Space Shuttle Navigation Sensor outputs and the guidance computer. Commonality of interfaces has been stressed so that the filters may be evaluated through operation with simulated sensors or with actual prototype sensor hardware. The filters will provide both a smoothed range and range rate output. Different conceptual approaches are utilized for each filter. The first filter is based on a combination low pass nonrecursive filter and a cascaded simple average smoother for range and range rate, respectively. Filter number two is a tracking filter which is capable of following transient data of the type encountered during burn periods. A test simulator was also designed which generates typical shuttle navigation sensor data.

Fuzzy Finite-Time Command Filtered Control of Nonlinear Systems With Input Saturation.

PubMed

Yu, Jinpeng; Zhao, Lin; Yu, Haisheng; Lin, Chong; Dong, Wenjie

2017-08-22

This paper considers the fuzzy finite-time tracking control problem for a class of nonlinear systems with input saturation. A novel fuzzy finite-time command filtered backstepping approach is proposed by introducing the fuzzy finite-time command filter, designing the new virtual control signals and the modified error compensation signals. The proposed approach not only holds the advantages of the conventional command-filtered backstepping control, but also guarantees the finite-time convergence. A practical example is included to show the effectiveness of the proposed method.

Engineering cell-fluorescent ion track hybrid detectors

PubMed Central

2013-01-01

Background The lack of sensitive biocompatible particle track detectors has so far limited parallel detection of physical energy deposition and biological response. Fluorescent nuclear track detectors (FNTDs) based on Al2O3:C,Mg single crystals combined with confocal laser scanning microscopy (CLSM) provide 3D information on ion tracks with a resolution limited by light diffraction. Here we report the development of next generation cell-fluorescent ion track hybrid detectors (Cell-Fit-HD). Methods The biocompatibility of FNTDs was tested using six different cell lines, i.e. human non-small cell lung carcinoma (A549), glioblastoma (U87), androgen independent prostate cancer (PC3), epidermoid cancer (A431) and murine (VmDk) glioma SMA-560. To evaluate cell adherence, viability and conformal coverage of the crystals different seeding densities and alternative coating with extracellular matrix (fibronectin) was tested. Carbon irradiation was performed in Bragg peak (initial 270.55 MeV u−1). A series of cell compartment specific fluorescence stains including nuclear (HOECHST), membrane (Glut-1), cytoplasm (Calcein AM, CM-DiI) were tested on Cell-Fit-HDs and a single CLSM was employed to co-detect the physical (crystal) as well as the biological (cell layer) information. Results The FNTD provides a biocompatible surface. Among the cells tested, A549 cells formed the most uniform, viable, tightly packed epithelial like monolayer. The ion track information was not compromised in Cell-Fit-HD as compared to the FNTD alone. Neither cell coating and culturing, nor additional staining procedures affected the properties of the FNTD surface to detect ion tracks. Standard immunofluorescence and live staining procedures could be employed to co-register cell biology and ion track information. Conclusions The Cell-Fit-Hybrid Detector system is a promising platform for a multitude of studies linking biological response to energy deposition at high level of optical microscopy resolution. PMID:23758749

ParseCNV integrative copy number variation association software with quality tracking

PubMed Central

Glessner, Joseph T.; Li, Jin; Hakonarson, Hakon

2013-01-01

A number of copy number variation (CNV) calling algorithms exist; however, comprehensive software tools for CNV association studies are lacking. We describe ParseCNV, unique software that takes CNV calls and creates probe-based statistics for CNV occurrence in both case–control design and in family based studies addressing both de novo and inheritance events, which are then summarized based on CNV regions (CNVRs). CNVRs are defined in a dynamic manner to allow for a complex CNV overlap while maintaining precise association region. Using this approach, we avoid failure to converge and non-monotonic curve fitting weaknesses of programs, such as CNVtools and CNVassoc, and although Plink is easy to use, it only provides combined CNV state probe-based statistics, not state-specific CNVRs. Existing CNV association methods do not provide any quality tracking information to filter confident associations, a key issue which is fully addressed by ParseCNV. In addition, uncertainty in CNV calls underlying CNV associations is evaluated to verify significant results, including CNV overlap profiles, genomic context, number of probes supporting the CNV and single-probe intensities. When optimal quality control parameters are followed using ParseCNV, 90% of CNVs validate by polymerase chain reaction, an often problematic stage because of inadequate significant association review. ParseCNV is freely available at http://parsecnv.sourceforge.net. PMID:23293001

ParseCNV integrative copy number variation association software with quality tracking.

PubMed

Glessner, Joseph T; Li, Jin; Hakonarson, Hakon

2013-03-01

A number of copy number variation (CNV) calling algorithms exist; however, comprehensive software tools for CNV association studies are lacking. We describe ParseCNV, unique software that takes CNV calls and creates probe-based statistics for CNV occurrence in both case-control design and in family based studies addressing both de novo and inheritance events, which are then summarized based on CNV regions (CNVRs). CNVRs are defined in a dynamic manner to allow for a complex CNV overlap while maintaining precise association region. Using this approach, we avoid failure to converge and non-monotonic curve fitting weaknesses of programs, such as CNVtools and CNVassoc, and although Plink is easy to use, it only provides combined CNV state probe-based statistics, not state-specific CNVRs. Existing CNV association methods do not provide any quality tracking information to filter confident associations, a key issue which is fully addressed by ParseCNV. In addition, uncertainty in CNV calls underlying CNV associations is evaluated to verify significant results, including CNV overlap profiles, genomic context, number of probes supporting the CNV and single-probe intensities. When optimal quality control parameters are followed using ParseCNV, 90% of CNVs validate by polymerase chain reaction, an often problematic stage because of inadequate significant association review. ParseCNV is freely available at http://parsecnv.sourceforge.net.

Localization Methods for a Mobile Robot in Urban Environments

DTIC Science & Technology

2004-10-04

Columbia University, Department of Computer Science, 2001. [30] R. Brown and P. Hwang , Introduction to random signals and applied Kalman filtering, 3rd...sensor. An extended Kalman filter integrates the sensor data and keeps track of the uncertainty associated with it. The second method is based on...errors+ compass/GPS errors corrected odometry pose odometry error estimates zk zk h(x)~ h(x)~ Kalman Filter zk Fig. 4. A diagram of the extended

Magician Simulator: A Realistic Simulator for Heterogenous Teams of Autonomous Robots. MAGIC 2010 Challenge

DTIC Science & Technology

2011-02-07

Sensor UGVs (SUGV) or Disruptor UGVs, depending on their payload. The SUGVs included vision, GPS/IMU, and LIDAR systems for identifying and tracking...employed by all the MAGICian research groups. Objects of interest were tracked using standard LIDAR and Computer Vision template-based feature...tracking approaches. Mapping was solved through Multi-Agent particle-filter based Simultaneous Locali- zation and Mapping ( SLAM ). Our system contains

Replaceable filters and cones for flared-tubing connectors

NASA Technical Reports Server (NTRS)

Grant, L. E.; Howland, B. T.

1970-01-01

Connector is modified by machining the cone from one end before the fitting is bored to accommodate a metallic-filament type of slip-in filter. Thus, when surface of the cone is damaged, only the cone needs replacement.

A-Track: A new approach for detection of moving objects in FITS images

NASA Astrophysics Data System (ADS)

Atay, T.; Kaplan, M.; Kilic, Y.; Karapinar, N.

2016-10-01

We have developed a fast, open-source, cross-platform pipeline, called A-Track, for detecting the moving objects (asteroids and comets) in sequential telescope images in FITS format. The pipeline is coded in Python 3. The moving objects are detected using a modified line detection algorithm, called MILD. We tested the pipeline on astronomical data acquired by an SI-1100 CCD with a 1-meter telescope. We found that A-Track performs very well in terms of detection efficiency, stability, and processing time. The code is hosted on GitHub under the GNU GPL v3 license.

Kalman filter tracking on parallel architectures

NASA Astrophysics Data System (ADS)

Cerati, G.; Elmer, P.; Krutelyov, S.; Lantz, S.; Lefebvre, M.; McDermott, K.; Riley, D.; Tadel, M.; Wittich, P.; Wurthwein, F.; Yagil, A.

2017-10-01

We report on the progress of our studies towards a Kalman filter track reconstruction algorithm with optimal performance on manycore architectures. The combinatorial structure of these algorithms is not immediately compatible with an efficient SIMD (or SIMT) implementation; the challenge for us is to recast the existing software so it can readily generate hundreds of shared-memory threads that exploit the underlying instruction set of modern processors. We show how the data and associated tasks can be organized in a way that is conducive to both multithreading and vectorization. We demonstrate very good performance on Intel Xeon and Xeon Phi architectures, as well as promising first results on Nvidia GPUs.

A landmark recognition and tracking experiment for flight on the Shuttle/Advanced Technology Laboratory (ATL)

NASA Technical Reports Server (NTRS)

Welch, J. D.

1975-01-01

The preliminary design of an experiment for landmark recognition and tracking from the Shuttle/Advanced Technology Laboratory is described. It makes use of parallel coherent optical processing to perform correlation tests between landmarks observed passively with a telescope and previously made holographic matched filters. The experimental equipment including the optics, the low power laser, the random access file of matched filters and the electro-optical readout device are described. A real time optically excited liquid crystal device is recommended for performing the input non-coherent optical to coherent optical interface function. A development program leading to a flight experiment in 1981 is outlined.

Location detection and tracking of moving targets by a 2D IR-UWB radar system.

PubMed

Nguyen, Van-Han; Pyun, Jae-Young

2015-03-19

In indoor environments, the Global Positioning System (GPS) and long-range tracking radar systems are not optimal, because of signal propagation limitations in the indoor environment. In recent years, the use of ultra-wide band (UWB) technology has become a possible solution for object detection, localization and tracking in indoor environments, because of its high range resolution, compact size and low cost. This paper presents improved target detection and tracking techniques for moving objects with impulse-radio UWB (IR-UWB) radar in a short-range indoor area. This is achieved through signal-processing steps, such as clutter reduction, target detection, target localization and tracking. In this paper, we introduce a new combination consisting of our proposed signal-processing procedures. In the clutter-reduction step, a filtering method that uses a Kalman filter (KF) is proposed. Then, in the target detection step, a modification of the conventional CLEAN algorithm which is used to estimate the impulse response from observation region is applied for the advanced elimination of false alarms. Then, the output is fed into the target localization and tracking step, in which the target location and trajectory are determined and tracked by using unscented KF in two-dimensional coordinates. In each step, the proposed methods are compared to conventional methods to demonstrate the differences in performance. The experiments are carried out using actual IR-UWB radar under different scenarios. The results verify that the proposed methods can improve the probability and efficiency of target detection and tracking.

Robust tracking and distributed synchronization control of a multi-motor servomechanism with H-infinity performance.

PubMed

Wang, Minlin; Ren, Xuemei; Chen, Qiang

2018-01-01

The multi-motor servomechanism (MMS) is a multi-variable, high coupling and nonlinear system, which makes the controller design challenging. In this paper, an adaptive robust H-infinity control scheme is proposed to achieve both the load tracking and multi-motor synchronization of MMS. This control scheme consists of two parts: a robust tracking controller and a distributed synchronization controller. The robust tracking controller is constructed by incorporating a neural network (NN) K-filter observer into the dynamic surface control, while the distributed synchronization controller is designed by combining the mean deviation coupling control strategy with the distributed technique. The proposed control scheme has several merits: 1) by using the mean deviation coupling synchronization control strategy, the tracking controller and the synchronization controller can be designed individually without any coupling problem; 2) the immeasurable states and unknown nonlinearities are handled by a NN K-filter observer, where the number of NN weights is largely reduced by using the minimal learning parameter technique; 3) the H-infinity performances of tracking error and synchronization error are guaranteed by introducing a robust term into the tracking controller and the synchronization controller, respectively. The stabilities of the tracking and synchronization control systems are analyzed by the Lyapunov theory. Simulation and experimental results based on a four-motor servomechanism are conducted to demonstrate the effectiveness of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

A multiscale filter for noise reduction of low-dose cone beam projections.

PubMed

Yao, Weiguang; Farr, Jonathan B

2015-08-21

The Poisson or compound Poisson process governs the randomness of photon fluence in cone beam computed tomography (CBCT) imaging systems. The probability density function depends on the mean (noiseless) of the fluence at a certain detector. This dependence indicates the natural requirement of multiscale filters to smooth noise while preserving structures of the imaged object on the low-dose cone beam projection. In this work, we used a Gaussian filter, exp(-x2/2σ(2)(f)) as the multiscale filter to de-noise the low-dose cone beam projections. We analytically obtained the expression of σ(f), which represents the scale of the filter, by minimizing local noise-to-signal ratio. We analytically derived the variance of residual noise from the Poisson or compound Poisson processes after Gaussian filtering. From the derived analytical form of the variance of residual noise, optimal σ(2)(f)) is proved to be proportional to the noiseless fluence and modulated by local structure strength expressed as the linear fitting error of the structure. A strategy was used to obtain the reliable linear fitting error: smoothing the projection along the longitudinal direction to calculate the linear fitting error along the lateral direction and vice versa. The performance of our multiscale filter was examined on low-dose cone beam projections of a Catphan phantom and a head-and-neck patient. After performing the filter on the Catphan phantom projections scanned with pulse time 4 ms, the number of visible line pairs was similar to that scanned with 16 ms, and the contrast-to-noise ratio of the inserts was higher than that scanned with 16 ms about 64% in average. For the simulated head-and-neck patient projections with pulse time 4 ms, the visibility of soft tissue structures in the patient was comparable to that scanned with 20 ms. The image processing took less than 0.5 s per projection with 1024   ×   768 pixels.

All-digital GPS receiver mechanization

NASA Astrophysics Data System (ADS)

Ould, P. C.; van Wechel, R. J.

The paper describes the all-digital baseband correlation processing of GPS signals, which is characterized by (1) a potential for improved antijamming performance, (2) fast acquisition by a digital matched filter, (3) reduction of adjustment, (4) increased system reliability, and (5) provision of a basis for the realization of a high degree of VLSI potential for the development of small economical GPS sets. The basic technical approach consists of a broadband fix-tuned RF converter followed by a digitizer; digital-matched-filter acquisition section; phase- and delay-lock tracking via baseband digital correlation; software acquisition logic and loop filter implementation; and all-digital implementation of the feedback numerical controlled oscillators and code generator. Broadband in-phase and quadrature tracking is performed by an arctangent angle detector followed by a phase-unwrapping algorithm that eliminates false locks induced by sampling and data bit transitions, and yields a wide pull-in frequency range approaching one-fourth of the loop iteration frequency.

Optimality based repetitive controller design for track-following servo system of optical disk drives.

PubMed

Chen, Wentao; Zhang, Weidong

2009-10-01

In an optical disk drive servo system, to attenuate the external periodic disturbances induced by inevitable disk eccentricity, repetitive control has been used successfully. The performance of a repetitive controller greatly depends on the bandwidth of the low-pass filter included in the repetitive controller. However, owing to the plant uncertainty and system stability, it is difficult to maximize the bandwidth of the low-pass filter. In this paper, we propose an optimality based repetitive controller design method for the track-following servo system with norm-bounded uncertainties. By embedding a lead compensator in the repetitive controller, both the system gain at periodic signal's harmonics and the bandwidth of the low-pass filter are greatly increased. The optimal values of the repetitive controller's parameters are obtained by solving two optimization problems. Simulation and experimental results are provided to illustrate the effectiveness of the proposed method.

Fuzzy adaptive strong tracking scaled unscented Kalman filter for initial alignment of large misalignment angles

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.

Dynamic performance of maximum power point tracking circuits using sinusoidal extremum seeking control for photovoltaic generation

NASA Astrophysics Data System (ADS)

Leyva, R.; Artillan, P.; Cabal, C.; Estibals, B.; Alonso, C.

2011-04-01

The article studies the dynamic performance of a family of maximum power point tracking circuits used for photovoltaic generation. It revisits the sinusoidal extremum seeking control (ESC) technique which can be considered as a particular subgroup of the Perturb and Observe algorithms. The sinusoidal ESC technique consists of adding a small sinusoidal disturbance to the input and processing the perturbed output to drive the operating point at its maximum. The output processing involves a synchronous multiplication and a filtering stage. The filter instance determines the dynamic performance of the MPPT based on sinusoidal ESC principle. The approach uses the well-known root-locus method to give insight about damping degree and settlement time of maximum-seeking waveforms. This article shows the transient waveforms in three different filter instances to illustrate the approach. Finally, an experimental prototype corroborates the dynamic analysis.

Laser Doppler velocimeter system simulation for sensing aircraft wake vortices. Part 2: Processing and analysis of LDV data (for runs 1023 and 2023)

NASA Technical Reports Server (NTRS)

Meng, J. C. S.; Thomson, J. A. L.

1975-01-01

A data analysis program constructed to assess LDV system performance, to validate the simulation model, and to test various vortex location algorithms is presented. Real or simulated Doppler spectra versus range and elevation is used and the spatial distributions of various spectral moments or other spectral characteristics are calculated and displayed. Each of the real or simulated scans can be processed by one of three different procedures: simple frequency or wavenumber filtering, matched filtering, and deconvolution filtering. The final output is displayed as contour plots in an x-y coordinate system, as well as in the form of vortex tracks deduced from the maxima of the processed data. A detailed analysis of run number 1023 and run number 2023 is presented to demonstrate the data analysis procedure. Vortex tracks and system range resolutions are compared with theoretical predictions.

Combining Charge Couple Devices and Rate Sensors for the Feedforward Control System of a Charge Coupled Device Tracking Loop.

PubMed

Tang, Tao; Tian, Jing; Zhong, Daijun; Fu, Chengyu

2016-06-25

A rate feed forward control-based sensor fusion is proposed to improve the closed-loop performance for a charge couple device (CCD) tracking loop. The target trajectory is recovered by combining line of sight (LOS) errors from the CCD and the angular rate from a fiber-optic gyroscope (FOG). A Kalman filter based on the Singer acceleration model utilizes the reconstructive target trajectory to estimate the target velocity. Different from classical feed forward control, additive feedback loops are inevitably added to the original control loops due to the fact some closed-loop information is used. The transfer function of the Kalman filter in the frequency domain is built for analyzing the closed loop stability. The bandwidth of the Kalman filter is the major factor affecting the control stability and close-loop performance. Both simulations and experiments are provided to demonstrate the benefits of the proposed algorithm.

Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction

PubMed Central

Li, Zhencai; Wang, Yang; Liu, Zhen

2016-01-01

The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state–space NN, and the unscented Kalman filter is used to train NN’s weights online. The slip parameters can be estimated and used to predict the time series of deviation velocity, which can be used to compensate control inputs of a WMR. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model. PMID:27467703

Adaptive object tracking via both positive and negative models matching

NASA Astrophysics Data System (ADS)

Li, Shaomei; Gao, Chao; Wang, Yawen

2015-03-01

To improve tracking drift which often occurs in adaptive tracking, an algorithm based on the fusion of tracking and detection is proposed in this paper. Firstly, object tracking is posed as abinary classification problem and is modeled by partial least squares (PLS) analysis. Secondly, tracking object frame by frame via particle filtering. Thirdly, validating the tracking reliability based on both positive and negative models matching. Finally, relocating the object based on SIFT features matching and voting when drift occurs. Object appearance model is updated at the same time. The algorithm can not only sense tracking drift but also relocate the object whenever needed. Experimental results demonstrate that this algorithm outperforms state-of-the-art algorithms on many challenging sequences.

Tracking and prevalence of cardiovascular disease risk factors across socio-economic classes: a longitudinal substudy of the European Youth Heart Study.

PubMed

Kristensen, Peter L; Wedderkopp, Niels; Møller, Niels C; Andersen, Lars B; Bai, Charlotte N; Froberg, Karsten

2006-01-27

The highest prevalence of several cardiovascular disease risk factors including obesity, smoking and low physical activity level is observed in adults of low socioeconomic status. This study investigates whether tracking of body mass index and physical fitness from childhood to adolescence differs between groups of socioeconomic status. Furthermore the study investigates whether social class differences in the prevalence of overweight and low physical fitness exist or develop within the age range from childhood to adolescence. In all, 384 school children were followed for a period of six years (from third to ninth grade). Physical fitness was determined by a progressive maximal cycle ergometer test and the classification of overweight was based on body mass index cut-points proposed by the International Obesity Task Force. Socioeconomic status was defined according to The International Standard Classification of Occupation scheme. Moderate and moderately high tracking was observed for physical fitness and body mass index, respectively. No significant difference in tracking was observed between groups of socioeconomic status. A significant social gradient was observed in both the prevalence of overweight and low physical fitness in the 14-16-year-old adolescents, whereas at the age of 8-10 years, only the prevalence of low physical fitness showed a significant inverse relation to socioeconomic status. The odds of both developing and maintaining risk during the measurement period were estimated as bigger in the group of low socioeconomic status than in the group of high socioeconomic status, although differences were significant only with respect to the odds of developing overweight. The results indicate that the fundamental possibilities of predicting overweight and low physical fitness at an early point in time are the same for different groups of socio-economic status. Furthermore, the observed development of social inequalities in the absolute prevalence of overweight and low physical fitness underline the need for broad preventive efforts targeting children of low socioeconomic status in early childhood.

A comparison of error bounds for a nonlinear tracking system with detection probability Pd < 1.

PubMed

Tong, Huisi; Zhang, Hao; Meng, Huadong; Wang, Xiqin

2012-12-14

Error bounds for nonlinear filtering are very important for performance evaluation and sensor management. This paper presents a comparative study of three error bounds for tracking filtering, when the detection probability is less than unity. One of these bounds is the random finite set (RFS) bound, which is deduced within the framework of finite set statistics. The others, which are the information reduction factor (IRF) posterior Cramer-Rao lower bound (PCRLB) and enumeration method (ENUM) PCRLB are introduced within the framework of finite vector statistics. In this paper, we deduce two propositions and prove that the RFS bound is equal to the ENUM PCRLB, while it is tighter than the IRF PCRLB, when the target exists from the beginning to the end. Considering the disappearance of existing targets and the appearance of new targets, the RFS bound is tighter than both IRF PCRLB and ENUM PCRLB with time, by introducing the uncertainty of target existence. The theory is illustrated by two nonlinear tracking applications: ballistic object tracking and bearings-only tracking. The simulation studies confirm the theory and reveal the relationship among the three bounds.

A Comparison of Error Bounds for a Nonlinear Tracking System with Detection Probability Pd < 1

PubMed Central

Tong, Huisi; Zhang, Hao; Meng, Huadong; Wang, Xiqin

2012-01-01

Error bounds for nonlinear filtering are very important for performance evaluation and sensor management. This paper presents a comparative study of three error bounds for tracking filtering, when the detection probability is less than unity. One of these bounds is the random finite set (RFS) bound, which is deduced within the framework of finite set statistics. The others, which are the information reduction factor (IRF) posterior Cramer-Rao lower bound (PCRLB) and enumeration method (ENUM) PCRLB are introduced within the framework of finite vector statistics. In this paper, we deduce two propositions and prove that the RFS bound is equal to the ENUM PCRLB, while it is tighter than the IRF PCRLB, when the target exists from the beginning to the end. Considering the disappearance of existing targets and the appearance of new targets, the RFS bound is tighter than both IRF PCRLB and ENUM PCRLB with time, by introducing the uncertainty of target existence. The theory is illustrated by two nonlinear tracking applications: ballistic object tracking and bearings-only tracking. The simulation studies confirm the theory and reveal the relationship among the three bounds. PMID:23242274

Technical Note: Kinect V2 surface filtering during gantry motion for radiotherapy applications.

PubMed

Nazir, Souha; Rihana, Sandy; Visvikis, Dimitris; Fayad, Hadi

2018-04-01

In radiotherapy, the Kinect V2 camera, has recently received a lot of attention concerning many clinical applications including patient positioning, respiratory motion tracking, and collision detection during the radiotherapy delivery phase. However, issues associated with such applications are related to some materials and surfaces reflections generating an offset in depth measurements especially during gantry motion. This phenomenon appears in particular when the collimator surface is observed by the camera; resulting in erroneous depth measurements, not only in Kinect surfaces itself, but also as a large peak when extracting a 1D respiratory signal from these data. In this paper, we proposed filtering techniques to reduce the noise effect in the Kinect-based 1D respiratory signal, using a trend removal filter, and in associated 2D surfaces, using a temporal median filter. Filtering process was validated using a phantom, in order to simulate a patient undergoing radiotherapy treatment while having the ground truth. Our results indicate a better correlation between the reference respiratory signal and its corresponding filtered signal (Correlation coefficient of 0.76) than that of the nonfiltered signal (Correlation coefficient of 0.13). Furthermore, surface filtering results show a decrease in the mean square distance error (85%) between the reference and the measured point clouds. This work shows a significant noise compensation and surface restitution after surface filtering and therefore a potential use of the Kinect V2 camera for different radiotherapy-based applications, such as respiratory tracking and collision detection. © 2018 American Association of Physicists in Medicine.

Real-time localization of mobile device by filtering method for sensor fusion

NASA Astrophysics Data System (ADS)

Fuse, Takashi; Nagara, Keita

2017-06-01

Most of the applications with mobile devices require self-localization of the devices. GPS cannot be used in indoor environment, the positions of mobile devices are estimated autonomously by using IMU. Since the self-localization is based on IMU of low accuracy, and then the self-localization in indoor environment is still challenging. The selflocalization method using images have been developed, and the accuracy of the method is increasing. This paper develops the self-localization method without GPS in indoor environment by integrating sensors, such as IMU and cameras, on mobile devices simultaneously. The proposed method consists of observations, forecasting and filtering. The position and velocity of the mobile device are defined as a state vector. In the self-localization, observations correspond to observation data from IMU and camera (observation vector), forecasting to mobile device moving model (system model) and filtering to tracking method by inertial surveying and coplanarity condition and inverse depth model (observation model). Positions of a mobile device being tracked are estimated by system model (forecasting step), which are assumed as linearly moving model. Then estimated positions are optimized referring to the new observation data based on likelihood (filtering step). The optimization at filtering step corresponds to estimation of the maximum a posterior probability. Particle filter are utilized for the calculation through forecasting and filtering steps. The proposed method is applied to data acquired by mobile devices in indoor environment. Through the experiments, the high performance of the method is confirmed.

Do Fitness Apps Need Text Reminders? An Experiment Testing Goal-Setting Text Message Reminders to Promote Self-Monitoring.

PubMed

Liu, Shuang; Willoughby, Jessica F

2018-01-01

Fitness tracking apps have the potential to change unhealthy lifestyles, but users' lack of compliance is an issue. The current intervention examined the effectiveness of using goal-setting theory-based text message reminders to promote tracking activities on fitness apps. We conducted a 2-week experiment with pre- and post-tests with young adults (n = 50). Participants were randomly assigned to two groups-a goal-setting text message reminder group and a generic text message reminder group. Participants were asked to use a fitness tracking app to log physical activity and diet for the duration of the study. Participants who received goal-setting reminders logged significantly more physical activities than those who only received generic reminders. Further, participants who received goal-setting reminders liked the messages and showed significantly increased self-efficacy, awareness of personal goals, motivation, and intention to use the app. The study shows that incorporating goal-setting theory-based text message reminders can be useful to boost user compliance with self-monitoring fitness apps by reinforcing users' personal goals and enhancing cognitive factors associated with health behavior change.

Multi-camera real-time three-dimensional tracking of multiple flying animals

PubMed Central

Straw, Andrew D.; Branson, Kristin; Neumann, Titus R.; Dickinson, Michael H.

2011-01-01

Automated tracking of animal movement allows analyses that would not otherwise be possible by providing great quantities of data. The additional capability of tracking in real time—with minimal latency—opens up the experimental possibility of manipulating sensory feedback, thus allowing detailed explorations of the neural basis for control of behaviour. Here, we describe a system capable of tracking the three-dimensional position and body orientation of animals such as flies and birds. The system operates with less than 40 ms latency and can track multiple animals simultaneously. To achieve these results, a multi-target tracking algorithm was developed based on the extended Kalman filter and the nearest neighbour standard filter data association algorithm. In one implementation, an 11-camera system is capable of tracking three flies simultaneously at 60 frames per second using a gigabit network of nine standard Intel Pentium 4 and Core 2 Duo computers. This manuscript presents the rationale and details of the algorithms employed and shows three implementations of the system. An experiment was performed using the tracking system to measure the effect of visual contrast on the flight speed of Drosophila melanogaster. At low contrasts, speed is more variable and faster on average than at high contrasts. Thus, the system is already a useful tool to study the neurobiology and behaviour of freely flying animals. If combined with other techniques, such as ‘virtual reality’-type computer graphics or genetic manipulation, the tracking system would offer a powerful new way to investigate the biology of flying animals. PMID:20630879

40 CFR 721.10607 - Aliphatic diisocyanate, homopolymer, alkanol-blocked (generic).

Code of Federal Regulations, 2014 CFR

2014-07-01

...-purifying, tight-fitting half-face respirator equipped with N100 (if oil aerosols absent), R100, or P100 filters. (B) NIOSH-certified air-purifying, tight-fitting full-face respirator equipped with N100 (if oil...-certified powered air-purifying respirator equipped with a tight-fitting facepiece (either half-face or full...

Wideband FM Demodulation and Multirate Frequency Transformations

DTIC Science & Technology

2016-12-15

FM signals. 2.2.1 Adaptive Linear Predictive IF Tracking For a pure FM signal, the IF demodulation approach employing adaptive filters was proposed...desired signal. As summarized in [5], the prediction error filter is given by: E (z) = 1− L∑ l=1 goptl z −l, (8) 2 Approved for public release...assumption and the further assumption that the message signal remains es- sentially invariant over the sampling range of the linear prediction filter , we end

Cascade and parallel combination (CPC) of adaptive filters for estimating heart rate during intensive physical exercise from photoplethysmographic signal

PubMed Central

Islam, Mohammad Tariqul; Tanvir Ahmed, Sk.; Zabir, Ishmam; Shahnaz, Celia

2018-01-01

Photoplethysmographic (PPG) signal is getting popularity for monitoring heart rate in wearable devices because of simplicity of construction and low cost of the sensor. The task becomes very difficult due to the presence of various motion artefacts. In this study, an algorithm based on cascade and parallel combination (CPC) of adaptive filters is proposed in order to reduce the effect of motion artefacts. First, preliminary noise reduction is performed by averaging two channel PPG signals. Next in order to reduce the effect of motion artefacts, a cascaded filter structure consisting of three cascaded adaptive filter blocks is developed where three-channel accelerometer signals are used as references to motion artefacts. To further reduce the affect of noise, a scheme based on convex combination of two such cascaded adaptive noise cancelers is introduced, where two widely used adaptive filters namely recursive least squares and least mean squares filters are employed. Heart rates are estimated from the noise reduced PPG signal in spectral domain. Finally, an efficient heart rate tracking algorithm is designed based on the nature of the heart rate variability. The performance of the proposed CPC method is tested on a widely used public database. It is found that the proposed method offers very low estimation error and a smooth heart rate tracking with simple algorithmic approach. PMID:29515812

An analytical investigation of acquisition techniques and system integration studies for a radar aircraft guidance research facility, phase 2

NASA Technical Reports Server (NTRS)

Thompson, W. S.; Ruedger, W. H.

1973-01-01

A review of user requirements and updated instrumentation plans are presented for the aircraft tracking and guidance facility at NASA Wallops Station. User demand has increased as a result of new flight research programs; however, basic requirements remain the same as originally reported. Instrumentation plans remain essentially the same but with plans for up- and down-link telemetry more firm. With slippages in the laser acquisition schedule, added importance is placed on the FPS-16 radar as the primary tracking device until the laser is available. Limited simulation studies of a particular Kalman-type filter are also presented. These studies simulated the use of the filter in a helicopter guidance loop in a real-time mode. Disadvantages and limitations of this mode of operation are pointed out. Laser eyesafety calculations show that laser tracking of aircraft is readily feasible from the eyesafety viewpoint.

Improvement on Gabor order tracking and objective comparison with Vold Kalman filtering order tracking

NASA Astrophysics Data System (ADS)

Pan, Min-Chun; Liao, Shiu-Wei; Chiu, Chun-Chin

2007-02-01

The waveform-reconstruction schemes of order tracking (OT) such as the Gabor and the Vold-Kalman filtering (VKF) techniques can extract specific order and/or spectral components in addition to characterizing the processed signal in rpm-frequency domain. The study first improves the Gabor OT (GOT) technique to handle the order-crossing problem, and then objectively compares the features of the improved GOT scheme and the angular-displacement VKF OT technique. It is numerically observed the improved method performs less accurately than the VKF_OT scheme at the crossing occurrences, but without end effect in the reconstructed waveform. As OT is not exact science, it may well be that the decrease in computation time can justify the reduced accuracy. The characterisation and discrimination of riding noise with crossing orders emitted by an electrical scooter are conducted as an example of the application.

A novel method to recover DD fusion proton CR-39 data corrupted by fast ablator ions at OMEGA and the National Ignition Facility

NASA Astrophysics Data System (ADS)

Sutcliffe, G. D.; Milanese, L. M.; Orozco, D.; Lahmann, B.; Gatu Johnson, M.; Séguin, F. H.; Sio, H.; Frenje, J. A.; Li, C. K.; Petrasso, R. D.; Park, H.-S.; Rygg, J. R.; Casey, D. T.; Bionta, R.; Turnbull, D. P.; Huntington, C. M.; Ross, J. S.; Zylstra, A. B.; Rosenberg, M. J.; Glebov, V. Yu.

2016-11-01

CR-39 detectors are used routinely in inertial confinement fusion (ICF) experiments as a part of nuclear diagnostics. CR-39 is filtered to stop fast ablator ions which have been accelerated from an ICF implosion due to electric fields caused by laser-plasma interactions. In some experiments, the filtering is insufficient to block these ions and the fusion-product signal tracks are lost in the large background of accelerated ion tracks. A technique for recovering signal in these scenarios has been developed, tested, and implemented successfully. The technique involves removing material from the surface of the CR-39 to a depth beyond the endpoint of the ablator ion tracks. The technique preserves signal magnitude (yield) as well as structure in radiograph images. The technique is effective when signal particle range is at least 10 μm deeper than the necessary bulk material removal.

A novel method to recover DD fusion proton CR-39 data corrupted by fast ablator ions at OMEGA and the National Ignition Facility.

PubMed

Sutcliffe, G D; Milanese, L M; Orozco, D; Lahmann, B; Gatu Johnson, M; Séguin, F H; Sio, H; Frenje, J A; Li, C K; Petrasso, R D; Park, H-S; Rygg, J R; Casey, D T; Bionta, R; Turnbull, D P; Huntington, C M; Ross, J S; Zylstra, A B; Rosenberg, M J; Glebov, V Yu

2016-11-01

CR-39 detectors are used routinely in inertial confinement fusion (ICF) experiments as a part of nuclear diagnostics. CR-39 is filtered to stop fast ablator ions which have been accelerated from an ICF implosion due to electric fields caused by laser-plasma interactions. In some experiments, the filtering is insufficient to block these ions and the fusion-product signal tracks are lost in the large background of accelerated ion tracks. A technique for recovering signal in these scenarios has been developed, tested, and implemented successfully. The technique involves removing material from the surface of the CR-39 to a depth beyond the endpoint of the ablator ion tracks. The technique preserves signal magnitude (yield) as well as structure in radiograph images. The technique is effective when signal particle range is at least 10 μm deeper than the necessary bulk material removal.

Real-time optical multiple object recognition and tracking system and method

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin (Inventor); Liu, Hua-Kuang (Inventor)

1990-01-01

System for optically recognizing and tracking a plurality of objects within a field of vision. Laser (46) produces a coherent beam (48). Beam splitter (24) splits the beam into object (26) and reference (28) beams. Beam expanders (50) and collimators (52) transform the beams (26, 28) into coherent collimated light beams (26', 28'). A two-dimensional SLM (54), disposed in the object beam (26'), modulates the object beam with optical information as a function of signals from a first camera (16) which develops X and Y signals reflecting the contents of its field of vision. A hololens (38), positioned in the object beam (26') subsequent to the modulator (54), focuses the object beam at a plurality of focal points (42). A planar transparency-forming film (32), disposed with the focal points on an exposable surface, forms a multiple position interference filter (62) upon exposure of the surface and development processing of the film (32). A reflector (53) directing the reference beam (28') onto the film (32), exposes the surface, with images focused by the hololens (38), to form interference patterns on the surface. There is apparatus (16', 64) for sensing and indicating light passage through respective ones of the positions of the filter (62), whereby recognition of objects corresponding to respective ones of the positions of the filter (62) is affected. For tracking, apparatus (64) focuses light passing through the filter (62) onto a matrix of CCD's in a second camera (16') to form a two-dimensional display of the recognized objects.

Graph-based geometric-iconic guide-wire tracking.

PubMed

Honnorat, Nicolas; Vaillant, Régis; Paragios, Nikos

2011-01-01

In this paper we introduce a novel hybrid graph-based approach for Guide-wire tracking. The image support is captured by steerable filters and improved through tensor voting. Then, a graphical model is considered that represents guide-wire extraction/tracking through a B-spline control-point model. Points with strong geometric interest (landmarks) are automatically determined and anchored to such a representation. Tracking is then performed through discrete MRFs that optimize the spatio-temporal positions of the control points while establishing landmark temporal correspondences. Promising results demonstrate the potentials of our method.

Effect of fit testing on the protection offered by n95 filtering facepiece respirators against fine particles in a laboratory setting.

PubMed

Reponen, Tiina; Lee, Shu-An; Grinshpun, Sergey A; Johnson, Erik; McKay, Roy

2011-04-01

This study investigated particle-size-selective protection factors (PFs) of four models of N95 filtering facepiece respirators (FFRs) that passed and failed fit testing. Particle size ranges were representative of individual viruses and bacteria (aerodynamic diameter d(a) = 0.04-1.3 μm). Standard respirator fit testing was followed by particle-size-selective measurement of PFs while subjects wore N95 FFRs in a test chamber. PF values obtained for all subjects were then compared to those obtained for the subjects who passed the fit testing. Overall fit test passing rate for all four models of FFRs was 67%. Of these, 29% had PFs <10 (the Occupational Safety and Health Administration Assigned Protection Factor designated for this type of respirator). When only subjects that passed fit testing were included, PFs improved with 9% having values <10. On average, the PFs were 1.4 times (29.5/21.5) higher when only data for those who passed fit testing were included. The minimum PFs were consistently observed in the particle size range of 0.08-0.2 μm. Overall PFs increased when subjects passed fit testing. The results support the value of fit testing but also show for the first time that PFs are dependent on particle size regardless of fit testing status.

BUPT_PRIS at TREC 2014 Knowledge Base Acceleration Track

DTIC Science & Technology

2014-11-01

BUPT_PRIS at TREC 2014 Knowledge Base Acceleration Track Yuanyuan Qi, Ye Xu, Dongxu Zhang, Weiran Xu qiyuanyuan@bupt.edu.cn,bob.ye.xu@gmail.com...Filtering for Entity Profile Updates for TREC 2013 [2]Yan Li, Zhaozhao Wang , Baojin Yu, Yong Zhang, Ruiyang Luo,Weiran Xu, Guang Chen, Jun Guo. PRIS

Peak impact accelerations during track and treadmill running.

PubMed

Bigelow, Erin M R; Elvin, Niell G; Elvin, Alex A; Arnoczky, Steven P

2013-10-01

To determine whether peak vertical and horizontal impact accelerations were different while running on a track or on a treadmill, 12 healthy subjects (average age 32.8 ± 9.8 y), were fitted with a novel, wireless accelerometer capable of recording triaxial acceleration over time. The accelerometer was attached to a custom-made acrylic plate and secured at the level of the L5 vertebra via a tight fitting triathlon belt. Each subject ran 4 miles on a synthetic, indoor track at a self-selected pace and accelerations were recorded on three perpendicular axes. Seven days later, the subjects ran 4 miles on a treadmill set at the individual runner's average pace on the track and the peak vertical and horizontal impact magnitudes between the track and treadmill were compared. There was no difference (P = .52) in the average peak vertical impact accelerations between the track and treadmill over the 4 mile run. However, peak horizontal impact accelerations were greater (P = .0012) on the track when compared with the treadmill. This study demonstrated the feasibility for long-term impact accelerations monitoring using a novel wireless accelerometer.

Motion-compensated speckle tracking via particle filtering

NASA Astrophysics Data System (ADS)

Liu, Lixin; Yagi, Shin-ichi; Bian, Hongyu

2015-07-01

Recently, an improved motion compensation method that uses the sum of absolute differences (SAD) has been applied to frame persistence utilized in conventional ultrasonic imaging because of its high accuracy and relative simplicity in implementation. However, high time consumption is still a significant drawback of this space-domain method. To seek for a more accelerated motion compensation method and verify if it is possible to eliminate conventional traversal correlation, motion-compensated speckle tracking between two temporally adjacent B-mode frames based on particle filtering is discussed. The optimal initial density of particles, the least number of iterations, and the optimal transition radius of the second iteration are analyzed from simulation results for the sake of evaluating the proposed method quantitatively. The speckle tracking results obtained using the optimized parameters indicate that the proposed method is capable of tracking the micromotion of speckle throughout the region of interest (ROI) that is superposed with global motion. The computational cost of the proposed method is reduced by 25% compared with that of the previous algorithm and further improvement is necessary.

MO-FG-CAMPUS-IeP1-04: Kerma Area Product Calculation for Non-Uniform X-Ray Fields Using a Skin Dose Tracking System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vijayan, S; Xiong, Z; Rudin, S

Purpose: The functionality of the Dose-Tracking System (DTS) has been expanded to include the calculation of the Kerma-Area Product (KAP) for non-uniform x-ray fields such as result from the use of compensation filters during fluoroscopic procedures Methods: The DTS calculates skin dose during fluoroscopic interventions and provides a color-coded dose map on a patient-graphic model. The KAP is the integral of air kerma over the x-ray field and is usually measured with a transmission-ionization chamber that intercepts the entire x-ray beam. The DTS has been modified to determine KAP when there are beam non-uniformities that can be modeled. For example,more » the DTS includes models of the three compensation filters with tapered edges located in the collimator assembly of the Toshiba Infinix fluoroscopic C-Arm and can track their movement. To determine the air kerma after the filters, DTS includes transmission factors for the compensation filters as a function of kVp and beam filtration. A virtual KAP dosimeter is simulated in the DTS by an array of graphic vertices; the air kerma at each vertex is corrected by the field non-uniformity, which in this case is the attenuation factor for those rays which pass through the filter. The products of individual vertex air-kerma values for all vertices within the beam times the effective-area-per-vertex are summed for each x-ray pulse to yield the KAP per pulse and the cumulative KAP for the procedure is then calculated. Results: The KAP values estimated by DTS with the compensation filter inserted into the x-ray field agree within ± 6% with the values displayed on the fluoroscopy unit monitor, which are measured with a transmission chamber. Conclusion: The DTS can account for field non-uniformities such as result from the use of compensation filters in calculating KAP and can obviate the need for a KAP transmission ionization chamber. Partial support from NIH Grant R01-EB002873 and Toshiba Medical Systems Corp.« less

A comparison of wearable fitness devices.

PubMed

Kaewkannate, Kanitthika; Kim, Soochan

2016-05-24

Wearable trackers can help motivate you during workouts and provide information about your daily routine or fitness in combination with your smartphone without requiring potentially disruptive manual calculations or records. This paper summarizes and compares wearable fitness devices, also called "fitness trackers" or "activity trackers." These devices are becoming increasingly popular in personal healthcare, motivating people to exercise more throughout the day without the need for lifestyle changes. The various choices in the market for wearable devices are also increasing, with customers searching for products that best suit their personal needs. Further, using a wearable device or fitness tracker can help people reach a fitness goal or finish line. Generally, companies display advertising for these kinds of products and depict them as beneficial, user friendly, and accurate. However, there are no objective research results to prove the veracity of their words. This research features subjective and objective experimental results, which reveal that some devices perform better than others. The four most popular wristband style wearable devices currently on the market (Withings Pulse, Misfit Shine, Jawbone Up24, and Fitbit Flex) are selected and compared. The accuracy of fitness tracking is one of the key components for fitness tracking, and some devices perform better than others. This research shows subjective and objective experimental results that are used to compare the accuracy of four wearable devices in conjunction with user friendliness and satisfaction of 7 real users. In addition, this research matches the opinions between reviewers on an Internet site and those of subjects when using the device. Withings Pulse is the most friendly and satisfactory from the users' viewpoint. It is the most accurate and repeatable for step and distance tracking, which is the most important measurement of fitness tracking, followed by Fitbit Flex, Jawbone Up24, and Misfit Shine. In contrast, Misfit Shine has the highest score for design and hardware, which is also appreciated by users. From the results of experiments on four wearable devices, it is determined that the most acceptable in terms of price and satisfaction levels is the Withings Pulse, followed by the Fitbit Flex, Jawbone Up24, and Misfit Shine.

Fitness Tracker for Weight Lifting Style Workouts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wihl, B. M.

This document proposes an early, high level design for a fitness tracking system which can automatically log weight lifting style workouts. The system will provide an easy to use interface both physically through the use of several wireless wristband style motion trackers worn on the limbs, and graphically through a smartphone application. Exercise classification will be accomplished by calibration of the user’s specific motions. The system will accurately track a user’s workout, miscounting no more than one repetition in every 20, have sufficient battery life to last several hours, work with existing smartphones and have a cost similar to thosemore » of current fitness tracking devices. This document presents the mission background, current state-of-theart, stakeholders and their expectations, the proposed system’s context and concepts, implementation concepts, system requirements, first sublevel function decomposition, possible risks for the system, and a reflection on the design process.« less

Evaluation of I-FIT results and machine variability using MnRoad test track mixtures.

DOT National Transportation Integrated Search

2017-06-01

The Illinois Flexibility Index Test (I-FIT) was developed to distinguish between different mixtures in terms of potential cracking. Several : machines were manufactured and are currently available to perform the I-FIT. This report presents the result...

Image-based topology for sensor gridlocking and association

NASA Astrophysics Data System (ADS)

Stanek, Clay J.; Javidi, Bahram; Yanni, Philip

2002-07-01

Correlation engines have been evolving since the implementation of radar. In modern sensor fusion architectures, correlation and gridlock filtering are required to produce common, continuous, and unambiguous tracks of all objects in the surveillance area. The objective is to provide a unified picture of the theatre or area of interest to battlefield decision makers, ultimately enabling them to make better inferences for future action and eliminate fratricide by reducing ambiguities. Here, correlation refers to association, which in this context is track-to-track association. A related process, gridlock filtering or gridlocking, refers to the reduction in navigation errors and sensor misalignment errors so that one sensor's track data can be accurately transformed into another sensor's coordinate system. As platforms gain multiple sensors, the correlation and gridlocking of tracks become significantly more difficult. Much of the existing correlation technology revolves around various interpretations of the generalized Bayesian decision rule: choose the action that minimizes conditional risk. One implementation of this principle equates the risk minimization statement to the comparison of ratios of a priori probability distributions to thresholds. The binary decision problem phrased in terms of likelihood ratios is also known as the famed Neyman-Pearson hypothesis test. Using another restatement of the principle for a symmetric loss function, risk minimization leads to a decision that maximizes the a posteriori probability distribution. Even for deterministic decision rules, situations can arise in correlation where there are ambiguities. For these situations, a common algorithm used is a sparse assignment technique such as the Munkres or JVC algorithm. Furthermore, associated tracks may be combined with the hope of reducing the positional uncertainty of a target or object identified by an existing track from the information of several fused/correlated tracks. Gridlocking is typically accomplished with some type of least-squares algorithm, such as the Kalman filtering technique, which attempts to locate the best bias error vector estimate from a set of correlated/fused track pairs. Here, we will introduce a new approach to this longstanding problem by adapting many of the familiar concepts from pattern recognition, ones certainly familiar to target recognition applications. Furthermore, we will show how this technique can lend itself to specialized processing, such as that available through an optical or hybrid correlator.

Using dual-energy x-ray imaging to enhance automated lung tumor tracking during real-time adaptive radiotherapy.

PubMed

Menten, Martin J; Fast, Martin F; Nill, Simeon; Oelfke, Uwe

2015-12-01

Real-time, markerless localization of lung tumors with kV imaging is often inhibited by ribs obscuring the tumor and poor soft-tissue contrast. This study investigates the use of dual-energy imaging, which can generate radiographs with reduced bone visibility, to enhance automated lung tumor tracking for real-time adaptive radiotherapy. kV images of an anthropomorphic breathing chest phantom were experimentally acquired and radiographs of actual lung cancer patients were Monte-Carlo-simulated at three imaging settings: low-energy (70 kVp, 1.5 mAs), high-energy (140 kVp, 2.5 mAs, 1 mm additional tin filtration), and clinical (120 kVp, 0.25 mAs). Regular dual-energy images were calculated by weighted logarithmic subtraction of high- and low-energy images and filter-free dual-energy images were generated from clinical and low-energy radiographs. The weighting factor to calculate the dual-energy images was determined by means of a novel objective score. The usefulness of dual-energy imaging for real-time tracking with an automated template matching algorithm was investigated. Regular dual-energy imaging was able to increase tracking accuracy in left-right images of the anthropomorphic phantom as well as in 7 out of 24 investigated patient cases. Tracking accuracy remained comparable in three cases and decreased in five cases. Filter-free dual-energy imaging was only able to increase accuracy in 2 out of 24 cases. In four cases no change in accuracy was observed and tracking accuracy worsened in nine cases. In 9 out of 24 cases, it was not possible to define a tracking template due to poor soft-tissue contrast regardless of input images. The mean localization errors using clinical, regular dual-energy, and filter-free dual-energy radiographs were 3.85, 3.32, and 5.24 mm, respectively. Tracking success was dependent on tumor position, tumor size, imaging beam angle, and patient size. This study has highlighted the influence of patient anatomy on the success rate of real-time markerless tumor tracking using dual-energy imaging. Additionally, the importance of the spectral separation of the imaging beams used to generate the dual-energy images has been shown.

Using dual-energy x-ray imaging to enhance automated lung tumor tracking during real-time adaptive radiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Menten, Martin J., E-mail: martin.menten@icr.ac.uk; Fast, Martin F.; Nill, Simeon

2015-12-15

Purpose: Real-time, markerless localization of lung tumors with kV imaging is often inhibited by ribs obscuring the tumor and poor soft-tissue contrast. This study investigates the use of dual-energy imaging, which can generate radiographs with reduced bone visibility, to enhance automated lung tumor tracking for real-time adaptive radiotherapy. Methods: kV images of an anthropomorphic breathing chest phantom were experimentally acquired and radiographs of actual lung cancer patients were Monte-Carlo-simulated at three imaging settings: low-energy (70 kVp, 1.5 mAs), high-energy (140 kVp, 2.5 mAs, 1 mm additional tin filtration), and clinical (120 kVp, 0.25 mAs). Regular dual-energy images were calculated bymore » weighted logarithmic subtraction of high- and low-energy images and filter-free dual-energy images were generated from clinical and low-energy radiographs. The weighting factor to calculate the dual-energy images was determined by means of a novel objective score. The usefulness of dual-energy imaging for real-time tracking with an automated template matching algorithm was investigated. Results: Regular dual-energy imaging was able to increase tracking accuracy in left–right images of the anthropomorphic phantom as well as in 7 out of 24 investigated patient cases. Tracking accuracy remained comparable in three cases and decreased in five cases. Filter-free dual-energy imaging was only able to increase accuracy in 2 out of 24 cases. In four cases no change in accuracy was observed and tracking accuracy worsened in nine cases. In 9 out of 24 cases, it was not possible to define a tracking template due to poor soft-tissue contrast regardless of input images. The mean localization errors using clinical, regular dual-energy, and filter-free dual-energy radiographs were 3.85, 3.32, and 5.24 mm, respectively. Tracking success was dependent on tumor position, tumor size, imaging beam angle, and patient size. Conclusions: This study has highlighted the influence of patient anatomy on the success rate of real-time markerless tumor tracking using dual-energy imaging. Additionally, the importance of the spectral separation of the imaging beams used to generate the dual-energy images has been shown.« less

Active disturbance rejection controller of fine tracking system for free space optical communication

NASA Astrophysics Data System (ADS)

Cui, Ning; Liu, Yang; Chen, Xinglin; Wang, Yan

2013-08-01

Free space optical communication is one of the best approaches in future communications. Laser beam's acquisition, pointing and tracking are crucial technologies of free space optical communication. Fine tracking system is important component of APT (acquisition, pointing and tracking) system. It cooperates with the coarse pointing system in executing the APT mission. Satellite platform vibration and disturbance, which reduce received optical power, increase bit error rate and affect seriously the natural performance of laser communication. For the characteristic of satellite platform, an active disturbance rejection controller was designed to reduce the vibration and disturbance. There are three major contributions in the paper. Firstly, the effects of vibration on the inter satellite optical communications were analyzed, and the reasons and characters of vibration of the satellite platform were summarized. The amplitude-frequency response of a filter was designed according to the power spectral density of platform vibration of SILEX (Semiconductor Inter-satellite Laser Experiment), and then the signals of platform vibration were generated by filtering white Gaussian noise using the filter. Secondly, the fast steering mirror is a key component of the fine tracking system for optical communication. The mechanical design and model analysis was made to the tip/tilt mirror driven by the piezoelectric actuator and transmitted by the flexure hinge. The transfer function of the fast steering mirror, camera, D/A data acquisition card was established, and the theory model of transfer function of this system was further obtained. Finally, an active disturbance rejection control method is developed, multiple parallel extended state observers were designed for estimation of unknown dynamics and external disturbance, and the estimated states were used for nonlinear feedback control and compensation to improve system performance. The simulation results show that the designed controller not only accurately estimates and compensates the disturbances, but also realizes the robustness to estimation of unknown dynamics. The controller can satisfy the requirement of fine tracking accuracy for free space optical communication system.

40 CFR 721.10572 - Benzamide, N-[[4- [(cyclopropylamino)carbonyl] phenyl]sulfonyl]-2-methoxy-.

Code of Federal Regulations, 2014 CFR

2014-07-01

...-fitting half-face respirator equipped with N100 (if oil aerosols absent), R100, or P100 filters. (B) NIOSH-certified air-purifying, tight-fitting full-face respirator equipped with N100 (if oil aerosols absent...-purifying respirator equipped with a tight-fitting facepiece (either half-face or full-face) and HEPA...

40 CFR 721.10581 - Brominated polyurethane prepolymers of methylene diphenyl diisocyanate (MDI) (generic).

Code of Federal Regulations, 2014 CFR

2014-07-01

..., tight-fitting half-face respirator equipped with N100 (if oil aerosols absent), R100, or P100 filters. (B) NIOSH-certified air-purifying, tight-fitting full-face respirator equipped with N100 (if oil...-certified powered air-purifying respirator equipped with a tight-fitting facepiece (either half-face or full...

Instantaneous and Frequency-Warped Signal Processing Techniques for Auditory Source Separation.

NASA Astrophysics Data System (ADS)

Wang, Avery Li-Chun

This thesis summarizes several contributions to the areas of signal processing and auditory source separation. The philosophy of Frequency-Warped Signal Processing is introduced as a means for separating the AM and FM contributions to the bandwidth of a complex-valued, frequency-varying sinusoid p (n), transforming it into a signal with slowly-varying parameters. This transformation facilitates the removal of p (n) from an additive mixture while minimizing the amount of damage done to other signal components. The average winding rate of a complex-valued phasor is explored as an estimate of the instantaneous frequency. Theorems are provided showing the robustness of this measure. To implement frequency tracking, a Frequency-Locked Loop algorithm is introduced which uses the complex winding error to update its frequency estimate. The input signal is dynamically demodulated and filtered to extract the envelope. This envelope may then be remodulated to reconstruct the target partial, which may be subtracted from the original signal mixture to yield a new, quickly-adapting form of notch filtering. Enhancements to the basic tracker are made which, under certain conditions, attain the Cramer -Rao bound for the instantaneous frequency estimate. To improve tracking, the novel idea of Harmonic -Locked Loop tracking, using N harmonically constrained trackers, is introduced for tracking signals, such as voices and certain musical instruments. The estimated fundamental frequency is computed from a maximum-likelihood weighting of the N tracking estimates, making it highly robust. The result is that harmonic signals, such as voices, can be isolated from complex mixtures in the presence of other spectrally overlapping signals. Additionally, since phase information is preserved, the resynthesized harmonic signals may be removed from the original mixtures with relatively little damage to the residual signal. Finally, a new methodology is given for designing linear-phase FIR filters which require a small fraction of the computational power of conventional FIR implementations. This design strategy is based on truncated and stabilized IIR filters. These signal-processing methods have been applied to the problem of auditory source separation, resulting in voice separation from complex music that is significantly better than previous results at far lower computational cost.

Segmentation of Nerve Bundles and Ganglia in Spine MRI Using Particle Filters

PubMed Central

Dalca, Adrian; Danagoulian, Giovanna; Kikinis, Ron; Schmidt, Ehud; Golland, Polina

2011-01-01

Automatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bézier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation. PMID:22003741

Segmentation of nerve bundles and ganglia in spine MRI using particle filters.

PubMed

Dalca, Adrian; Danagoulian, Giovanna; Kikinis, Ron; Schmidt, Ehud; Golland, Polina

2011-01-01

Automatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on Bézier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation.

Health hazards associated with the use of di-(2-ethylhexyl) phthalate (commonly referred to as DOP) in HEPA filter test

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1995-01-01

Di-(2-ethylhexyl) phthalate (DEHP), commonly referred to as di-octyl phthalate, is an important production chemical in the US. In addition to its major use as an additive in plastics, DEHP is widely used to evaluate the effectiveness of high efficiency particulate air (HEPA) filters. Historically, DEHP was also used in quantitative fit testing for respirators. Evaluations of this compound a decade ago showed that it can induce hepatocellular carcinomas in laboratory animals. Although most Department of Energy (DOE) facilities have since discontinued using DEHP in respirator fit testing, DEHP continues to be used for evaluating HEPA filters. This report summarizes availablemore » information on the toxicity, mutagenicity, carcinogenicity, and other hazards and problems posed by DEHP, specifically with reference to HEPA filter testing. Information on work practice improvements as well as the availability and suitability of DEHP substitutes are also presented. This material should assist the DOE in the safe use of this material.« less

Improved electromagnetic tracking for catheter path reconstruction with application in high-dose-rate brachytherapy.

PubMed

Lugez, Elodie; Sadjadi, Hossein; Joshi, Chandra P; Akl, Selim G; Fichtinger, Gabor

2017-04-01

Electromagnetic (EM) catheter tracking has recently been introduced in order to enable prompt and uncomplicated reconstruction of catheter paths in various clinical interventions. However, EM tracking is prone to measurement errors which can compromise the outcome of the procedure. Minimizing catheter tracking errors is therefore paramount to improve the path reconstruction accuracy. An extended Kalman filter (EKF) was employed to combine the nonlinear kinematic model of an EM sensor inside the catheter, with both its position and orientation measurements. The formulation of the kinematic model was based on the nonholonomic motion constraints of the EM sensor inside the catheter. Experimental verification was carried out in a clinical HDR suite. Ten catheters were inserted with mean curvatures varying from 0 to [Formula: see text] in a phantom. A miniaturized Ascension (Burlington, Vermont, USA) trakSTAR EM sensor (model 55) was threaded within each catheter at various speeds ranging from 7.4 to [Formula: see text]. The nonholonomic EKF was applied on the tracking data in order to statistically improve the EM tracking accuracy. A sample reconstruction error was defined at each point as the Euclidean distance between the estimated EM measurement and its corresponding ground truth. A path reconstruction accuracy was defined as the root mean square of the sample reconstruction errors, while the path reconstruction precision was defined as the standard deviation of these sample reconstruction errors. The impacts of sensor velocity and path curvature on the nonholonomic EKF method were determined. Finally, the nonholonomic EKF catheter path reconstructions were compared with the reconstructions provided by the manufacturer's filters under default settings, namely the AC wide notch and the DC adaptive filter. With a path reconstruction accuracy of 1.9 mm, the nonholonomic EKF surpassed the performance of the manufacturer's filters (2.4 mm) by 21% and the raw EM measurements (3.5 mm) by 46%. Similarly, with a path reconstruction precision of 0.8 mm, the nonholonomic EKF surpassed the performance of the manufacturer's filters (1.0 mm) by 20% and the raw EM measurements (1.7 mm) by 53%. Path reconstruction accuracies did not follow an apparent trend when varying the path curvature and sensor velocity; instead, reconstruction accuracies were predominantly impacted by the position of the EM field transmitter ([Formula: see text]). The advanced nonholonomic EKF is effective in reducing EM measurement errors when reconstructing catheter paths, is robust to path curvature and sensor speed, and runs in real time. Our approach is promising for a plurality of clinical procedures requiring catheter reconstructions, such as cardiovascular interventions, pulmonary applications (Bender et al. in medical image computing and computer-assisted intervention-MICCAI 99. Springer, Berlin, pp 981-989, 1999), and brachytherapy.

LATTE Linking Acoustic Tests and Tagging Using Statistical Estimation

DTIC Science & Technology

2015-09-30

the complexity of the model: (from simplest to most complex) Kalman filter , Markov chain Monte-Carlo (MCMC) and ABC. Many of these methods have been...using SMMs fitted using Kalman filters . Therefore, using the DTAG data, we can estimate the distributions associated with 2D horizontal displacement...speed (a key problem in the previous Kalman filter implementation). This new approach also allows the animal’s horizontal movement direction to differ

Underwater terrain-aided navigation system based on combination matching algorithm.

PubMed

Li, Peijuan; Sheng, Guoliang; Zhang, Xiaofei; Wu, Jingqiu; Xu, Baochun; Liu, Xing; Zhang, Yao

2018-07-01

Considering that the terrain-aided navigation (TAN) system based on iterated closest contour point (ICCP) algorithm diverges easily when the indicative track of strapdown inertial navigation system (SINS) is large, Kalman filter is adopted in the traditional ICCP algorithm, difference between matching result and SINS output is used as the measurement of Kalman filter, then the cumulative error of the SINS is corrected in time by filter feedback correction, and the indicative track used in ICCP is improved. The mathematic model of the autonomous underwater vehicle (AUV) integrated into the navigation system and the observation model of TAN is built. Proper matching point number is designated by comparing the simulation results of matching time and matching precision. Simulation experiments are carried out according to the ICCP algorithm and the mathematic model. It can be concluded from the simulation experiments that the navigation accuracy and stability are improved with the proposed combinational algorithm in case that proper matching point number is engaged. It will be shown that the integrated navigation system is effective in prohibiting the divergence of the indicative track and can meet the requirements of underwater, long-term and high precision of the navigation system for autonomous underwater vehicles. Copyright © 2017. Published by Elsevier Ltd.

Tracking with time-delayed data in multisensor systems

NASA Astrophysics Data System (ADS)

Hilton, Richard D.; Martin, David A.; Blair, William D.

1993-08-01

When techniques for target tracking are expanded to make use of multiple sensors in a multiplatform system, the possibility of time delayed data becomes a reality. When a discrete-time Kalman filter is applied and some of the data entering the filter are delayed, proper processing of these late data is a necessity for obtaining an optimal estimate of a target's state. If this problem is not given special care, the quality of the state estimates can be degraded relative to that quality provided by a single sensor. A negative-time update technique is developed using the criteria of minimum mean-square error (MMSE) under the constraint that only the results of the most recent update are saved. The performance of the MMSE technique is compared to that of the ad hoc approach employed in the Cooperative Engagement Capabilities (CEC) system for processing data from multiple platforms. It was discovered that the MMSE technique is a stable solution to the negative-time update problem, while the CEC technique was found to be less than desirable when used with filters designed for tracking highly maneuvering targets at relatively low data rates. The MMSE negative-time update technique was found to be a superior alternative to the existing CEC negative-time update technique.

Electrons and photons at High Level Trigger in CMS for Run II

NASA Astrophysics Data System (ADS)

Anuar, Afiq A.

2015-12-01

The CMS experiment has been designed with a 2-level trigger system. The first level is implemented using custom-designed electronics. The second level is the so-called High Level Trigger (HLT), a streamlined version of the CMS offline reconstruction software running on a computer farm. For Run II of the Large Hadron Collider, the increase in center-of-mass energy and luminosity will raise the event rate to a level challenging for the HLT algorithms. New approaches have been studied to keep the HLT output rate manageable while maintaining thresholds low enough to cover physics analyses. The strategy mainly relies on porting online the ingredients that have been successfully applied in the offline reconstruction, thus allowing to move HLT selection closer to offline cuts. Improvements in HLT electron and photon definitions will be presented, focusing in particular on: updated clustering algorithm and the energy calibration procedure, new Particle-Flow-based isolation approach and pileup mitigation techniques, and the electron-dedicated track fitting algorithm based on Gaussian Sum Filter.

Picture of the global field of quasi-monochromatic gravity waves observed by stratospheric balloons and MST radars

NASA Technical Reports Server (NTRS)

Yamanaka, M. D.

1989-01-01

In MAP observations, it was found that: (1) gravity waves in selected or filtered portions of data are fit for monochromatic structures, whereas (2) those in fully continuous and resolved observations take universal continuous spectra. It is possible to explain (2) by dispersion of quasi-monochromatic (or slowly varying) wave packets observed locally as (1), since the medium atmosphere is unsteady and nonuniform. Complete verification of the wave-mean flow interactions by tracking individual wave packets seems hopeless, because the wave induced flow cannot be distinguished from the basic flow independent of the waves. Instead, the primitive picture is looked at before MAP, that is, the atmosphere is just like an entertainment stage illuminated by cocktail lights of quasi-monochromatic gravity waves. The wave parameters are regarded as functions of time and spatial coordinates. The observational evidences (1) and (2) suggest that the wave parameter field is rather homogeneous, which can be explained by interference of quasi-monochromatic wave packets.

Bayesian investigation of isochrone consistency using the old open cluster NGC 188

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hills, Shane; Courteau, Stéphane; Von Hippel, Ted

2015-03-01

This paper provides a detailed comparison of the differences in parameters derived for a star cluster from its color–magnitude diagrams (CMDs) depending on the filters and models used. We examine the consistency and reliability of fitting three widely used stellar evolution models to 15 combinations of optical and near-IR photometry for the old open cluster NGC 188. The optical filter response curves match those of theoretical systems and are thus not the source of fit inconsistencies. NGC 188 is ideally suited to this study thanks to a wide variety of high-quality photometry and available proper motions and radial velocities thatmore » enable us to remove non-cluster members and many binaries. Our Bayesian fitting technique yields inferred values of age, metallicity, distance modulus, and absorption as a function of the photometric band combinations and stellar models. We show that the historically favored three-band combinations of UBV and VRI can be meaningfully inconsistent with each other and with longer baseline data sets such as UBVRIJHK{sub S}. Differences among model sets can also be substantial. For instance, fitting Yi et al. (2001) and Dotter et al. (2008) models to UBVRIJHK{sub S} photometry for NGC 188 yields the following cluster parameters: age = (5.78 ± 0.03, 6.45 ± 0.04) Gyr, [Fe/H] = (+0.125 ± 0.003, −0.077 ± 0.003) dex, (m−M){sub V} = (11.441 ± 0.007, 11.525 ± 0.005) mag, and A{sub V} = (0.162 ± 0.003, 0.236 ± 0.003) mag, respectively. Within the formal fitting errors, these two fits are substantially and statistically different. Such differences among fits using different filters and models are a cautionary tale regarding our current ability to fit star cluster CMDs. Additional modeling of this kind, with more models and star clusters, and future Gaia parallaxes are critical for isolating and quantifying the most relevant uncertainties in stellar evolutionary models.« less

Tracking of aerobic fitness from adolescence to mid-adulthood.

PubMed

Van Oort, C; Jackowski, S A; Eisenmann, J C; Sherar, L B; Bailey, D A; Mirwald, R; Baxter-Jones, A D G

2013-01-01

Although adults' aerobic fitness is known to be correlated with cardiovascular disease risk, the longitudinal relationship with adolescent aerobic fitness is poorly described. To longitudinally investigate the relationship between aerobic fitness during adolescence and adulthood. Participants (207 boys, 149 girls) aged 7-17 years performed annual measures of VO2peak. In adulthood (40 and 50 years), 78 individuals (59 males and 18 females) were reassessed. Serial height measurements were used to estimate age at peak height velocity (APHV). During adolescence, VO2peak was measured via a treadmill test to voluntary exhaustion; adult VO2peak was assessed using submaximal predictive tests. Correlations were tested using Spearman's rho. ANCOVA was used to assess adult VO2peak group differences based off APHV VO2peak groupings (low, average or high). When sexes were pooled, moderate tracking existed from 2 years prior to APHV to APHV and APHV to 2 years after APHV (0.46, p < 0.001 and 0.35, p < 0.01, respectively). Correlations between APHV and adult values were low when sexes were pooled (p < 0.05). Comparisons of aggregated sexes revealed the low adolescent VO2peak group had lower values in adulthood relative to other groups (p < 0.05). Aerobic fitness has a low tracking between APHV and adulthood.

Object tracking with adaptive HOG detector and adaptive Rao-Blackwellised particle filter

NASA Astrophysics Data System (ADS)

Rosa, Stefano; Paleari, Marco; Ariano, Paolo; Bona, Basilio

2012-01-01

Scenarios for a manned mission to the Moon or Mars call for astronaut teams to be accompanied by semiautonomous robots. A prerequisite for human-robot interaction is the capability of successfully tracking humans and objects in the environment. In this paper we present a system for real-time visual object tracking in 2D images for mobile robotic systems. The proposed algorithm is able to specialize to individual objects and to adapt to substantial changes in illumination and object appearance during tracking. The algorithm is composed by two main blocks: a detector based on Histogram of Oriented Gradient (HOG) descriptors and linear Support Vector Machines (SVM), and a tracker which is implemented by an adaptive Rao-Blackwellised particle filter (RBPF). The SVM is re-trained online on new samples taken from previous predicted positions. We use the effective sample size to decide when the classifier needs to be re-trained. Position hypotheses for the tracked object are the result of a clustering procedure applied on the set of particles. The algorithm has been tested on challenging video sequences presenting strong changes in object appearance, illumination, and occlusion. Experimental tests show that the presented method is able to achieve near real-time performances with a precision of about 7 pixels on standard video sequences of dimensions 320 × 240.

Human tracking in thermal images using adaptive particle filters with online random forest learning

NASA Astrophysics Data System (ADS)

Ko, Byoung Chul; Kwak, Joon-Young; Nam, Jae-Yeal

2013-11-01

This paper presents a fast and robust human tracking method to use in a moving long-wave infrared thermal camera under poor illumination with the existence of shadows and cluttered backgrounds. To improve the human tracking performance while minimizing the computation time, this study proposes an online learning of classifiers based on particle filters and combination of a local intensity distribution (LID) with oriented center-symmetric local binary patterns (OCS-LBP). Specifically, we design a real-time random forest (RF), which is the ensemble of decision trees for confidence estimation, and confidences of the RF are converted into a likelihood function of the target state. First, the target model is selected by the user and particles are sampled. Then, RFs are generated using the positive and negative examples with LID and OCS-LBP features by online learning. The learned RF classifiers are used to detect the most likely target position in the subsequent frame in the next stage. Then, the RFs are learned again by means of fast retraining with the tracked object and background appearance in the new frame. The proposed algorithm is successfully applied to various thermal videos as tests and its tracking performance is better than those of other methods.

Linear FBG Temperature Sensor Interrogation with Fabry-Perot ITU Multi-wavelength Reference.

PubMed

Park, Hyoung-Jun; Song, Minho

2008-10-29

The equidistantly spaced multi-passbands of a Fabry-Perot ITU filter are used as an efficient multi-wavelength reference for fiber Bragg grating sensor demodulation. To compensate for the nonlinear wavelength tuning effect in the FBG sensor demodulator, a polynomial fitting algorithm was applied to the temporal peaks of the wavelength-scanned ITU filter. The fitted wavelength values are assigned to the peak locations of the FBG sensor reflections, obtaining constant accuracy, regardless of the wavelength scan range and frequency. A linearity error of about 0.18% against a reference thermocouple thermometer was obtained with the suggested method.

Scalable Conjunction Processing using Spatiotemporally Indexed Ephemeris Data

NASA Astrophysics Data System (ADS)

Budianto-Ho, I.; Johnson, S.; Sivilli, R.; Alberty, C.; Scarberry, R.

2014-09-01

The collision warnings produced by the Joint Space Operations Center (JSpOC) are of critical importance in protecting U.S. and allied spacecraft against destructive collisions and protecting the lives of astronauts during space flight. As the Space Surveillance Network (SSN) improves its sensor capabilities for tracking small and dim space objects, the number of tracked objects increases from thousands to hundreds of thousands of objects, while the number of potential conjunctions increases with the square of the number of tracked objects. Classical filtering techniques such as apogee and perigee filters have proven insufficient. Novel and orders of magnitude faster conjunction analysis algorithms are required to find conjunctions in a timely manner. Stellar Science has developed innovative filtering techniques for satellite conjunction processing using spatiotemporally indexed ephemeris data that efficiently and accurately reduces the number of objects requiring high-fidelity and computationally-intensive conjunction analysis. Two such algorithms, one based on the k-d Tree pioneered in robotics applications and the other based on Spatial Hash Tables used in computer gaming and animation, use, at worst, an initial O(N log N) preprocessing pass (where N is the number of tracked objects) to build large O(N) spatial data structures that substantially reduce the required number of O(N^2) computations, substituting linear memory usage for quadratic processing time. The filters have been implemented as Open Services Gateway initiative (OSGi) plug-ins for the Continuous Anomalous Orbital Situation Discriminator (CAOS-D) conjunction analysis architecture. We have demonstrated the effectiveness, efficiency, and scalability of the techniques using a catalog of 100,000 objects, an analysis window of one day, on a 64-core computer with 1TB shared memory. Each algorithm can process the full catalog in 6 minutes or less, almost a twenty-fold performance improvement over the baseline implementation running on the same machine. We will present an overview of the algorithms and results that demonstrate the scalability of our concepts.

Digital Inequalities in the Use of Self-Tracking Diet and Fitness Apps: Interview Study on the Influence of Social, Economic, and Cultural Factors

PubMed Central

Chauvel, Louis

2018-01-01

Background Digital devices are driving economic and social transformations, but assessing the uses, perceptions, and impact of these new technologies on diet and physical activity remains a major societal challenge. Objective We aimed to determine under which social, economic, and cultural conditions individuals in France were more likely to be actively invested in the use of self-tracking diet and fitness apps for better health behaviors. Methods Existing users of 3 diet and fitness self-tracking apps (Weight Watchers, MyFitnessPal, and sport apps) were recruited from 3 regions of France. We interviewed 79 individuals (Weight Watchers, n=37; MyFitnessPal, n=20; sport apps, n=22). In-depth semistructured interviews were conducted with each participant, using open-ended questions about their use of diet and fitness apps. A triangulation of methods (content, textual, and quantitative analyses) was performed. Results We found 3 clusters of interviewees who differed by social background and curative goal linked to use under constraint versus preventive goal linked to chosen use, and intensity of their self-quantification efforts and participation in social networks. Interviewees used the apps for a diversity of uses, including measurement, tracking, quantification, and participation in digital communities. A digital divide was highlighted, comprising a major social gap. Social conditions for appropriation of self-tracking devices included sociodemographic factors, life course stages, and cross-cutting factors of heterogeneity. Conclusions Individuals from affluent or intermediate social milieus were most likely to use the apps and to participate in the associated online social networks. These interviewees also demonstrated a preventive approach to a healthy lifestyle. Individuals from lower milieus were more reluctant to use digital devices relating to diet and physical activity or to participate in self-quantification. The results of the study have major implications for public health: the digital self-quantification device is intrinsically less important than the way the individual uses it, in terms of adoption of successful health behaviors. PMID:29678807

Digital Inequalities in the Use of Self-Tracking Diet and Fitness Apps: Interview Study on the Influence of Social, Economic, and Cultural Factors.

PubMed

Régnier, Faustine; Chauvel, Louis

2018-04-20

Digital devices are driving economic and social transformations, but assessing the uses, perceptions, and impact of these new technologies on diet and physical activity remains a major societal challenge. We aimed to determine under which social, economic, and cultural conditions individuals in France were more likely to be actively invested in the use of self-tracking diet and fitness apps for better health behaviors. Existing users of 3 diet and fitness self-tracking apps (Weight Watchers, MyFitnessPal, and sport apps) were recruited from 3 regions of France. We interviewed 79 individuals (Weight Watchers, n=37; MyFitnessPal, n=20; sport apps, n=22). In-depth semistructured interviews were conducted with each participant, using open-ended questions about their use of diet and fitness apps. A triangulation of methods (content, textual, and quantitative analyses) was performed. We found 3 clusters of interviewees who differed by social background and curative goal linked to use under constraint versus preventive goal linked to chosen use, and intensity of their self-quantification efforts and participation in social networks. Interviewees used the apps for a diversity of uses, including measurement, tracking, quantification, and participation in digital communities. A digital divide was highlighted, comprising a major social gap. Social conditions for appropriation of self-tracking devices included sociodemographic factors, life course stages, and cross-cutting factors of heterogeneity. Individuals from affluent or intermediate social milieus were most likely to use the apps and to participate in the associated online social networks. These interviewees also demonstrated a preventive approach to a healthy lifestyle. Individuals from lower milieus were more reluctant to use digital devices relating to diet and physical activity or to participate in self-quantification. The results of the study have major implications for public health: the digital self-quantification device is intrinsically less important than the way the individual uses it, in terms of adoption of successful health behaviors. ©Faustine Régnier, Louis Chauvel. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 20.04.2018.

Evolution of the SOFIA tracking control system

NASA Astrophysics Data System (ADS)

Fiebig, Norbert; Jakob, Holger; Pfüller, Enrico; Röser, Hans-Peter; Wiedemann, Manuel; Wolf, Jürgen

2014-07-01

The airborne observatory SOFIA (Stratospheric Observatory for Infrared Astronomy) is undergoing a modernization of its tracking system. This included new, highly sensitive tracking cameras, control computers, filter wheels and other equipment, as well as a major redesign of the control software. The experiences along the migration path from an aged 19" VMbus based control system to the application of modern industrial PCs, from VxWorks real-time operating system to embedded Linux and a state of the art software architecture are presented. Further, the concept is presented to operate the new camera also as a scientific instrument, in parallel to tracking.

Global tracking of space debris via CPHD and consensus

NASA Astrophysics Data System (ADS)

Wei, Baishen; Nener, Brett; Liu, Weifeng; Ma, Liang

2017-05-01

Space debris tracking is of great importance for safe operation of spacecraft. This paper presents an algorithm that achieves global tracking of space debris with a multi-sensor network. The sensor network has unknown and possibly time-varying topology. A consensus algorithm is used to effectively counteract the effects of data incest. Gaussian Mixture-Cardinalized Probability Hypothesis Density (GM-CPHD) filtering is used to estimate the state of the space debris. As an example of the method, 45 clusters of sensors are used to achieve global tracking. The performance of the proposed approach is demonstrated by simulation experiments.

Visual tracking of da Vinci instruments for laparoscopic surgery

NASA Astrophysics Data System (ADS)

Speidel, S.; Kuhn, E.; Bodenstedt, S.; Röhl, S.; Kenngott, H.; Müller-Stich, B.; Dillmann, R.

2014-03-01

Intraoperative tracking of laparoscopic instruments is a prerequisite to realize further assistance functions. Since endoscopic images are always available, this sensor input can be used to localize the instruments without special devices or robot kinematics. In this paper, we present an image-based markerless 3D tracking of different da Vinci instruments in near real-time without an explicit model. The method is based on different visual cues to segment the instrument tip, calculates a tip point and uses a multiple object particle filter for tracking. The accuracy and robustness is evaluated with in vivo data.

Treatment of late time instabilities in finite-difference EMP scattering codes

NASA Astrophysics Data System (ADS)

Simpson, L. T.; Holland, R.; Arman, S.

1982-12-01

Constraints applicable to a finite difference mesh for solution of Maxwell's equations are defined. The equations are applied in the time domain for computing electromagnetic coupling to complex structures, e.g., rectangular, cylindrical, or spherical. In a spatially varying grid, the amplitude growth of high frequency waves becomes exponential through multiple reflections from the outer boundary in cases of late-time solution. The exponential growth of the numerical noise exceeds the value of the real signal. The correction technique employs an absorbing surface and a radiating boundary, along with tailored selection of the grid mesh size. High frequency noise is removed through use of a low-pass digital filter, a linear least squares fit is made to thy low frequency filtered response, and the original, filtered, and fitted data are merged to preserve the high frequency early-time response.

Robust dead reckoning system for mobile robots based on particle filter and raw range scan.

PubMed

Duan, Zhuohua; Cai, Zixing; Min, Huaqing

2014-09-04

Robust dead reckoning is a complicated problem for wheeled mobile robots (WMRs), where the robots are faulty, such as the sticking of sensors or the slippage of wheels, for the discrete fault models and the continuous states have to be estimated simultaneously to reach a reliable fault diagnosis and accurate dead reckoning. Particle filters are one of the most promising approaches to handle hybrid system estimation problems, and they have also been widely used in many WMRs applications, such as pose tracking, SLAM, video tracking, fault identification, etc. In this paper, the readings of a laser range finder, which may be also interfered with by noises, are used to reach accurate dead reckoning. The main contribution is that a systematic method to implement fault diagnosis and dead reckoning in a particle filter framework concurrently is proposed. Firstly, the perception model of a laser range finder is given, where the raw scan may be faulty. Secondly, the kinematics of the normal model and different fault models for WMRs are given. Thirdly, the particle filter for fault diagnosis and dead reckoning is discussed. At last, experiments and analyses are reported to show the accuracy and efficiency of the presented method.

Robust Dead Reckoning System for Mobile Robots Based on Particle Filter and Raw Range Scan

PubMed Central

Duan, Zhuohua; Cai, Zixing; Min, Huaqing

2014-01-01

Robust dead reckoning is a complicated problem for wheeled mobile robots (WMRs), where the robots are faulty, such as the sticking of sensors or the slippage of wheels, for the discrete fault models and the continuous states have to be estimated simultaneously to reach a reliable fault diagnosis and accurate dead reckoning. Particle filters are one of the most promising approaches to handle hybrid system estimation problems, and they have also been widely used in many WMRs applications, such as pose tracking, SLAM, video tracking, fault identification, etc. In this paper, the readings of a laser range finder, which may be also interfered with by noises, are used to reach accurate dead reckoning. The main contribution is that a systematic method to implement fault diagnosis and dead reckoning in a particle filter framework concurrently is proposed. Firstly, the perception model of a laser range finder is given, where the raw scan may be faulty. Secondly, the kinematics of the normal model and different fault models for WMRs are given. Thirdly, the particle filter for fault diagnosis and dead reckoning is discussed. At last, experiments and analyses are reported to show the accuracy and efficiency of the presented method. PMID:25192318

Application of Kalman filter in frequency offset estimation for coherent optical quadrature phase-shift keying communication system

NASA Astrophysics Data System (ADS)

Jiang, Wen; Yang, Yanfu; Zhang, Qun; Sun, Yunxu; Zhong, Kangping; Zhou, Xian; Yao, Yong

2016-09-01

The frequency offset estimation (FOE) schemes based on Kalman filter are proposed and investigated in detail via numerical simulation and experiment. The schemes consist of a modulation phase removing stage and Kalman filter estimation stage. In the second stage, the Kalman filters are employed for tracking either differential angles or differential data between two successive symbols. Several implementations of the proposed FOE scheme are compared by employing different modulation removing methods and two Kalman algorithms. The optimal FOE implementation is suggested for different operating conditions including optical signal-to-noise ratio and the number of the available data symbols.

Belavkin filter for mixture of quadrature and photon counting process with some control techniques

NASA Astrophysics Data System (ADS)

Garg, Naman; Parthasarathy, Harish; Upadhyay, D. K.

2018-03-01

The Belavkin filter for the H-P Schrödinger equation is derived when the measurement process consists of a mixture of quantum Brownian motions and conservation/Poisson process. Higher-order powers of the measurement noise differentials appear in the Belavkin dynamics. For simulation, we use a second-order truncation. Control of the Belavkin filtered state by infinitesimal unitary operators is achieved in order to reduce the noise effects in the Belavkin filter equation. This is carried out along the lines of Luc Bouten. Various optimization criteria for control are described like state tracking and Lindblad noise removal.

Fast leaf-fitting with generalized underdose/overdose constraints for real-time MLC tracking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moore, Douglas, E-mail: douglas.moore@utsouthwestern.edu; Sawant, Amit; Ruan, Dan

2016-01-15

Purpose: Real-time multileaf collimator (MLC) tracking is a promising approach to the management of intrafractional tumor motion during thoracic and abdominal radiotherapy. MLC tracking is typically performed in two steps: transforming a planned MLC aperture in response to patient motion and refitting the leaves to the newly generated aperture. One of the challenges of this approach is the inability to faithfully reproduce the desired motion-adapted aperture. This work presents an optimization-based framework with which to solve this leaf-fitting problem in real-time. Methods: This optimization framework is designed to facilitate the determination of leaf positions in real-time while accounting for themore » trade-off between coverage of the PTV and avoidance of organs at risk (OARs). Derived within this framework, an algorithm is presented that can account for general linear transformations of the planned MLC aperture, particularly 3D translations and in-plane rotations. This algorithm, together with algorithms presented in Sawant et al. [“Management of three-dimensional intrafraction motion through real-time DMLC tracking,” Med. Phys. 35, 2050–2061 (2008)] and Ruan and Keall [Presented at the 2011 IEEE Power Engineering and Automation Conference (PEAM) (2011) (unpublished)], was applied to apertures derived from eight lung intensity modulated radiotherapy plans subjected to six-degree-of-freedom motion traces acquired from lung cancer patients using the kilovoltage intrafraction monitoring system developed at the University of Sydney. A quality-of-fit metric was defined, and each algorithm was evaluated in terms of quality-of-fit and computation time. Results: This algorithm is shown to perform leaf-fittings of apertures, each with 80 leaf pairs, in 0.226 ms on average as compared to 0.082 and 64.2 ms for the algorithms of Sawant et al., Ruan, and Keall, respectively. The algorithm shows approximately 12% improvement in quality-of-fit over the Sawant et al. approach, while performing comparably to Ruan and Keall. Conclusions: This work improves upon the quality of the Sawant et al. approach, but does so without sacrificing run-time performance. In addition, using this framework allows for complex leaf-fitting strategies that can be used to account for PTV/OAR trade-off during real-time MLC tracking.« less

A multiscale filter for noise reduction of low-dose cone beam projections

NASA Astrophysics Data System (ADS)

Yao, Weiguang; Farr, Jonathan B.

2015-08-01

The Poisson or compound Poisson process governs the randomness of photon fluence in cone beam computed tomography (CBCT) imaging systems. The probability density function depends on the mean (noiseless) of the fluence at a certain detector. This dependence indicates the natural requirement of multiscale filters to smooth noise while preserving structures of the imaged object on the low-dose cone beam projection. In this work, we used a Gaussian filter, \\text{exp}≤ft(-{{x}2}/2σ f2\\right) as the multiscale filter to de-noise the low-dose cone beam projections. We analytically obtained the expression of {σf} , which represents the scale of the filter, by minimizing local noise-to-signal ratio. We analytically derived the variance of residual noise from the Poisson or compound Poisson processes after Gaussian filtering. From the derived analytical form of the variance of residual noise, optimal σ f2 is proved to be proportional to the noiseless fluence and modulated by local structure strength expressed as the linear fitting error of the structure. A strategy was used to obtain the reliable linear fitting error: smoothing the projection along the longitudinal direction to calculate the linear fitting error along the lateral direction and vice versa. The performance of our multiscale filter was examined on low-dose cone beam projections of a Catphan phantom and a head-and-neck patient. After performing the filter on the Catphan phantom projections scanned with pulse time 4 ms, the number of visible line pairs was similar to that scanned with 16 ms, and the contrast-to-noise ratio of the inserts was higher than that scanned with 16 ms about 64% in average. For the simulated head-and-neck patient projections with pulse time 4 ms, the visibility of soft tissue structures in the patient was comparable to that scanned with 20 ms. The image processing took less than 0.5 s per projection with 1024   ×   768 pixels.

Eye gaze tracking using correlation filters

NASA Astrophysics Data System (ADS)

Karakaya, Mahmut; Bolme, David; Boehnen, Chris

2014-03-01

In this paper, we studied a method for eye gaze tracking that provide gaze estimation from a standard webcam with a zoom lens and reduce the setup and calibration requirements for new users. Specifically, we have developed a gaze estimation method based on the relative locations of points on the top of the eyelid and eye corners. Gaze estimation method in this paper is based on the distances between top point of the eyelid and eye corner detected by the correlation filters. Advanced correlation filters were found to provide facial landmark detections that are accurate enough to determine the subjects gaze direction up to angle of approximately 4-5 degrees although calibration errors often produce a larger overall shift in the estimates. This is approximately a circle of diameter 2 inches for a screen that is arm's length from the subject. At this accuracy it is possible to figure out what regions of text or images the subject is looking but it falls short of being able to determine which word the subject has looked at.

Integrated navigation fusion strategy of INS/UWB for indoor carrier attitude angle and position synchronous tracking.

PubMed

Fan, Qigao; Wu, Yaheng; Hui, Jing; Wu, Lei; Yu, Zhenzhong; Zhou, Lijuan

2014-01-01

In some GPS failure conditions, positioning for mobile target is difficult. This paper proposed a new method based on INS/UWB for attitude angle and position synchronous tracking of indoor carrier. Firstly, error model of INS/UWB integrated system is built, including error equation of INS and UWB. And combined filtering model of INS/UWB is researched. Simulation results show that the two subsystems are complementary. Secondly, integrated navigation data fusion strategy of INS/UWB based on Kalman filtering theory is proposed. Simulation results show that FAKF method is better than the conventional Kalman filtering. Finally, an indoor experiment platform is established to verify the integrated navigation theory of INS/UWB, which is geared to the needs of coal mine working environment. Static and dynamic positioning results show that the INS/UWB integrated navigation system is stable and real-time, positioning precision meets the requirements of working condition and is better than any independent subsystem.

Fast spacecraft adaptive attitude tracking control through immersion and invariance design

NASA Astrophysics Data System (ADS)

Wen, Haowei; Yue, Xiaokui; Li, Peng; Yuan, Jianping

2017-10-01

This paper presents a novel non-certainty-equivalence adaptive control method for the attitude tracking control problem of spacecraft with inertia uncertainties. The proposed immersion and invariance (I&I) based adaptation law provides a more direct and flexible approach to circumvent the limitations of the basic I&I method without employing any filter signal. By virtue of the adaptation high-gain equivalence property derived from the proposed adaptive method, the closed-loop adaptive system with a low adaptation gain could recover the high adaptation gain performance of the filter-based I&I method, and the resulting control torque demands during the initial transient has been significantly reduced. A special feature of this method is that the convergence of the parameter estimation error has been observably improved by utilizing an adaptation gain matrix instead of a single adaptation gain value. Numerical simulations are presented to highlight the various benefits of the proposed method compared with the certainty-equivalence-based control method and filter-based I&I control schemes.

Small Infrared Target Detection by Region-Adaptive Clutter Rejection for Sea-Based Infrared Search and Track

PubMed Central

Kim, Sungho; Lee, Joohyoung

2014-01-01

This paper presents a region-adaptive clutter rejection method for small target detection in sea-based infrared search and track. In the real world, clutter normally generates many false detections that impede the deployment of such detection systems. Incoming targets (missiles, boats, etc.) can be located in the sky, horizon and sea regions, which have different types of clutters, such as clouds, a horizontal line and sea-glint. The characteristics of regional clutter were analyzed after the geometrical analysis-based region segmentation. The false detections caused by cloud clutter were removed by the spatial attribute-based classification. Those by the horizontal line were removed using the heterogeneous background removal filter. False alarms by sun-glint were rejected using the temporal consistency filter, which is the most difficult part. The experimental results of the various cluttered background sequences show that the proposed region adaptive clutter rejection method produces fewer false alarms than that of the mean subtraction filter (MSF) with an acceptable degradation detection rate. PMID:25054633

Method and system for detecting polygon boundaries of structures in images as particle tracks through fields of corners and pixel gradients

DOEpatents

Paglieroni, David W [Pleasanton, CA; Manay, Siddharth [Livermore, CA

2011-12-20

A stochastic method and system for detecting polygon structures in images, by detecting a set of best matching corners of predetermined acuteness .alpha. of a polygon model from a set of similarity scores based on GDM features of corners, and tracking polygon boundaries as particle tracks using a sequential Monte Carlo approach. The tracking involves initializing polygon boundary tracking by selecting pairs of corners from the set of best matching corners to define a first side of a corresponding polygon boundary; tracking all intermediate sides of the polygon boundaries using a particle filter, and terminating polygon boundary tracking by determining the last side of the tracked polygon boundaries to close the polygon boundaries. The particle tracks are then blended to determine polygon matches, which may be made available, such as to a user, for ranking and inspection.

Text-mining as a methodology to assess eating disorder-relevant factors: Comparing mentions of fitness tracking technology across online communities.

PubMed

McCaig, Duncan; Bhatia, Sudeep; Elliott, Mark T; Walasek, Lukasz; Meyer, Caroline

2018-05-07

Text-mining offers a technique to identify and extract information from a large corpus of textual data. As an example, this study presents the application of text-mining to assess and compare interest in fitness tracking technology across eating disorder and health-related online communities. A list of fitness tracking technology terms was developed, and communities (i.e., 'subreddits') on a large online discussion platform (Reddit) were compared regarding the frequency with which these terms occurred. The corpus used in this study comprised all comments posted between May 2015 and January 2018 (inclusive) on six subreddits-three eating disorder-related, and three relating to either fitness, weight-management, or nutrition. All comments relating to the same 'thread' (i.e., conversation) were concatenated, and formed the cases used in this study (N = 377,276). Within the eating disorder-related subreddits, the findings indicated that a 'pro-eating disorder' subreddit, which is less recovery focused than the other eating disorder subreddits, had the highest frequency of fitness tracker terms. Across all subreddits, the weight-management subreddit had the highest frequency of the fitness tracker terms' occurrence, and MyFitnessPal was the most frequently mentioned fitness tracker. The technique exemplified here can potentially be used to assess group differences to identify at-risk populations, generate and explore clinically relevant research questions in populations who are difficult to recruit, and scope an area for which there is little extant literature. The technique also facilitates methodological triangulation of research findings obtained through more 'traditional' techniques, such as surveys or interviews. © 2018 Wiley Periodicals, Inc.

A tool for filtering information in complex systems

NASA Astrophysics Data System (ADS)

Tumminello, M.; Aste, T.; Di Matteo, T.; Mantegna, R. N.

2005-07-01

We introduce a technique to filter out complex data sets by extracting a subgraph of representative links. Such a filtering can be tuned up to any desired level by controlling the genus of the resulting graph. We show that this technique is especially suitable for correlation-based graphs, giving filtered graphs that preserve the hierarchical organization of the minimum spanning tree but containing a larger amount of information in their internal structure. In particular in the case of planar filtered graphs (genus equal to 0), triangular loops and four-element cliques are formed. The application of this filtering procedure to 100 stocks in the U.S. equity markets shows that such loops and cliques have important and significant relationships with the market structure and properties. This paper was submitted directly (Track II) to the PNAS office.Abbreviations: MST, minimum spanning tree; PMFG, Planar Maximally Filtered Graph; r-clique, clique of r elements.

Design of tunable thermo-optic C-band filter based on coated silicon slab

NASA Astrophysics Data System (ADS)

Pinhas, Hadar; Malka, Dror; Danan, Yossef; Sinvani, Moshe; Zalevsky, Zeev

2018-03-01

Optical filters are required to have narrow band-pass filtering in the spectral C-band for applications such as signal tracking, sub-band filtering or noise suppression. These requirements lead to a variety of filters such as Mach-Zehnder interferometer inter-leaver in silica, which offer thermo-optic effect for optical switching, however, without proper thermal and optical efficiency. In this paper we propose tunable thermo-optic filtering device based on coated silicon slab resonator with increased Q-factor for the C-band optical switching. The device can be designed either for long range wavelength tuning of for short range with increased wavelength resolution. Theoretical examination of the thermal parameters affecting the filtering process is shown together with experimental results. Proper channel isolation with an extinction ratio of 20dBs is achieved with spectral bandpass width of 0.07nm.

Error analysis and algorithm implementation for an improved optical-electric tracking device based on MEMS

NASA Astrophysics Data System (ADS)

Sun, Hong; Wu, Qian-zhong

2013-09-01

In order to improve the precision of optical-electric tracking device, proposing a kind of improved optical-electric tracking device based on MEMS, in allusion to the tracking error of gyroscope senor and the random drift, According to the principles of time series analysis of random sequence, establish AR model of gyro random error based on Kalman filter algorithm, then the output signals of gyro are multiple filtered with Kalman filter. And use ARM as micro controller servo motor is controlled by fuzzy PID full closed loop control algorithm, and add advanced correction and feed-forward links to improve response lag of angle input, Free-forward can make output perfectly follow input. The function of lead compensation link is to shorten the response of input signals, so as to reduce errors. Use the wireless video monitor module and remote monitoring software (Visual Basic 6.0) to monitor servo motor state in real time, the video monitor module gathers video signals, and the wireless video module will sent these signals to upper computer, so that show the motor running state in the window of Visual Basic 6.0. At the same time, take a detailed analysis to the main error source. Through the quantitative analysis of the errors from bandwidth and gyro sensor, it makes the proportion of each error in the whole error more intuitive, consequently, decrease the error of the system. Through the simulation and experiment results shows the system has good following characteristic, and it is very valuable for engineering application.

Experiments and analysis of tunable monolithic 1- μm single-frequency fiber lasers with loop mirror filters

NASA Astrophysics Data System (ADS)

Wang, Qi; Song, Huaqing; Wang, Xingpeng; Wang, Dongdong; Li, Li

2018-03-01

In this paper, we demonstrated thermally tunable 1- μm single-frequency fiber lasers utilizing loop mirror filters (LMFs) with unpumped Yb-doped fibers. The frequency selection and tracking was achieved by combining a fiber Bragg grating (FBG) and a dynamic grating established inside the LMF. The central emission wavelength was at 1064.07 nm with a tuning range of 1.4 nm, and the measured emission linewidth was less than 10 kHz. We also systematically studied the wavelength-tracking thermal stability of the LMF with separate thermal treatment upon the FBG and LMF, respectively. Finally, we presented a selection criterion for the minimum unpumped doped fiber length inside the LMF with experimental verification.

A robust high-order lattice adaptive notch filter and its application to narrowband noise cancellation

NASA Astrophysics Data System (ADS)

Kim, Seong-woo; Park, Young-cheol; Seo, Young-soo; Youn, Dae Hee

2014-12-01

In this paper, we propose a high-order lattice adaptive notch filter (LANF) that can robustly track multiple sinusoids. Unlike the conventional cascade structure, the proposed high-order LANF has robust tracking characteristics regardless of the frequencies of reference sinusoids and initial notch frequencies. The proposed high-order LANF is applied to a narrowband adaptive noise cancellation (ANC) to mitigate the effect of the broadband disturbance in the reference signal. By utilizing the gradient adaptive lattice (GAL) ANC algorithm and approximately combining it with the proposed high-order LANF, a computationally efficient narrowband ANC system is obtained. Experimental results demonstrate the robustness of the proposed high-order LANF and the effectiveness of the obtained narrowband ANC system.

Online Sensor Fault Detection Based on an Improved Strong Tracking Filter

PubMed Central

Wang, Lijuan; Wu, Lifeng; Guan, Yong; Wang, Guohui

2015-01-01

We propose a method for online sensor fault detection that is based on the evolving Strong Tracking Filter (STCKF). The cubature rule is used to estimate states to improve the accuracy of making estimates in a nonlinear case. A residual is the difference in value between an estimated value and the true value. A residual will be regarded as a signal that includes fault information. The threshold is set at a reasonable level, and will be compared with residuals to determine whether or not the sensor is faulty. The proposed method requires only a nominal plant model and uses STCKF to estimate the original state vector. The effectiveness of the algorithm is verified by simulation on a drum-boiler model. PMID:25690553

The use of the Kalman filter in the automated segmentation of EIT lung images.

PubMed

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.

Robust Visual Tracking Revisited: From Correlation Filter to Template Matching.

PubMed

Liu, Fanghui; Gong, Chen; Huang, Xiaolin; Zhou, Tao; Yang, Jie; Tao, Dacheng

2018-06-01

In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs). Compared to the correlation operation in CFTs, a sophisticated similarity metric termed mutual buddies similarity is proposed to exploit the relationship of multiple reciprocal nearest neighbors for target matching. By doing so, our tracker obtains powerful discriminative ability on distinguishing target and background as demonstrated by both empirical and theoretical analyses. Besides, instead of utilizing single template with the improper updating scheme in CFTs, we design a novel online template updating strategy named memory, which aims to select a certain amount of representative and reliable tracking results in history to construct the current stable and expressive template set. This scheme is beneficial for the proposed tracker to comprehensively understand the target appearance variations, recall some stable results. Both qualitative and quantitative evaluations on two benchmarks suggest that the proposed tracking method performs favorably against some recently developed CFTs and other competitive trackers.

A novel method to recover DD fusion proton CR-39 data corrupted by fast ablator ions at OMEGA and the National Ignition Facility

DOE PAGES

Sutcliffe, G. D.; Milanese, L. M.; Orozco, D.; ...

2016-08-05

CR-39 detectors are used routinely in inertial confinement fusion (ICF) experiments as a part of nuclear diagnostics. CR-39 is filtered to stop fast ablator ions which have been accelerated from an ICF implosion due to electric fields caused by laser-plasma interactions. In some experiments, the filtering is insufficient to block these ions and the fusion-product signal tracks are lost in the large background of accelerated ion tracks. A technique for recovering signal in these scenarios has been developed, tested, and implemented successfully. The technique involves removing material from the surface of the CR-39 to a depth beyond the endpoint ofmore » the ablator ion tracks. The technique preserves signal magnitude (yield) as well as structure in radiograph images. The technique is effective when signal particle range is at least 10 μm deeper than the necessary bulk material removal.« less

Multicell migration tracking within angiogenic networks by deep learning-based segmentation and augmented Bayesian filtering.

PubMed

Wang, Mengmeng; Ong, Lee-Ling Sharon; Dauwels, Justin; Asada, H Harry

2018-04-01

Cell migration is a key feature for living organisms. Image analysis tools are useful in studying cell migration in three-dimensional (3-D) in vitro environments. We consider angiogenic vessels formed in 3-D microfluidic devices (MFDs) and develop an image analysis system to extract cell behaviors from experimental phase-contrast microscopy image sequences. The proposed system initializes tracks with the end-point confocal nuclei coordinates. We apply convolutional neural networks to detect cell candidates and combine backward Kalman filtering with multiple hypothesis tracking to link the cell candidates at each time step. These hypotheses incorporate prior knowledge on vessel formation and cell proliferation rates. The association accuracy reaches 86.4% for the proposed algorithm, indicating that the proposed system is able to associate cells more accurately than existing approaches. Cell culture experiments in 3-D MFDs have shown considerable promise for improving biology research. The proposed system is expected to be a useful quantitative tool for potential microscopy problems of MFDs.

Environmentally adaptive processing for shallow ocean applications: A sequential Bayesian approach.

PubMed

Candy, J V

2015-09-01

The shallow ocean is a changing environment primarily due to temperature variations in its upper layers directly affecting sound propagation throughout. The need to develop processors capable of tracking these changes implies a stochastic as well as an environmentally adaptive design. Bayesian techniques have evolved to enable a class of processors capable of performing in such an uncertain, nonstationary (varying statistics), non-Gaussian, variable shallow ocean environment. A solution to this problem is addressed by developing a sequential Bayesian processor capable of providing a joint solution to the modal function tracking and environmental adaptivity problem. Here, the focus is on the development of both a particle filter and an unscented Kalman filter capable of providing reasonable performance for this problem. These processors are applied to hydrophone measurements obtained from a vertical array. The adaptivity problem is attacked by allowing the modal coefficients and/or wavenumbers to be jointly estimated from the noisy measurement data along with tracking of the modal functions while simultaneously enhancing the noisy pressure-field measurements.

Motion control of the rabbit ankle joint with a flat interface nerve electrode.

PubMed

Park, Hyun-Joo; Durand, Dominique M

2015-12-01

A flat interface nerve electrode (FINE) has been shown to improve fascicular and subfascicular selectivity. A recently developed novel control algorithm for FINE was applied to motion control of the rabbit ankle. A 14-contact FINE was placed on the rabbit sciatic nerve (n = 8), and ankle joint motion was controlled for sinusoidal trajectories and filtered random trajectories. To this end, a real-time controller was implemented with a multiple-channel current stimulus isolator. The performance test results showed good tracking performance of rabbit ankle joint motion for filtered random trajectories and sinusoidal trajectories (0.5 Hz and 1.0 Hz) with <10% average root-mean-square (RMS) tracking error, whereas the average range of ankle joint motion was between -20.0 ± 9.3° and 18.1 ± 8.8°. The proposed control algorithm enables the use of a multiple-contact nerve electrode for motion trajectory tracking control of musculoskeletal systems. © 2015 Wiley Periodicals, Inc.

A novel method to recover DD fusion proton CR-39 data corrupted by fast ablator ions at OMEGA and the National Ignition Facility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sutcliffe, G. D., E-mail: gdsut@mit.edu; Milanese, L. M.; Orozco, D.

2016-11-15

CR-39 detectors are used routinely in inertial confinement fusion (ICF) experiments as a part of nuclear diagnostics. CR-39 is filtered to stop fast ablator ions which have been accelerated from an ICF implosion due to electric fields caused by laser-plasma interactions. In some experiments, the filtering is insufficient to block these ions and the fusion-product signal tracks are lost in the large background of accelerated ion tracks. A technique for recovering signal in these scenarios has been developed, tested, and implemented successfully. The technique involves removing material from the surface of the CR-39 to a depth beyond the endpoint ofmore » the ablator ion tracks. The technique preserves signal magnitude (yield) as well as structure in radiograph images. The technique is effective when signal particle range is at least 10 μm deeper than the necessary bulk material removal.« less

Tracking and Control of a Neutral Particle Beam Using Multiple Model Adaptive Meer Filter.

DTIC Science & Technology

1987-12-01

34 method incorporated by Zicker in 1983 [32]. Once the beam estimation problem had been solved, the problem of beam control was examined. Zicker conducted a...filter. Then, the methods applied by Meer, and later Zicker , to reduce the computational load of a simple Meer filter, will be presented. 2.5.1 Basic...number of possible methods to prune the hypothesis tree and chose the "Best Half Method" as the most viable (21). Zicker [323, applied the work of Weiss

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.

Quantitative analysis of the improvement in omnidirectional maritime surveillance and tracking due to real-time image enhancement

NASA Astrophysics Data System (ADS)

de Villiers, Jason P.; Bachoo, Asheer K.; Nicolls, Fred C.; le Roux, Francois P. J.

2011-05-01

Tracking targets in a panoramic image is in many senses the inverse problem of tracking targets with a narrow field of view camera on a pan-tilt pedestal. In a narrow field of view camera tracking a moving target, the object is constant and the background is changing. A panoramic camera is able to model the entire scene, or background, and those areas it cannot model well are the potential targets and typically subtended far fewer pixels in the panoramic view compared to the narrow field of view. The outputs of an outward staring array of calibrated machine vision cameras are stitched into a single omnidirectional panorama and used to observe False Bay near Simon's Town, South Africa. A ground truth data-set was created by geo-aligning the camera array and placing a differential global position system receiver on a small target boat thus allowing its position in the array's field of view to be determined. Common tracking techniques including level-sets, Kalman filters and particle filters were implemented to run on the central processing unit of the tracking computer. Image enhancement techniques including multi-scale tone mapping, interpolated local histogram equalisation and several sharpening techniques were implemented on the graphics processing unit. An objective measurement of each tracking algorithm's robustness in the presence of sea-glint, low contrast visibility and sea clutter - such as white caps is performed on the raw recorded video data. These results are then compared to those obtained with the enhanced video data.

Good Features to Correlate for Visual Tracking

NASA Astrophysics Data System (ADS)

Gundogdu, Erhan; Alatan, A. Aydin

2018-05-01

During the recent years, correlation filters have shown dominant and spectacular results for visual object tracking. The types of the features that are employed in these family of trackers significantly affect the performance of visual tracking. The ultimate goal is to utilize robust features invariant to any kind of appearance change of the object, while predicting the object location as properly as in the case of no appearance change. As the deep learning based methods have emerged, the study of learning features for specific tasks has accelerated. For instance, discriminative visual tracking methods based on deep architectures have been studied with promising performance. Nevertheless, correlation filter based (CFB) trackers confine themselves to use the pre-trained networks which are trained for object classification problem. To this end, in this manuscript the problem of learning deep fully convolutional features for the CFB visual tracking is formulated. In order to learn the proposed model, a novel and efficient backpropagation algorithm is presented based on the loss function of the network. The proposed learning framework enables the network model to be flexible for a custom design. Moreover, it alleviates the dependency on the network trained for classification. Extensive performance analysis shows the efficacy of the proposed custom design in the CFB tracking framework. By fine-tuning the convolutional parts of a state-of-the-art network and integrating this model to a CFB tracker, which is the top performing one of VOT2016, 18% increase is achieved in terms of expected average overlap, and tracking failures are decreased by 25%, while maintaining the superiority over the state-of-the-art methods in OTB-2013 and OTB-2015 tracking datasets.

Multi-Robot FastSLAM for Large Domains

DTIC Science & Technology

2007-03-01

Derr, D. Fox, A.B. Cremers , Integrating global position estimation and position tracking for mobile robots: The dynamic markov localization approach...Intelligence (AAAI), 2000. 53. Andrew J. Davison and David W. Murray. Simultaneous Localization and Map- Building Using Active Vision. IEEE...Wyeth, Michael Milford and David Prasser. A Modified Particle Filter for Simultaneous Robot Localization and Landmark Tracking in an Indoor

A Class Act--Mathematics as Filter of Equity in South Africa's schools

ERIC Educational Resources Information Center

Kahn, Michael

2005-01-01

This article explores the way that educational disadvantage and opportunity became manifest in the South African school system by tracking performance in the gateway subject of Mathematics (and to a lesser extent Physical Science). Previous research by the author showed how it was possible to use a proxy method to track redress in the absence of…

Energy awareness for supercapacitors using Kalman filter state-of-charge tracking

NASA Astrophysics Data System (ADS)

Nadeau, Andrew; Hassanalieragh, Moeen; Sharma, Gaurav; Soyata, Tolga

2015-11-01

Among energy buffering alternatives, supercapacitors can provide unmatched efficiency and durability. Additionally, the direct relation between a supercapacitor's terminal voltage and stored energy can improve energy awareness. However, a simple capacitive approximation cannot adequately represent the stored energy in a supercapacitor. It is shown that the three branch equivalent circuit model provides more accurate energy awareness. This equivalent circuit uses three capacitances and associated resistances to represent the supercapacitor's internal SOC (state-of-charge). However, the SOC cannot be determined from one observation of the terminal voltage, and must be tracked over time using inexact measurements. We present: 1) a Kalman filtering solution for tracking the SOC; 2) an on-line system identification procedure to efficiently estimate the equivalent circuit's parameters; and 3) experimental validation of both parameter estimation and SOC tracking for 5 F, 10 F, 50 F, and 350 F supercapacitors. Validation is done within the operating range of a solar powered application and the associated power variability due to energy harvesting. The proposed techniques are benchmarked against the simple capacitive model and prior parameter estimation techniques, and provide a 67% reduction in root-mean-square error for predicting usable buffered energy.

Laser-based pedestrian tracking in outdoor environments by multiple mobile robots.

PubMed

Ozaki, Masataka; Kakimuma, Kei; Hashimoto, Masafumi; Takahashi, Kazuhiko

2012-10-29

This paper presents an outdoors laser-based pedestrian tracking system using a group of mobile robots located near each other. Each robot detects pedestrians from its own laser scan image using an occupancy-grid-based method, and the robot tracks the detected pedestrians via Kalman filtering and global-nearest-neighbor (GNN)-based data association. The tracking data is broadcast to multiple robots through intercommunication and is combined using the covariance intersection (CI) method. For pedestrian tracking, each robot identifies its own posture using real-time-kinematic GPS (RTK-GPS) and laser scan matching. Using our cooperative tracking method, all the robots share the tracking data with each other; hence, individual robots can always recognize pedestrians that are invisible to any other robot. The simulation and experimental results show that cooperating tracking provides the tracking performance better than conventional individual tracking does. Our tracking system functions in a decentralized manner without any central server, and therefore, this provides a degree of scalability and robustness that cannot be achieved by conventional centralized architectures.

Fast-synchronizing high-fidelity spread-spectrum receiver

DOEpatents

Moore, Michael Roy; Smith, Stephen Fulton; Emery, Michael Steven

2004-06-01

A fast-synchronizing receiver having a circuit including an equalizer configured for manipulating an analog signal; a detector in communication with the equalizer; a filter in communication with the detector; an oscillator in communication with the filter; a gate for receiving the manipulated signal; a circuit portion for synchronizing and tracking the manipulated signal; a summing circuit in communication with the circuit portion; and an output gate.

Particle Filtering Methods for Incorporating Intelligence Updates

DTIC Science & Technology

2017-03-01

methodology for incorporating intelligence updates into a stochastic model for target tracking. Due to the non -parametric assumptions of the PF...samples are taken with replacement from the remaining non -zero weighted particles at each iteration. With this methodology , a zero-weighted particle is...incorporation of information updates. A common method for incorporating information updates is Kalman filtering. However, given the probable nonlinear and non

Magnetic tracking for TomoTherapy systems: gradiometer based methods to filter eddy-current magnetic fields.

PubMed

McGary, John E; Xiong, Zubiao; Chen, Ji

2013-07-01

TomoTherapy systems lack real-time, tumor tracking. A possible solution is to use electromagnetic markers; however, eddy-current magnetic fields generated in response to a magnetic source can be comparable to the signal, thus degrading the localization accuracy. Therefore, the tracking system must be designed to account for the eddy fields created along the inner bore conducting surfaces. The aim of this work is to investigate localization accuracy using magnetic field gradients to determine feasibility toward TomoTherapy applications. Electromagnetic models are used to simulate magnetic fields created by a source and its simultaneous generation of eddy currents within a conducting cylinder. The source position is calculated using a least-squares fit of simulated sensor data using the dipole equation as the model equation. To account for field gradients across the sensor area (≈ 25 cm(2)), an iterative method is used to estimate the magnetic field at the sensor center. Spatial gradients are calculated with two arrays of uniaxial, paired sensors that form a gradiometer array, where the sensors are considered ideal. Experimental measurements of magnetic fields within the TomoTherapy bore are shown to be 1%-10% less than calculated with the electromagnetic model. Localization results using a 5 × 5 array of gradiometers are, in general, 2-4 times more accurate than a planar array of sensors, depending on the solenoid orientation and position. Simulation results show that the localization accuracy using a gradiometer array is within 1.3 mm over a distance of 20 cm from the array plane. In comparison, localization errors using single array are within 5 mm. The results indicate that the gradiometer method merits further studies and work due to the accuracy achieved with ideal sensors. Future studies should include realistic sensor models and extensive numerical studies to estimate the expected magnetic tracking accuracy within a TomoTherapy system before proceeding with prototype development.

Real-Time Radar-Based Tracking and State Estimation of Multiple Non-Conformant Aircraft

NASA Technical Reports Server (NTRS)

Cook, Brandon; Arnett, Timothy; Macmann, Owen; Kumar, Manish

2017-01-01

In this study, a novel solution for automated tracking of multiple unknown aircraft is proposed. Many current methods use transponders to self-report state information and augment track identification. While conformant aircraft typically report transponder information to alert surrounding aircraft of its state, vehicles may exist in the airspace that are non-compliant and need to be accurately tracked using alternative methods. In this study, a multi-agent tracking solution is presented that solely utilizes primary surveillance radar data to estimate aircraft state information. Main research challenges include state estimation, track management, data association, and establishing persistent track validity. In an effort to realize these challenges, techniques such as Maximum a Posteriori estimation, Kalman filtering, degree of membership data association, and Nearest Neighbor Spanning Tree clustering are implemented for this application.

Nonlinear filtering properties of detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Kiyono, Ken; Tsujimoto, Yutaka

2016-11-01

Detrended fluctuation analysis (DFA) has been widely used for quantifying long-range correlation and fractal scaling behavior. In DFA, to avoid spurious detection of scaling behavior caused by a nonstationary trend embedded in the analyzed time series, a detrending procedure using piecewise least-squares fitting has been applied. However, it has been pointed out that the nonlinear filtering properties involved with detrending may induce instabilities in the scaling exponent estimation. To understand this issue, we investigate the adverse effects of the DFA detrending procedure on the statistical estimation. We show that the detrending procedure using piecewise least-squares fitting results in the nonuniformly weighted estimation of the root-mean-square deviation and that this property could induce an increase in the estimation error. In addition, for comparison purposes, we investigate the performance of a centered detrending moving average analysis with a linear detrending filter and sliding window DFA and show that these methods have better performance than the standard DFA.

An efficient algorithm for measurement of retinal vessel diameter from fundus images based on directional filtering

NASA Astrophysics Data System (ADS)

Wang, Xuchu; Niu, Yanmin

2011-02-01

Automatic measurement of vessels from fundus images is a crucial step for assessing vessel anomalies in ophthalmological community, where the change in retinal vessel diameters is believed to be indicative of the risk level of diabetic retinopathy. In this paper, a new retinal vessel diameter measurement method by combining vessel orientation estimation and filter response is proposed. Its interesting characteristics include: (1) different from the methods that only fit the vessel profiles, the proposed method extracts more stable and accurate vessel diameter by casting this problem as a maximal response problem of a variation of Gabor filter; (2) the proposed method can directly and efficiently estimate the vessel's orientation, which is usually captured by time-consuming multi-orientation fitting techniques in many existing methods. Experimental results shows that the proposed method both retains the computational simplicity and achieves stable and accurate estimation results.

FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter.

PubMed

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.

Linear FBG Temperature Sensor Interrogation with Fabry-Perot ITU Multi-wavelength Reference

PubMed Central

Park, Hyoung-Jun; Song, Minho

2008-01-01

The equidistantly spaced multi-passbands of a Fabry-Perot ITU filter are used as an efficient multi-wavelength reference for fiber Bragg grating sensor demodulation. To compensate for the nonlinear wavelength tuning effect in the FBG sensor demodulator, a polynomial fitting algorithm was applied to the temporal peaks of the wavelength-scanned ITU filter. The fitted wavelength values are assigned to the peak locations of the FBG sensor reflections, obtaining constant accuracy, regardless of the wavelength scan range and frequency. A linearity error of about 0.18% against a reference thermocouple thermometer was obtained with the suggested method. PMID:27873898

Online two-stage association method for robust multiple people tracking

NASA Astrophysics Data System (ADS)

Lv, Jingqin; Fang, Jiangxiong; Yang, Jie

2011-07-01

Robust multiple people tracking is very important for many applications. It is a challenging problem due to occlusion and interaction in crowded scenarios. This paper proposes an online two-stage association method for robust multiple people tracking. In the first stage, short tracklets generated by linking people detection responses grow longer by particle filter based tracking, with detection confidence embedded into the observation model. And, an examining scheme runs at each frame for the reliability of tracking. In the second stage, multiple people tracking is achieved by linking tracklets to generate trajectories. An online tracklet association method is proposed to solve the linking problem, which allows applications in time-critical scenarios. This method is evaluated on the popular CAVIAR dataset. The experimental results show that our two-stage method is robust.

A Novel Multi-Sensor Environmental Perception Method Using Low-Rank Representation and a Particle Filter for Vehicle Reversing Safety

PubMed Central

Zhang, Zutao; Li, Yanjun; Wang, Fubing; Meng, Guanjun; Salman, Waleed; Saleem, Layth; Zhang, Xiaoliang; Wang, Chunbai; Hu, Guangdi; Liu, Yugang

2016-01-01

Environmental perception and information processing are two key steps of active safety for vehicle reversing. Single-sensor environmental perception cannot meet the need for vehicle reversing safety due to its low reliability. In this paper, we present a novel multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. The proposed system consists of four main steps, namely multi-sensor environmental perception, information fusion, target recognition and tracking using low-rank representation and a particle filter, and vehicle reversing speed control modules. First of all, the multi-sensor environmental perception module, based on a binocular-camera system and ultrasonic range finders, obtains the distance data for obstacles behind the vehicle when the vehicle is reversing. Secondly, the information fusion algorithm using an adaptive Kalman filter is used to process the data obtained with the multi-sensor environmental perception module, which greatly improves the robustness of the sensors. Then the framework of a particle filter and low-rank representation is used to track the main obstacles. The low-rank representation is used to optimize an objective particle template that has the smallest L-1 norm. Finally, the electronic throttle opening and automatic braking is under control of the proposed vehicle reversing control strategy prior to any potential collisions, making the reversing control safer and more reliable. The final system simulation and practical testing results demonstrate the validity of the proposed multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. PMID:27294931

A Novel Kalman Filter for Human Motion Tracking With an Inertial-Based Dynamic Inclinometer.

PubMed

Ligorio, Gabriele; Sabatini, Angelo M

2015-08-01

Design and development of a linear Kalman filter to create an inertial-based inclinometer targeted to dynamic conditions of motion. The estimation of the body attitude (i.e., the inclination with respect to the vertical) was treated as a source separation problem to discriminate the gravity and the body acceleration from the specific force measured by a triaxial accelerometer. The sensor fusion between triaxial gyroscope and triaxial accelerometer data was performed using a linear Kalman filter. Wrist-worn inertial measurement unit data from ten participants were acquired while performing two dynamic tasks: 60-s sequence of seven manual activities and 90 s of walking at natural speed. Stereophotogrammetric data were used as a reference. A statistical analysis was performed to assess the significance of the accuracy improvement over state-of-the-art approaches. The proposed method achieved, on an average, a root mean square attitude error of 3.6° and 1.8° in manual activities and locomotion tasks (respectively). The statistical analysis showed that, when compared to few competing methods, the proposed method improved the attitude estimation accuracy. A novel Kalman filter for inertial-based attitude estimation was presented in this study. A significant accuracy improvement was achieved over state-of-the-art approaches, due to a filter design that better matched the basic optimality assumptions of Kalman filtering. Human motion tracking is the main application field of the proposed method. Accurately discriminating the two components present in the triaxial accelerometer signal is well suited for studying both the rotational and the linear body kinematics.

A Novel Multi-Sensor Environmental Perception Method Using Low-Rank Representation and a Particle Filter for Vehicle Reversing Safety.

PubMed

Zhang, Zutao; Li, Yanjun; Wang, Fubing; Meng, Guanjun; Salman, Waleed; Saleem, Layth; Zhang, Xiaoliang; Wang, Chunbai; Hu, Guangdi; Liu, Yugang

2016-06-09

Environmental perception and information processing are two key steps of active safety for vehicle reversing. Single-sensor environmental perception cannot meet the need for vehicle reversing safety due to its low reliability. In this paper, we present a novel multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. The proposed system consists of four main steps, namely multi-sensor environmental perception, information fusion, target recognition and tracking using low-rank representation and a particle filter, and vehicle reversing speed control modules. First of all, the multi-sensor environmental perception module, based on a binocular-camera system and ultrasonic range finders, obtains the distance data for obstacles behind the vehicle when the vehicle is reversing. Secondly, the information fusion algorithm using an adaptive Kalman filter is used to process the data obtained with the multi-sensor environmental perception module, which greatly improves the robustness of the sensors. Then the framework of a particle filter and low-rank representation is used to track the main obstacles. The low-rank representation is used to optimize an objective particle template that has the smallest L-1 norm. Finally, the electronic throttle opening and automatic braking is under control of the proposed vehicle reversing control strategy prior to any potential collisions, making the reversing control safer and more reliable. The final system simulation and practical testing results demonstrate the validity of the proposed multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety.

Combined line-of-sight error and angular position to generate feedforward control for a charge-coupled device-based tracking loop

NASA Astrophysics Data System (ADS)

Tang, Tao; Cai, Huaxiang; Huang, Yongmei; Ren, Ge

2015-10-01

A feedforward control based on data fusion is proposed to enhance closed-loop performance. The target trajectory as the observed value of a Kalman filter is recovered by synthesizing line-of-sight error and angular position from the encoder. A Kalman filter based on a Singer acceleration model is employed to estimate the target velocity. In this control scheme, the control stability is influenced by the bandwidth of the Kalman filter and time misalignment. The transfer function of the Kalman filter in the frequency domain is built for analyzing the closed loop stability, which shows that the Kalman filter is the major factor that affects the control stability. The feedforward control proposed here is verified through simulations and experiments.

New estimation architecture for multisensor data fusion

NASA Astrophysics Data System (ADS)

Covino, Joseph M.; Griffiths, Barry E.

1991-07-01

This paper describes a novel method of hierarchical asynchronous distributed filtering called the Net Information Approach (NIA). The NIA is a Kalman-filter-based estimation scheme for spatially distributed sensors which must retain their local optimality yet require a nearly optimal global estimate. The key idea of the NIA is that each local sensor-dedicated filter tells the global filter 'what I've learned since the last local-to-global transmission,' whereas in other estimation architectures the local-to-global transmission consists of 'what I think now.' An algorithm based on this idea has been demonstrated on a small-scale target-tracking problem with many encouraging results. Feasibility of this approach was demonstrated by comparing NIA performance to an optimal centralized Kalman filter (lower bound) via Monte Carlo simulations.

A miniature shoe-mounted orientation determination system for accurate indoor heading and trajectory tracking.

PubMed

Zhang, Shengzhi; Yu, Shuai; Liu, Chaojun; Liu, Sheng

2016-06-01

Tracking the position of pedestrian is urgently demanded when the most commonly used GPS (Global Position System) is unavailable. Benefited from the small size, low-power consumption, and relatively high reliability, micro-electro-mechanical system sensors are well suited for GPS-denied indoor pedestrian heading estimation. In this paper, a real-time miniature orientation determination system (MODS) was developed for indoor heading and trajectory tracking based on a novel dual-linear Kalman filter. The proposed filter precludes the impact of geomagnetic distortions on pitch and roll that the heading is subjected to. A robust calibration approach was designed to improve the accuracy of sensors measurements based on a unified sensor model. Online tests were performed on the MODS with an improved turntable. The results demonstrate that the average RMSE (root-mean-square error) of heading estimation is less than 1°. Indoor heading experiments were carried out with the MODS mounted on the shoe of pedestrian. Besides, we integrated the existing MODS into an indoor pedestrian dead reckoning application as an example of its utility in realistic actions. A human attitude-based walking model was developed to calculate the walking distance. Test results indicate that mean percentage error of indoor trajectory tracking achieves 2% of the total walking distance. This paper provides a feasible alternative for accurate indoor heading and trajectory tracking.

Correlation peak analysis applied to a sequence of images using two different filters for eye tracking model

NASA Astrophysics Data System (ADS)

Patrón, Verónica A.; Álvarez Borrego, Josué; Coronel Beltrán, Ángel

2015-09-01

Eye tracking has many useful applications that range from biometrics to face recognition and human-computer interaction. The analysis of the characteristics of the eyes has become one of the methods to accomplish the location of the eyes and the tracking of the point of gaze. Characteristics such as the contrast between the iris and the sclera, the shape, and distribution of colors and dark/light zones in the area are the starting point for these analyses. In this work, the focus will be on the contrast between the iris and the sclera, performing a correlation in the frequency domain. The images are acquired with an ordinary camera, which with were taken images of thirty-one volunteers. The reference image is an image of the subjects looking to a point in front of them at 0° angle. Then sequences of images are taken with the subject looking at different angles. These images are processed in MATLAB, obtaining the maximum correlation peak for each image, using two different filters. Each filter were analyzed and then one was selected, which is the filter that gives the best performance in terms of the utility of the data, which is displayed in graphs that shows the decay of the correlation peak as the eye moves progressively at different angle. This data will be used to obtain a mathematical model or function that establishes a relationship between the angle of vision (AOV) and the maximum correlation peak (MCP). This model will be tested using different input images from other subject not contained in the initial database, being able to predict angle of vision using the maximum correlation peak data.

Advanced data assimilation in strongly nonlinear dynamical systems

NASA Technical Reports Server (NTRS)

Miller, Robert N.; Ghil, Michael; Gauthiez, Francois

1994-01-01

Advanced data assimilation methods are applied to simple but highly nonlinear problems. The dynamical systems studied here are the stochastically forced double well and the Lorenz model. In both systems, linear approximation of the dynamics about the critical points near which regime transitions occur is not always sufficient to track their occurrence or nonoccurrence. Straightforward application of the extended Kalman filter yields mixed results. The ability of the extended Kalman filter to track transitions of the double-well system from one stable critical point to the other depends on the frequency and accuracy of the observations relative to the mean-square amplitude of the stochastic forcing. The ability of the filter to track the chaotic trajectories of the Lorenz model is limited to short times, as is the ability of strong-constraint variational methods. Examples are given to illustrate the difficulties involved, and qualitative explanations for these difficulties are provided. Three generalizations of the extended Kalman filter are described. The first is based on inspection of the innovation sequence, that is, the successive differences between observations and forecasts; it works very well for the double-well problem. The second, an extension to fourth-order moments, yields excellent results for the Lorenz model but will be unwieldy when applied to models with high-dimensional state spaces. A third, more practical method--based on an empirical statistical model derived from a Monte Carlo simulation--is formulated, and shown to work very well. Weak-constraint methods can be made to perform satisfactorily in the context of these simple models, but such methods do not seem to generalize easily to practical models of the atmosphere and ocean. In particular, it is shown that the equations derived in the weak variational formulation are difficult to solve conveniently for large systems.

Development of a high efficiency personal/environmental radon dosimeter using polycarbonate detectors.

PubMed

Taheri, M; Jafarizadeh, M; Baradaran, S; Zainali, Gh

2006-12-01

Passive radon dosimeters, based on alpha particle etched track detectors, are widely used for the assessment of radon exposure. These methods are often applied in radon dosimetry for long periods of time. In this research work, we have developed a highly efficient method of personal/environmental radon dosimetry that is based upon the detection of alpha particles from radon daughters, (218)Po and (214)Po, using a polycarbonate detector (PC). The radon daughters are collected on the filter surface by passing a fixed flow of air through it and the PC detector, placed at a specified distance from the filter, is simultaneously exposed to alpha particles. After exposure, the latent tracks on the detector are made to appear by means of an electrochemical etching process; these are proportional to the radon dose. The air flow rate and the detector-filter distance are the major factors that can affect the performance of the dosimeter. The results obtained in our experimental investigations have shown that a distance of 1.5 cm between the detector and the filter, an absorber layer of Al with a thickness of 12 microm and an air flow rate of 4 l min(-1) offer the best design parameters for a high efficiency radon dosimeter. Then, the designed dosimeter was calibrated against different values of radon exposures and the obtained sensitivity was found to be 2.1 (tracks cm(-2)) (kBq h m(-3))(-1). The most important advantages of this method are that it is reliable, fast and convenient when used for radon dose assessment. In this paper, the optimized parameters of the dosimeter structure and its calibration procedure are presented and discussed.

Multiradar tracking for theater missile defense

NASA Astrophysics Data System (ADS)

Sviestins, Egils

1995-09-01

A prototype system for tracking tactical ballistic missiles using multiple radars has been developed. The tracking is based on measurement level fusion (`true' multi-radar) tracking. Strobes from passive sensors can also be used. We describe various features of the system with some emphasis on the filtering technique. This is based on the Interacting Multiple Model framework where the states are Free Flight, Drag, Boost, and Auxiliary. Measurement error modeling includes the signal to noise ratio dependence; outliers and miscorrelations are handled in the same way. The launch point is calculated within one minute from the detection of the missile. The impact point, and its uncertainty region, is calculated continually by extrapolating the track state vector using the equations of planetary motion.

Frequency-scanning interferometry using a time-varying Kalman filter for dynamic tracking measurements.

PubMed

Jia, Xingyu; Liu, Zhigang; Tao, Long; Deng, Zhongwen

2017-10-16

Frequency scanning interferometry (FSI) with a single external cavity diode laser (ECDL) and time-invariant Kalman filtering is an effective technique for measuring the distance of a dynamic target. However, due to the hysteresis of the piezoelectric ceramic transducer (PZT) actuator in the ECDL, the optical frequency sweeps of the ECDL exhibit different behaviors, depending on whether the frequency is increasing or decreasing. Consequently, the model parameters of Kalman filter appear time varying in each iteration, which produces state estimation errors with time-invariant filtering. To address this, in this paper, a time-varying Kalman filter is proposed to model the instantaneous movement of a target relative to the different optical frequency tuning durations of the ECDL. The combination of the FSI method with the time-varying Kalman filter was theoretically analyzed, and the simulation and experimental results show the proposed method greatly improves the performance of dynamic FSI measurements.

Partial removal of correlated noise in thermal imagery

NASA Astrophysics Data System (ADS)

Borel, Christoph C.; Cooke, Bradly J.; Laubscher, Bryan E.

1996-05-01

Correlated noise occurs in many imaging systems such as scanners and push-broom imagers. The sources of correlated noise can be from the detectors, pre-amplifiers and sampling circuits. Correlated noise appears as streaking along the scan direction of a scanner or in the along track direction of a push-broom imager. We have developed algorithms to simulate correlated noise and pre-filter to reduce the amount of streaking while not destroying the scene content. The pre-filter in the Fourier domain consists of the product of two filters. One filter models the correlated noise spectrum, the other is a windowing function, e.g. Gaussian or Hanning window with variable width to block high frequency noise away from the origin of the Fourier Transform of the image data. We have optimized the filter parameters for various scenes and find improvements of the RMS error of the original minus the pre-filtered noisy image.

Assessing a local ensemble Kalman filter: perfect model experiments with the National Centers for Environmental Prediction global model

NASA Astrophysics Data System (ADS)

Szunyogh, Istvan; Kostelich, Eric J.; Gyarmati, G.; Patil, D. J.; Hunt, Brian R.; Kalnay, Eugenia; Ott, Edward; Yorke, James A.

2005-08-01

The accuracy and computational efficiency of the recently proposed local ensemble Kalman filter (LEKF) data assimilation scheme is investigated on a state-of-the-art operational numerical weather prediction model using simulated observations. The model selected for this purpose is the T62 horizontal- and 28-level vertical-resolution version of the Global Forecast System (GFS) of the National Center for Environmental Prediction. The performance of the data assimilation system is assessed for different configurations of the LEKF scheme. It is shown that a modest size (40-member) ensemble is sufficient to track the evolution of the atmospheric state with high accuracy. For this ensemble size, the computational time per analysis is less than 9 min on a cluster of PCs. The analyses are extremely accurate in the mid-latitude storm track regions. The largest analysis errors, which are typically much smaller than the observational errors, occur where parametrized physical processes play important roles. Because these are also the regions where model errors are expected to be the largest, limitations of a real-data implementation of the ensemble-based Kalman filter may be easily mistaken for model errors. In light of these results, the importance of testing the ensemble-based Kalman filter data assimilation systems on simulated observations is stressed.

An adaptive three-stage extended Kalman filter for nonlinear discrete-time system in presence of unknown inputs.

PubMed

Xiao, Mengli; Zhang, Yongbo; Wang, Zhihua; Fu, Huimin

2018-04-01

Considering the performances of conventional Kalman filter may seriously degrade when it suffers stochastic faults and unknown input, which is very common in engineering problems, a new type of adaptive three-stage extended Kalman filter (AThSEKF) is proposed to solve state and fault estimation in nonlinear discrete-time system under these conditions. The three-stage UV transformation and adaptive forgetting factor are introduced for derivation, and by comparing with the adaptive augmented state extended Kalman filter, it is proven to be uniformly asymptotically stable. Furthermore, the adaptive three-stage extended Kalman filter is applied to a two-dimensional radar tracking scenario to illustrate the effect, and the performance is compared with that of conventional three stage extended Kalman filter (ThSEKF) and the adaptive two-stage extended Kalman filter (ATEKF). The results show that the adaptive three-stage extended Kalman filter is more effective than these two filters when facing the nonlinear discrete-time systems with information of unknown inputs not perfectly known. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

A nonlinear generalization of the Savitzky-Golay filter and the quantitative analysis of saccades

PubMed Central

Dai, Weiwei; Selesnick, Ivan; Rizzo, John-Ross; Rucker, Janet; Hudson, Todd

2017-01-01

The Savitzky-Golay (SG) filter is widely used to smooth and differentiate time series, especially biomedical data. However, time series that exhibit abrupt departures from their typical trends, such as sharp waves or steps, which are of physiological interest, tend to be oversmoothed by the SG filter. Hence, the SG filter tends to systematically underestimate physiological parameters in certain situations. This article proposes a generalization of the SG filter to more accurately track abrupt deviations in time series, leading to more accurate parameter estimates (e.g., peak velocity of saccadic eye movements). The proposed filtering methodology models a time series as the sum of two component time series: a low-frequency time series for which the conventional SG filter is well suited, and a second time series that exhibits instantaneous deviations (e.g., sharp waves, steps, or more generally, discontinuities in a higher order derivative). The generalized SG filter is then applied to the quantitative analysis of saccadic eye movements. It is demonstrated that (a) the conventional SG filter underestimates the peak velocity of saccades, especially those of small amplitude, and (b) the generalized SG filter estimates peak saccadic velocity more accurately than the conventional filter. PMID:28813566

A nonlinear generalization of the Savitzky-Golay filter and the quantitative analysis of saccades.

PubMed

Dai, Weiwei; Selesnick, Ivan; Rizzo, John-Ross; Rucker, Janet; Hudson, Todd

2017-08-01

The Savitzky-Golay (SG) filter is widely used to smooth and differentiate time series, especially biomedical data. However, time series that exhibit abrupt departures from their typical trends, such as sharp waves or steps, which are of physiological interest, tend to be oversmoothed by the SG filter. Hence, the SG filter tends to systematically underestimate physiological parameters in certain situations. This article proposes a generalization of the SG filter to more accurately track abrupt deviations in time series, leading to more accurate parameter estimates (e.g., peak velocity of saccadic eye movements). The proposed filtering methodology models a time series as the sum of two component time series: a low-frequency time series for which the conventional SG filter is well suited, and a second time series that exhibits instantaneous deviations (e.g., sharp waves, steps, or more generally, discontinuities in a higher order derivative). The generalized SG filter is then applied to the quantitative analysis of saccadic eye movements. It is demonstrated that (a) the conventional SG filter underestimates the peak velocity of saccades, especially those of small amplitude, and (b) the generalized SG filter estimates peak saccadic velocity more accurately than the conventional filter.

Respiratory protection against Mycobacterium tuberculosis: quantitative fit test outcomes for five type N95 filtering-facepiece respirators.

PubMed

Lee, Kiyoung; Slavcev, Andrea; Nicas, Mark

2004-01-01

In preparing to fit test a large workforce, a respirator program manager needs to initially choose respirators that will fit the greatest proportion of employees and achieve the best fits. This article discusses our strategy in selecting respirators from an initial array of seven NIOSH-certified Type N95 filtering-facepiece devices for a respiratory protection program against Mycobacterium tuberculosis (M. tb) aerosol. The seven respirators were screened based on manufacturer-provided fit test data, comfort, and cost. From these 7 devices, 5 were chosen for quantitative fit testing on 40 subjects who were a convenience sample from a cohort of approximately 30,000 workers scheduled to undergo fit testing. Across the five brands, medium/regular-size respirators fit from 8% to 95% of the subjects; providing another size of the same brand improved the pass rates slightly. Gender was not found to significantly affect fit test pass rates for any respirator brand. Among test panel members, an Aearo Corporation respirator (TC 84A-2630) and a 3M Company respirator (TC 84A-0006) provided the highest overall pass rates of 98% and 90%, respectively. We selected these two brands for fit testing in the larger worker cohort. To date, these two respirators have provided overall pass rates of 98% (1793/1830) and 88% (50/57), respectively, which are similar to the test panel results. Among 1850 individuals who have been fit tested, 1843 (99.6%) have been successfully fitted with one or the other brand. In a separate analysis, we used the test panel pass rates to estimate the reduction in M. tb infection risk afforded by the medium/regular-size of five filtering-facepiece respirators. We posed a low-exposure versus a high-exposure scenario for health care workers and assumed that respirators could be assigned without conducting fit testing, as proposed by many hospital infection control practitioners. Among those who would pass versus fail the fit test, we assumed an average respirator penetration (primarily due to faceseal leakage) of .04 and 0.3, respectively. The respirator with the highest overall pass rate (95%) reduced M. tb infection risk by 95%, while the respirator with the lowest pass rate (8%) reduced M. tb infection risk by only 70%. To promote the marketing of respirators that will successfully fit the highest proportion of wearers, and to increase protection for workers who might use respirators without the benefit of being fit tested, we recommend that fit testing be part of the NIOSH certification process for negative-pressure air-purifying respirators with tightly fitting facepieces. At a minimum, we recommend that respirator manufacturers generate and provide pass rate data to assist in selecting candidate respirators. In any event, program managers can initially select candidate respirators by comparing quantitative fit tests for a representative sample of their employee population.

OpenCV and TYZX : video surveillance for tracking.

DOE Office of Scientific and Technical Information (OSTI.GOV)

He, Jim; Spencer, Andrew; Chu, Eric

2008-08-01

As part of the National Security Engineering Institute (NSEI) project, several sensors were developed in conjunction with an assessment algorithm. A camera system was developed in-house to track the locations of personnel within a secure room. In addition, a commercial, off-the-shelf (COTS) tracking system developed by TYZX was examined. TYZX is a Bay Area start-up that has developed its own tracking hardware and software which we use as COTS support for robust tracking. This report discusses the pros and cons of each camera system, how they work, a proposed data fusion method, and some visual results. Distributed, embedded image processingmore » solutions show the most promise in their ability to track multiple targets in complex environments and in real-time. Future work on the camera system may include three-dimensional volumetric tracking by using multiple simple cameras, Kalman or particle filtering, automated camera calibration and registration, and gesture or path recognition.« less

Constrained Kalman Filtering Via Density Function Truncation for Turbofan Engine Health Estimation

NASA Technical Reports Server (NTRS)

Simon, Dan; Simon, Donald L.

2006-01-01

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 truncates the PDF (probability density function) of the Kalman filter estimate at the known constraints and then computes the constrained filter estimate as the mean of the truncated PDF. The incorporation of state variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is demonstrated via simulation results obtained from a turbofan engine model. The turbofan engine model contains 3 state variables, 11 measurements, and 10 component health parameters. It is also shown that the truncated Kalman filter may be a more accurate way of incorporating inequality constraints than other constrained filters (e.g., the projection approach to constrained filtering).

Fit Assessment of N95 Filtering-Facepiece Respirators in the U.S. Centers for Disease Control and Prevention Strategic National Stockpile.

PubMed

Bergman, Michael; Zhuang, Ziqing; Brochu, Elizabeth; Palmiero, Andrew

National Institute for Occupational Safety and Health (NIOSH)-approved N95 filtering-facepiece respirators (FFR) are currently stockpiled by the U.S. Centers for Disease Control and Prevention (CDC) for emergency deployment to healthcare facilities in the event of a widespread emergency such as an influenza pandemic. This study assessed the fit of N95 FFRs purchased for the CDC Strategic National Stockpile. The study addresses the question of whether the fit achieved by specific respirator sizes relates to facial size categories as defined by two NIOSH fit test panels. Fit test data were analyzed from 229 test subjects who performed a nine-donning fit test on seven N95 FFR models using a quantitative fit test protocol. An initial respirator model selection process was used to determine if the subject could achieve an adequate fit on a particular model; subjects then tested the adequately fitting model for the nine-donning fit test. Only data for models which provided an adequate initial fit (through the model selection process) for a subject were analyzed for this study. For the nine-donning fit test, six of the seven respirator models accommodated the fit of subjects (as indicated by geometric mean fit factor > 100) for not only the intended NIOSH bivariate and PCA panel sizes corresponding to the respirator size, but also for other panel sizes which were tested for each model. The model which showed poor performance may not be accurately represented because only two subjects passed the initial selection criteria to use this model. Findings are supportive of the current selection of facial dimensions for the new NIOSH panels. The various FFR models selected for the CDC Strategic National Stockpile provide a range of sizing options to fit a variety of facial sizes.

Fit Assessment of N95 Filtering-Facepiece Respirators in the U.S. Centers for Disease Control and Prevention Strategic National Stockpile

PubMed Central

Bergman, Michael; Zhuang, Ziqing; Brochu, Elizabeth; Palmiero, Andrew

2016-01-01

National Institute for Occupational Safety and Health (NIOSH)-approved N95 filtering-facepiece respirators (FFR) are currently stockpiled by the U.S. Centers for Disease Control and Prevention (CDC) for emergency deployment to healthcare facilities in the event of a widespread emergency such as an influenza pandemic. This study assessed the fit of N95 FFRs purchased for the CDC Strategic National Stockpile. The study addresses the question of whether the fit achieved by specific respirator sizes relates to facial size categories as defined by two NIOSH fit test panels. Fit test data were analyzed from 229 test subjects who performed a nine-donning fit test on seven N95 FFR models using a quantitative fit test protocol. An initial respirator model selection process was used to determine if the subject could achieve an adequate fit on a particular model; subjects then tested the adequately fitting model for the nine-donning fit test. Only data for models which provided an adequate initial fit (through the model selection process) for a subject were analyzed for this study. For the nine-donning fit test, six of the seven respirator models accommodated the fit of subjects (as indicated by geometric mean fit factor > 100) for not only the intended NIOSH bivariate and PCA panel sizes corresponding to the respirator size, but also for other panel sizes which were tested for each model. The model which showed poor performance may not be accurately represented because only two subjects passed the initial selection criteria to use this model. Findings are supportive of the current selection of facial dimensions for the new NIOSH panels. The various FFR models selected for the CDC Strategic National Stockpile provide a range of sizing options to fit a variety of facial sizes. PMID:26877587

H(infinity)/H(2)/Kalman filtering of linear dynamical systems via variational techniques with applications to target tracking

NASA Astrophysics Data System (ADS)

Rawicz, Paul Lawrence

In this thesis, the similarities between the structure of the H infinity, H2, and Kalman filters are examined. The filters used in this examination have been derived through duality to the full information controller. In addition, a direct variation of parameters derivation of the Hinfinity filter is presented for both continuous and discrete time (staler case). Direct and controller dual derivations using differential games exist in the literature and also employ variational techniques. Using a variational, rather than a differential games, viewpoint has resulted in a simple relationship between the Riccati equations that arise from the derivation and the results of the Bounded Real Lemma. This same relation has previously been found in the literature and used to relate the Riccati inequality for linear systems to the Hamilton Jacobi inequality for nonlinear systems when implementing the Hinfinity controller. The Hinfinity, H2, and Kalman filters are applied to the two-state target tracking problem. In continuous time, closed form analytic expressions for the trackers and their performance are determined. To evaluate the trackers using a neutral, realistic, criterion, the probability of target escape is developed. That is, the probability that the target position error will be such that the target is outside the radar beam width resulting in a loss of measurement. In discrete time, a numerical example, using the probability of target escape, is presented to illustrate the differences in tracker performance.

Interacting multiple model forward filtering and backward smoothing for maneuvering target tracking

NASA Astrophysics Data System (ADS)

Nandakumaran, N.; Sutharsan, S.; Tharmarasa, R.; Lang, Tom; McDonald, Mike; Kirubarajan, T.

2009-08-01

The Interacting Multiple Model (IMM) estimator has been proven to be effective in tracking agile targets. Smoothing or retrodiction, which uses measurements beyond the current estimation time, provides better estimates of target states. Various methods have been proposed for multiple model smoothing in the literature. In this paper, a new smoothing method, which involves forward filtering followed by backward smoothing while maintaining the fundamental spirit of the IMM, is proposed. The forward filtering is performed using the standard IMM recursion, while the backward smoothing is performed using a novel interacting smoothing recursion. This backward recursion mimics the IMM estimator in the backward direction, where each mode conditioned smoother uses standard Kalman smoothing recursion. Resulting algorithm provides improved but delayed estimates of target states. Simulation studies are performed to demonstrate the improved performance with a maneuvering target scenario. The comparison with existing methods confirms the improved smoothing accuracy. This improvement results from avoiding the augmented state vector used by other algorithms. In addition, the new technique to account for model switching in smoothing is a key in improving the performance.

Eye Gaze Tracking using Correlation Filters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karakaya, Mahmut; Boehnen, Chris Bensing; Bolme, David S

In this paper, we studied a method for eye gaze tracking that provide gaze estimation from a standard webcam with a zoom lens and reduce the setup and calibration requirements for new users. Specifically, we have developed a gaze estimation method based on the relative locations of points on the top of the eyelid and eye corners. Gaze estimation method in this paper is based on the distances between top point of the eyelid and eye corner detected by the correlation filters. Advanced correlation filters were found to provide facial landmark detections that are accurate enough to determine the subjectsmore » gaze direction up to angle of approximately 4-5 degrees although calibration errors often produce a larger overall shift in the estimates. This is approximately a circle of diameter 2 inches for a screen that is arm s length from the subject. At this accuracy it is possible to figure out what regions of text or images the subject is looking but it falls short of being able to determine which word the subject has looked at.« less

A combined mean dynamic topography model - DTU17cMDT

NASA Astrophysics Data System (ADS)

Knudsen, P.; Andersen, O. B.; Nielsen, K.; Maximenko, N. A.

2017-12-01

Within the ESA supported Optimal Geoid for Modelling Ocean Circulation (OGMOC) project a new geoid model have been derived. It is based on the GOCO05C setup though the newer DTU15GRA altimetric surface gravity has been used in the combination. Subsequently the model has been augmented using the EIGEN-6C4 coefficients to d/o 2160. Compared to the DTU13MSS, the DTU15MSS has been derived by including re-tracked CRYOSAT-2 altimetry also, hence, increasing its resolution. Also, some issues in the Polar regions have been solved. The new DTU17MDT has been derived using this new geoid model and the DTU15MSS mean sea surface. Compared to other geoid models the new OGMOC geoid model has been optimized to avoid striations and orange skin like features. The filtering was re-evaluated by adjusting the quasi-gaussian filter width to optimize the fit to drifter velocities. The results show that the new MDT improves the resolution of the details of the ocean circulation. Subsequently, the drifter velocities were integrated to enhance the resolution of the MDT. As a contribution to the ESA supported GOCE++ project DYCOT a special concern was devoted to the coastal areas to optimize the extrapolation towards the coast and to integrate mean sea levels at tide gauges into that process. The presentation will focus on the coastal zone when assessing the methodology, the data and the final model DTU17cMDT.

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.

Laser-Based Pedestrian Tracking in Outdoor Environments by Multiple Mobile Robots

PubMed Central

Ozaki, Masataka; Kakimuma, Kei; Hashimoto, Masafumi; Takahashi, Kazuhiko

2012-01-01

This paper presents an outdoors laser-based pedestrian tracking system using a group of mobile robots located near each other. Each robot detects pedestrians from its own laser scan image using an occupancy-grid-based method, and the robot tracks the detected pedestrians via Kalman filtering and global-nearest-neighbor (GNN)-based data association. The tracking data is broadcast to multiple robots through intercommunication and is combined using the covariance intersection (CI) method. For pedestrian tracking, each robot identifies its own posture using real-time-kinematic GPS (RTK-GPS) and laser scan matching. Using our cooperative tracking method, all the robots share the tracking data with each other; hence, individual robots can always recognize pedestrians that are invisible to any other robot. The simulation and experimental results show that cooperating tracking provides the tracking performance better than conventional individual tracking does. Our tracking system functions in a decentralized manner without any central server, and therefore, this provides a degree of scalability and robustness that cannot be achieved by conventional centralized architectures. PMID:23202171

Interactive Spectral Analysis and Computation (ISAAC)

NASA Technical Reports Server (NTRS)

Lytle, D. M.

1992-01-01

Isaac is a task in the NSO external package for IRAF. A descendant of a FORTRAN program written to analyze data from a Fourier transform spectrometer, the current implementation has been generalized sufficiently to make it useful for general spectral analysis and other one dimensional data analysis tasks. The user interface for Isaac is implemented as an interpreted mini-language containing a powerful, programmable vector calculator. Built-in commands provide much of the functionality needed to produce accurate line lists from input spectra. These built-in functions include automated spectral line finding, least squares fitting of Voigt profiles to spectral lines including equality constraints, various filters including an optimal filter construction tool, continuum fitting, and various I/O functions.

Phoenix: Service Oriented Architecture for Information Management - Abstract Architecture Document

DTIC Science & Technology

2011-09-01

implementation logic and policy if and which Information Brokering and Repository Services the information is going to be forwarded to. These service chains...descriptions are going to be retrieved. Raised Exceptions: • Exception getConsumers(sessionTrack : SessionTrack, information : Information...that exetnd the usefullness of the IM system as a whole. • Client • Event Notification • Filter • Information Discovery • Security • Service

Magician Simulator. A Realistic Simulator for Heterogenous Teams of Autonomous Robots

DTIC Science & Technology

2011-01-18

IMU, and LIDAR systems for identifying and tracking mobile OOI at long range (>20m), providing early warnings and allowing neutralization from a... LIDAR and Computer Vision template-based feature tracking approaches. Mapping was solved through Multi-Agent particle-filter based Simultaneous...Locali- zation and Mapping ( SLAM ). Our system contains two maps, a physical map and an influence map (location of hostile OOI, explored and unexplored

A Simplified Baseband Prefilter Model with Adaptive Kalman Filter for Ultra-Tight COMPASS/INS Integration

PubMed Central

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. PMID:23012564

A stochastic approach to noise modeling for barometric altimeters.

PubMed

Sabatini, Angelo Maria; Genovese, Vincenzo

2013-11-18

The question whether barometric altimeters can be applied to accurately track human motions is still debated, since their measurement performance are rather poor due to either coarse resolution or drifting behavior problems. As a step toward accurate short-time tracking of changes in height (up to few minutes), we develop a stochastic model that attempts to capture some statistical properties of the barometric altimeter noise. The barometric altimeter noise is decomposed in three components with different physical origin and properties: a deterministic time-varying mean, mainly correlated with global environment changes, and a first-order Gauss-Markov (GM) random process, mainly accounting for short-term, local environment changes, the effects of which are prominent, respectively, for long-time and short-time motion tracking; an uncorrelated random process, mainly due to wideband electronic noise, including quantization noise. Autoregressive-moving average (ARMA) system identification techniques are used to capture the correlation structure of the piecewise stationary GM component, and to estimate its standard deviation, together with the standard deviation of the uncorrelated component. M-point moving average filters used alone or in combination with whitening filters learnt from ARMA model parameters are further tested in few dynamic motion experiments and discussed for their capability of short-time tracking small-amplitude, low-frequency motions.

Track-Etched Magnetic Micropores for Immunomagnetic Isolation of Pathogens

PubMed Central

Muluneh, Melaku; Shang, Wu

2014-01-01

A microfluidic chip is developed to selectively isolate magnetically tagged cells from heterogeneous suspensions, the track-etched magnetic micropore (TEMPO) filter. The TEMPO consists of an ion track-etched polycarbonate membrane coated with soft magnetic film (Ni20Fe80). In the presence of an applied field, provided by a small external magnet, the filter becomes magnetized and strong magnetic traps are created along the edges of the micropores. In contrast to conventional microfluidics, fluid flows vertically through the porous membrane allowing large flow rates while keeping the capture rate high and the chip compact. By utilizing track-etching instead of conventional semiconductor fabrication, TEMPOs can be fabricated with microscale pores over large areas A > 1 cm2 at little cost (< 5 ¢ cm−2). To demonstrate the utility of this platform, a TEMPO with 5 μm pore size is used to selectively and rapidly isolate immunomagnetically targeted Escherichia coli from heterogeneous suspensions, demonstrating enrichment of ζ > 500 at a flow rate of Φ = 5 mL h−1. Furthermore, the large density of micropores (ρ = 106 cm−2) allows the TEMPO to sort E. coli from unprocessed environmental and clinical samples, as the blockage of a few pores does not significantly change the behavior of the device. PMID:24535921