Particle Filter with Swarm Move for Optimization
Yang, Shengxiang
method in particle swarm optimization (PSO). In this way, the PSO update equation is treated the ability of PSO in searching the optimal position can be embedded into the particle filter optimization in both convergence speed and final fitness in comparison with the PSO algorithm over a set of standard
PID controller tuning using particle filtering optimization
Jie Li; Tianyou Chai; Lisheng Fan; Li Pan; Jingkuan Gong
2010-01-01
The PID controller is one of the most popular controllers, due to its remarkable effectiveness, simplicity of implementation and broad applicability. However, the conventional approach for parameter optimization in PID controller is easy to produce surge and big overshoot, and therefore heuristics optimization methods such as genetic algorithm (GA), particle swarm optimization (PSO) are employed to enhance the capability of
Junpei Zhong; Yu-fai Fung; Mingjun Dai
2010-01-01
Particle Filter (PF) is a sophisticated model estimation technique based on simulation. Due to the natural limitations of\\u000a PF, two problems, namely particle impoverishment and sample size dependency, frequently occur during the particles updating\\u000a stage and these problems will limit the accuracy of the estimation results. In order to alleviate these problems, Ant Colony\\u000a Optimization is incorporated into the generic
Particle filter based on Particle Swarm Optimization resampling for vision tracking
Jing Zhao; Zhiyuan Li
2010-01-01
Particle filter is a powerful tool for vision tracking based on Sequential Monte Carlo framework. The core of particle filter in vision tracking is how to allocate particles to a high posterior area. Particle Swarm Optimization (PSO) is applied to find high likelihood area in this paper. PSO algorithm can search the sample area around the last time object position
Video object tracing based on particle filter with ant colony optimization
Zhou Hao; Xuejie Zhang; Pengfei Yu; Haiyan Li
2010-01-01
Classical particle filter needs large numbers of samples to properly approximate the posterior density of the state evolution. Furthermore, sample impoverishment is an inevitable problem, which is a key issue in the performance of a particle filter. In this paper, a particle filtering algorithm based on ant colony optimization (ACO) was proposed to enhance the performance of particle filter with
Neuromuscular fiber segmentation through particle filtering and discrete optimization
NASA Astrophysics Data System (ADS)
Dietenbeck, Thomas; Varray, François; Kybic, Jan; Basset, Olivier; Cachard, Christian
2014-03-01
We present an algorithm to segment a set of parallel, intertwined and bifurcating fibers from 3D images, targeted for the identification of neuronal fibers in very large sets of 3D confocal microscopy images. The method consists of preprocessing, local calculation of fiber probabilities, seed detection, tracking by particle filtering, global supervised seed clustering and final voxel segmentation. The preprocessing uses a novel random local probability filtering (RLPF). The fiber probabilities computation is performed by means of SVM using steerable filters and the RLPF outputs as features. The global segmentation is solved by discrete optimization. The combination of global and local approaches makes the segmentation robust, yet the individual data blocks can be processed sequentially, limiting memory consumption. The method is automatic but efficient manual interactions are possible if needed. The method is validated on the Neuromuscular Projection Fibers dataset from the Diadem Challenge. On the 15 first blocks present, our method has a 99.4% detection rate. We also compare our segmentation results to a state-of-the-art method. On average, the performances of our method are either higher or equivalent to that of the state-of-the-art method but less user interactions is needed in our approach.
Rajesh Kumar; Anupam Kumar
2010-01-01
We incorporate the optimization problem of two-dimensional infinite impulse response (IIR) recursive filters and the optimization methodology of hybrid multiagent particle swarm optimization (HMAPSO) and then apply the resultant optimized IIR filter in image processing for justifying HMAPSO robustness over other algorithm and its role in optimizing real-time situations. The design of the 2-D IIR filter is reduced to a
NASA Astrophysics Data System (ADS)
Semwal, Girish; Rastogi, Vipul
2014-01-01
We present design optimization of wavelength filters based on long period waveguide gratings (LPWGs) using the adaptive particle swarm optimization (APSO) technique. We demonstrate optimization of the LPWG parameters for single-band, wide-band and dual-band rejection filters for testing the convergence of APSO algorithms. After convergence tests on the algorithms, the optimization technique has been implemented to design more complicated application specific filters such as erbium doped fiber amplifier (EDFA) amplified spontaneous emission (ASE) flattening, erbium doped waveguide amplifier (EDWA) gain flattening and pre-defined broadband rejection filters. The technique is useful for designing and optimizing the parameters of LPWGs to achieve complicated application specific spectra.
14:30 Yuan Cheng (Numerische Mathematik) A Particle Filter based on Optimal Transportation
Baer, Christian
particle filter based on optimal transportation that relaxes this trade-off . 15:00 Sathej Gopalakrishnan infection The human immunodeficiency virus (HIV) targets the human immune system leading to Acquired
A novel finite-wordlength particle swarm optimization technique for FRM IIR digital filters
Seyyed Ali Hashemi; Behrouz Nowrouzian
2011-01-01
A novel technique is presented for finite-wordlength (FW) particle swarm optimization (PSO) of BIBO stable FRM digital filters incorporating bilinear-LDI IIR interpolation subfil- ters. A novel LUT scheme is developed to ensure that the FWPSO automatically searches over permissible FW multiplier coefficient values only in the course of optimization. The salient feature of the proposed LUT scheme is that unlike
Robust Object Tracking via Hierarchical Particle Filter
Wei Sun; Bao-long Guo
2008-01-01
A potential weakness of generic particle filters discussed above is that the particle-based approximation of filtered density is not sufficient to characterize the tail behavior of true density, due to the use of finite mixture approximation; To alleviate this problem, In this paper we propose a general hierarchical particle filtering framework for designing an optimal proposal distribution. The essential idea
IRINO Toshio; Morinosato Wakamiya
1995-01-01
The optimality of the peripheral auditory filter is investigated using operator methods applied to a scale representation. A `gammachirp' function, which consists of a frequency modulated carrier and an envelope of a gamma distribution function, is found to be the optimal auditory filter in terms of minimal uncertainty if the time-scale representation is calculated in the auditory system. The gammatone
Optimal separable correlation filters
NASA Astrophysics Data System (ADS)
McFadden, Frank E.
2002-07-01
Separable filters, because they are specified separately in each dimension, require less memory space and present opportunities for faster computation. Mahalanobis and Kumar1 presented a method for deriving separable correlation filters, but the filters were required to satisfy a restrictive assumption, and were thus not fully optimized. In this work, we present a general procedure for deriving separable versions of any correlation filter, using singular value decomposition (SVD), and prove that this is optimal for separable filters based on the Maximum Average Correlation Height (MACH) criterion. Further, we show that additional separable components may be used to improve the performance of the filter, with only a linear increase in computational and memory space requirements. MSTAR data is used to demonstrate the effects on sharpness of correlation peaks and locational precision, as the number of separable components is varied.
Jason D. Mcewen; Michael P. Hobson; Anthony N. Lasenby
2008-01-01
We derive optimal filters on the sphere in the context of detecting compact objects embedded in a stochastic background process. The matched filter and the scale adaptiv e filter are derived on the sphere in the most general setting, allowing for directional template profiles and filters. The p erfor- mance and relative merits of the two optimal filters are discu
Bounds on the performance of particle filters
NASA Astrophysics Data System (ADS)
Snyder, C.; Bengtsson, T.
2014-12-01
Particle filters rely on sequential importance sampling and it is well known that their performance can depend strongly on the choice of proposal distribution from which new ensemble members (particles) are drawn. The use of clever proposals has seen substantial recent interest in the geophysical literature, with schemes such as the implicit particle filter and the equivalent-weights particle filter. A persistent issue with all particle filters is degeneracy of the importance weights, where one or a few particles receive almost all the weight. Considering single-step filters such as the equivalent-weights or implicit particle filters (that is, those in which the particles and weights at time tk depend only on the observations at tk and the particles and weights at tk-1), two results provide a bound on their performance. First, the optimal proposal minimizes the variance of the importance weights not only over draws of the particles at tk, but also over draws from the joint proposal for tk-1 and tk. This shows that a particle filter using the optimal proposal will have minimal degeneracy relative to all other single-step filters. Second, the asymptotic results of Bengtsson et al. (2008) and Snyder et al. (2008) also hold rigorously for the optimal proposal in the case of linear, Gaussian systems. The number of particles necessary to avoid degeneracy must increase exponentially with the variance of the incremental importance weights. In the simplest examples, that variance is proportional to the dimension of the system, though in general it depends on other factors, including the characteristics of the observing network. A rough estimate indicates that single-step particle filter applied to global numerical weather prediction will require very large numbers of particles.
NASA Astrophysics Data System (ADS)
Stevens, Mark R.; Gutchess, Dan; Checka, Neal; Snorrason, Magnús
2006-05-01
Image exploitation algorithms for Intelligence, Surveillance and Reconnaissance (ISR) and weapon systems are extremely sensitive to differences between the operating conditions (OCs) under which they are trained and the extended operating conditions (EOCs) in which the fielded algorithms are tested. As an example, terrain type is an important OC for the problem of tracking hostile vehicles from an airborne camera. A system designed to track cars driving on highways and on major city streets would probably not do well in the EOC of parking lots because of the very different dynamics. In this paper, we present a system we call ALPS for Adaptive Learning in Particle Systems. ALPS takes as input a sequence of video images and produces labeled tracks. The system detects moving targets and tracks those targets across multiple frames using a multiple hypothesis tracker (MHT) tightly coupled with a particle filter. This tracker exploits the strengths of traditional MHT based tracking algorithms by directly incorporating tree-based hypothesis considerations into the particle filter update and resampling steps. We demonstrate results in a parking lot domain tracking objects through occlusions and object interactions.
Optimal Particle Filters for Tracking a Time-Varying Harmonic or Chirp Signal
Efthimios E. Tsakonas; Nicholas D. Sidiropoulos; Ananthram Swami
2008-01-01
We consider the problem of tracking the time-varying (TV) parameters of a harmonic or chirp signal using particle fil- tering (PF) tools. Similar to previous PF approaches to TV spec- tral analysis, we assume that the model parameters (complex am- plitude, frequency, and frequency rate in the chirp case) evolve ac- cording to a Gaussian AR(1) model; but we concentrate
Noisy Systems – Optimal Linear Filtering
Emeritus Eric Ostertag
\\u000a This chapter deals with noise signals, also called random or stochastic processes, which affect the plant itself or the available measurements. As mentioned earlier, the state estimator will be here a filter.\\u000a The optimal linear filter, if optimization is understood as minimization of the estimation error variance, is the well known Kalman filter. This chapter is organized as follows: after
Head Tracking by Active Particle Filtering
Zhihong Zeng; Songde Ma
2002-01-01
Particle filtering has attracted much attention due to its robust tracking performance in clutter. However, a price to pay for its robustness is the computational cost. Active particle filtering is proposed in this paper. Unlike traditional particle filtering, every particle in active particle filtering is first driven to its local maximum of the likelihood before it is weighted. In this
A Color - based Particle Filter
K. Nummiario; E. Koller Meier; L. V. Gool
2002-01-01
Abstract— Robust real-time tracking of non-rigid objects is a challenging task. Particle filtering has been proven very successful for non-linear and non-Gaussian estimation problems. However, for the tracking of non-rigid objects, the selection of reliable image features is also essential. This paper presents the integration of color distributions into particle filtering, which has typically used edge-based image features. Color distributions
POPULATION BASED PARTICLE FILTERING
Harish Bhaskar; Lyudmila Mihaylova; Simon Maskell
This paper proposes a novel particle flltering strat- egy by combining population Monte Carlo Markov chain methods with sequential Monte Carlo chain particle which we call evolving population Monte Carlo Markov Chain (EP MCMC) flltering. Iter- ative convergence on groups of particles (popula- tions) is obtained using a specifled kernel moving particles toward more likely regions. The proposed technique introduces
ADVANCES IN COST-REFERENCE PARTICLE FILTERING
F. Bugallo; Petar M. Djuric
2006-01-01
Recently, we have proposed a particle filtering-type method- ology, which we refer to as cost-reference particle filtering (CRPF). Its main feature is that it is not based on any partic- ular probabilistic assumptions regarding the studied dynamic model. The concepts of particles and particle streams, how- ever, are the same in CRPF as in standard particle filtering (SPF), but the
Switching particle filters for efficient visual tracking
Takashi Bando; Tomohiro Shibata; Kenji Doya; Shin Ishii
2006-01-01
Abstract In this article, we propose a new particle filtering scheme, called a switching particle filter, which allows robust and accurate visual tracking under typical circumstances of real-time visual tracking. This scheme switches two complementary sampling algorithms, Condensation and Auxiliary Particle Filter, in an on-line fashion based on the confidence of the filtered state of the visual target. The accuracy
Switching particle filters for efficient visual tracking
Takashi Bando; Tomohiro Shibata; Kenji Doya; Shin Ishii
In this article, we propose a new particle filtering scheme, called a switching particle filter, which allows robust and accurate visual tracking under typical circumstances of real-time visual tracking. This scheme switches two complementary sampling algorithms, Condensation and Auxiliary Particle Filter, in an on-line fashion based on the confidence of the filtered state of the visual target. The accuracy and
OPTIMIZATION OF ADVANCED FILTER SYSTEMS
R.A. Newby; G.J. Bruck; M.A. Alvin; T.E. Lippert
1998-04-30
Reliable, maintainable and cost effective hot gas particulate filter technology is critical to the successful commercialization of advanced, coal-fired power generation technologies, such as IGCC and PFBC. In pilot plant testing, the operating reliability of hot gas particulate filters have been periodically compromised by process issues, such as process upsets and difficult ash cake behavior (ash bridging and sintering), and by design issues, such as cantilevered filter elements damaged by ash bridging, or excessively close packing of filtering surfaces resulting in unacceptable pressure drop or filtering surface plugging. This test experience has focused the issues and has helped to define advanced hot gas filter design concepts that offer higher reliability. Westinghouse has identified two advanced ceramic barrier filter concepts that are configured to minimize the possibility of ash bridge formation and to be robust against ash bridges should they occur. The ''inverted candle filter system'' uses arrays of thin-walled, ceramic candle-type filter elements with inside-surface filtering, and contains the filter elements in metal enclosures for complete separation from ash bridges. The ''sheet filter system'' uses ceramic, flat plate filter elements supported from vertical pipe-header arrays that provide geometry that avoids the buildup of ash bridges and allows free fall of the back-pulse released filter cake. The Optimization of Advanced Filter Systems program is being conducted to evaluate these two advanced designs and to ultimately demonstrate one of the concepts in pilot scale. In the Base Contract program, the subject of this report, Westinghouse has developed conceptual designs of the two advanced ceramic barrier filter systems to assess their performance, availability and cost potential, and to identify technical issues that may hinder the commercialization of the technologies. A plan for the Option I, bench-scale test program has also been developed based on the issues identified. The two advanced barrier filter systems have been found to have the potential to be significantly more reliable and less expensive to operate than standard ceramic candle filter system designs. Their key development requirements are the assessment of the design and manufacturing feasibility of the ceramic filter elements, and the small-scale demonstration of their conceptual reliability and availability merits.
Towards robust particle filters for high-dimensional systems
NASA Astrophysics Data System (ADS)
van Leeuwen, Peter Jan
2015-04-01
In recent years particle filters have matured and several variants are now available that are not degenerate for high-dimensional systems. Often they are based on ad-hoc combinations with Ensemble Kalman Filters. Unfortunately it is unclear what approximations are made when these hybrids are used. The proper way to derive particle filters for high-dimensional systems is exploring the freedom in the proposal density. It is well known that using an Ensemble Kalman Filter as proposal density (the so-called Weighted Ensemble Kalman Filter) does not work for high-dimensional systems. However, much better results are obtained when weak-constraint 4Dvar is used as proposal, leading to the implicit particle filter. Still this filter is degenerate when the number of independent observations is large. The Equivalent-Weights Particle Filter is a filter that works well in systems of arbitrary dimensions, but it contains a few tuning parameters that have to be chosen well to avoid biases. In this paper we discuss ways to derive more robust particle filters for high-dimensional systems. Using ideas from large-deviation theory and optimal transportation particle filters will be generated that are robust and work well in these systems. It will be shown that all successful filters can be derived from one general framework. Also, the performance of the filters will be tested on simple but high-dimensional systems, and, if time permits, on a high-dimensional highly nonlinear barotropic vorticity equation model.
Distributed SLAM Using Improved Particle Filter for Mobile Robot Localization
Pei, Fujun; Wu, Mei; Zhang, Simin
2014-01-01
The distributed SLAM system has a similar estimation performance and requires only one-fifth of the computation time compared with centralized particle filter. However, particle impoverishment is inevitably because of the random particles prediction and resampling applied in generic particle filter, especially in SLAM problem that involves a large number of dimensions. In this paper, particle filter use in distributed SLAM was improved in two aspects. First, we improved the important function of the local filters in particle filter. The adaptive values were used to replace a set of constants in the computational process of importance function, which improved the robustness of the particle filter. Second, an information fusion method was proposed by mixing the innovation method and the number of effective particles method, which combined the advantages of these two methods. And this paper extends the previously known convergence results for particle filter to prove that improved particle filter converges to the optimal filter in mean square as the number of particles goes to infinity. The experiment results show that the proposed algorithm improved the virtue of the DPF-SLAM system in isolate faults and enabled the system to have a better tolerance and robustness. PMID:24883362
Research on robust unscented regularized particle filtering
Li Xue; Shesheng Gao; Jianchao Wang
2010-01-01
In nonlinear and non-Gaussian systems, particle filtering is effective but it is difficult to select the importance distribution function and diverges more greatly. Aiming at this problem, the paper represents robust unscented regularized particle filtering to improve the performance of filtering. This algorithm is more suitable for filtering calculation in nonlinear system, not only because overcomes the limitations of the
Robustness and accuracy in particle filtering
LeGland, François
1 #12;2 Robustness and accuracy in particle filtering Application to navigation and tracking Ph of the multimodal density #12;10 Multimodality : mixture filters Maintaining the modes : mixture of particle filters of multimodality · Mixture filters · Application to terrain navigation · Analysis of the Monte Carlo error
NASA Astrophysics Data System (ADS)
Rafiee, Mohammad; Barrau, Axel; Bayen, Alexandre M.
2013-06-01
This article investigates the performance of Monte Carlo-based estimation methods for estimation of flow state in large-scale open channel networks. After constructing a state space model of the flow based on the Saint-Venant equations, we implement the optimal sampling importance resampling filter to perform state estimation in a case in which measurements are available at every time step. Considering a case in which measurements become available intermittently, a random-map implementation of the implicit particle filter is applied to estimate the state trajectory in the interval between the measurements. Finally, some heuristics are proposed, which are shown to improve the estimation results and lower the computational cost. In the first heuristics, considering the case in which measurements are available at every time step, we apply the implicit particle filter over time intervals of a desired size while incorporating all the available measurements over the corresponding time interval. As a second heuristic method, we introduce a maximum a posteriori (MAP) method, which does not require sampling. It will be seen, through implementation, that the MAP method provides more accurate results in the case of our application while having a smaller computational cost. All estimation methods are tested on a network of 19 tidally forced subchannels and 1 reservoir, Clifton Court Forebay, in Sacramento-San Joaquin Delta in California, and numerical results are presented.
Applied particle filter in traffic tracking
Le Hoai Bac; Pham Nam Trung; Le Nguyen Tuong Vu
2006-01-01
This paper introduces a general traffic tracking system which has the ability to detect and track objects moving in the street automatically. By combining the idea of Particle Filter and background subtraction, we propose ODAPF - Object Detection Aided Particle Filter, a framework of multi-object tracking using Particle Filter, which works under the assist of an object detection process. This
Target Tracking Based on Adaptive Particle Filter
Tingting Wang; Jingling Wang; Chuanzhen Li; Hui Wang; Jianbo Liu
2009-01-01
This paper presents a method that can track non-rigid moving objects using adaptive particle filter based on spatiograms. Particle filters offer a probabilistic framework for dynamic state estimation and have proven to work well in target tracking. Two key components of particle filters are observation models and motion models. Firstly, because the observation model based on general color histograms discards
A PSO Accelerated Immune Particle Filter for Dynamic State Estimation
S. Akhtar; A. R. Ahmad; E. M. Abdel-Rahman; T. Naqvi
2011-01-01
Particle Filter (PF) is a flexible and powerful Sequential Monte Carlo (SMC) technique to solve the nonlinear state\\/parameter estimation problems. The generic PF suffers due to degeneracy or sample impoverishment, which adversely affects its performance. In order to overcome this issue of the generic PF, a Particle Swarm Optimization accelerated Immune Particle Filter (PSO-acc-IPF) is proposed in this work. It
Real-time hand tracking using a mean shift embedded particle filter
Caifeng Shan; Tieniu Tan; Yucheng Wei
2007-01-01
Particle filtering and mean shift (MS) are two successful approaches to visual tracking. Both have their respective strengths and weaknesses. In this paper, we propose to integrate advantages of the two approaches for improved tracking. By incorporating the MS optimization into particle filtering to move particles to local peaks in the likelihood, the proposed mean shift embedded particle filter (MSEPF)
NASA Technical Reports Server (NTRS)
Venter, Gerhard; Sobieszczanski-Sobieski Jaroslaw
2002-01-01
The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of the algorithm, Results of numerical experiments for both continuous and discrete applications are presented in the paper. The results indicate that the particle swarm optimization algorithm does locate the constrained minimum design in continuous applications with very good precision, albeit at a much higher computational cost than that of a typical gradient based optimizer. However, the true potential of particle swarm optimization is primarily in applications with discrete and/or discontinuous functions and variables. Additionally, particle swarm optimization has the potential of efficient computation with very large numbers of concurrently operating processors.
Distributed Particle Filter for Target Tracking
Dongbing Gu
2007-01-01
This paper investigates target tracking using a distributed particle filter over sensor networks. Gaussian mixture model is adopted to approximate the posterior distribution of weighted particles in this distributed particle filter. The parameters of Gaussian mixture model are exchanged between neighbor sensor nodes. Each node can obtain the Gaussian mixture model representing particle's posterior distribution through the parameter exchange. With
James N. Kennedy; Russell C. Eberhart
1995-01-01
A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life
Annual energy consumption prediction using particle filters
Osamah A. Alsayegh
2003-01-01
This paper presents a framework for predicting the monthly-annual electric energy consumption (EC) using practical filters, sequential Monte Carlo methods. The particle filtering technique is utilized to describe and track the EC \\
A SIMULATION-BASED OPTIMIZATION APPROACH TO POLYMER EXTRUSION FILTER
Jenkins, Lea
is the effective removal of debris, via filtration, from the polymer melt during the extrusion process. We propose model that describes the deposition of debris particles in the filter. Optimization algorithms are used. ¡ ¡ ¡ ¡ ¡ ¡ ¡ ¡ ¢¡¢¡¢¡¢¡¢¢¡¢¡¢¡¢¡¢ £¤£¤£¤£¤£¤£¤£¤££¤£¤£¤£¤£¤£¤£¤££¤£¤£¤£¤£¤£¤£¤£ ¥¤¥¤¥¤¥¤¥¤¥¤¥¥¤¥¤¥¤¥¤¥¤¥¤¥¥¤¥¤¥¤¥¤¥¤¥¤¥ .. . Godets Convergence Guide Air Quench Filter Metering Pump Spinneret Spin Bobbin Polymer Melt Figure 1
Manoeuvring target tracking in clutter using particle filters
MARK R. MORELANDE; SUBHASH CHALLA
2005-01-01
A particle filter (PF) is a recursive numerical technique which uses random sampling to approximate the optimal solution to target tracking problems involving nonlinearities and\\/or non-Gaussianity. A set of particle filtering methods for tracking and manoeuvering target in clutter from angle-only measurements is presented and evaluated. The aim is to compare PFs to a well-established tracking algorithm, the IMM-PDA-EKF (interacting
Mapping large scale environments by combining Particle Filter and Information Filter
Mahesh Mohan; K. Madhava Krishna
2010-01-01
This paper presents two approaches to combine two popular mapping strategies, namely Particle Filters and Information Filters. The first method describes how the Particle Filter can be incorporated into the Information Filter framework by building local submaps using the Particle Filter and combining them using a Information Filter to obtain a global map. Using the Particle Filter locally reduces the
Robust regularized particle filter for terrain navigation
Achille Murangira; Christian Musso; Karim Dahia; Jean-Michel Allard
2011-01-01
In this paper, we address the multimodality of the filtering distribution in the context of ambiguous measurements. In terrain navigation, the similarities in the elevation profiles can lead to multiple modes in the posterior distribution. Typical experiments based on standard particle filters show that the filter tends to lose the true mode as time goes on. Using a clustering algorithm,
Direct electromagnetic optimization of microwave filters
S. Bila; D. Baillargeat; M. Aubourg; S. Verdeyme; P. Guillon; F. Seyfert; J. Grimm; L. Baratchart; C. Zanchi; J. Sombrin
2001-01-01
This article explores an optimization procedure for microwave filters and multiplexers. The procedure is initialized by a classical filter synthesis based on a segmented electromagnetic synthesis that provides the basic dimensions of the structure. The optimization loop, which combines a global electromagnetic analysis and a coupling identification, improves the structure response compared to an empirical optimization
Rickard Karlsson ISIS Particle Filtering in Practice
Zhao, Yuxiao
Rickard Karlsson ISIS 2004-11-04 Particle Filtering in Practice Sensor fusion, Positioning and Tracking Rickard Karlsson Automatic Control Linköping University, SWEDEN rickard@isy.liu.se #12;Rickard Karlsson ISIS Linköping 2004-11-05 Particle Filtering within ISIS from my perspective #12;Rickard Karlsson
A Robust Particle Filter for People Tracking
Bo Yang; Xinting Pan; Aidong Men; Xiaobo Chen
2010-01-01
Among various tracking algorithms, particle filtering (PF) is a robust and accurate one for different applications. It also allows data fusion from different sources due to its inherent property without increasing the dimension of the state vector. In this paper, we propose three strategies to improve the performance of particle filters. First, our approach combines the foreground region with the
Progressive correction for regularized particle filters
N. Oudjane; C. Musso
2000-01-01
Particle methods have been recently proposed to deal with the nonlinear filtering problem. These are Monte Carlo methods that can provide a nonparametric approximation to the signal conditional distribution even in nonlinear and non Gaussian cases, without depending on the state space dimension. We present a new version of regularized particle filter using a progressive correction (PC) principle which improves
Fuzzy Particle Filtering for Uncertain Systems
Hao Wu; Fuchun Sun; Huaping Liu
2008-01-01
In this paper, we propose a novel fuzzy particle filtering method for online estimation of nonlinear dynamic systems with fuzzy uncertainties. This approach uses a sequential fuzzy simulation to approximate the possibilities of the state intervals in the state-space, and estimates the state by fuzzy expected value operator. To solve the degeneracy problem of the fuzzy particle filter, one corresponding
Tracking Multiple Objects with Particle Filtering
Del Moral , Pierre
Tracking Multiple Objects with Particle Filtering C. HUE J-P. LE CADRE, Member, IEEE IRISA of the classical particle filter where the stochastic vector of assignment is estimated by a Gibbs sampler is not robust enough in many situations. As long as the association is considered in a deterministic way
Efficient parallelized particle filter design on CUDA
Min-An Chao; Chun-Yuan Chu; Chih-Hao Chao; An-Yeu Wu
2010-01-01
Particle filtering is widely used in numerous nonlinear applications which require reconfigurability, fast prototyping, and online parallel signal processing. The emerging computing platform, CUDA, may be regarded as the most appealing platform for such implementation. However, there are not yet literatures exploring how to utilize CUDA for particle filters. This parer aims to provide two design techniques, A) finite-redraw importance-maximizing
Particle filtering in the presence of outliers
C. S. Maiz; J. Miguez; P. M. Djuric
2009-01-01
Particle filters have become very popular signal processing tools for problems that involve nonlinear tracking of an unobserved signal of interest given a series of related observations. In this paper we propose a new scheme for particle filtering when the observed data are possibly contaminated with outliers. An outlier is an observation that has been generated by some (unknown) mechanism
Fourier transform particle flow for nonlinear filters
NASA Astrophysics Data System (ADS)
Daum, Fred; Huang, Jim
2013-05-01
We derive five new algorithms to design particle flow for nonlinear filters using the Fourier transform of the PDE that determines the flow of particles corresponding to Bayes' rule. This exploits the fact that our PDE is linear with constant coefficients. We also use variance reduction and explicit stabilization to enhance robustness of the filter. Our new filter works for arbitrary smooth nowhere vanishing probability densities.
The Auxiliary Extended and Auxiliary Unscented Kalman Particle Filters
Laurence Smith; Victor Aitken
2007-01-01
This paper proposes two new particle filters, namely, the auxiliary extended Kalman particle filter (AEKPF) and the auxiliary unscented Kalman particle filter (AUKPF). The theory governing the newly proposed filtering techniques is developed and the algorithms are described and contrasted. Next, a series of tests is presented in which the new filters are compared against the extended Kalman filter (EKF),
Particle Filtering for Multisensor Data Fusion with Switching Observation Models.
Caron, François
1 Particle Filtering for Multisensor Data Fusion with Switching Observation Models. Application are Markovian. We propose a family of efficient particle filters, for both synchronous and asynchronous sensor, Particle Filter, Multisensor System, Data Fusion, Global Positioning System, Switching Observation Model
Optimal and robust noncausal filter formulations
Garry A. Einicke
2006-01-01
The paper describes an optimal minimum-variance noncausal filter or fixed-interval smoother. The optimal solution involves a cascade of a Kalman predictor and an adjoint Kalman predictor. A robust smoother involving H? predictors is also described. Filter asymptotes are developed for output estimation and input estimation problems which yield bounds on the spectrum of the estimation error. These bounds lead to
The non-recursive estimation using a batch filter based on particle filtering
Young-Rok Kim; Sang-Young Park; Chan-Deok Park
2012-01-01
In this paper, the non-recursive estimation algorithm using a batch filter based on particle filtering is developed and utilized for one-dimensional nonlinear example. For comparison study, algorithms of a batch filter based on unscented transformation and generic particle filtering are briefly reviewed and new algorithm of a batch filter based on particle filtering is presented. For verification of presented batch
Near-Optimal deterministic filtering on the Rotation Mohammad Zamani, Jochen Trumpf, Member, IEEE,
Trumpf, Jochen
(EKF). Our results indicate that the proposed filter achieves better robustness against a range.g. the Extended Kalman Filter (EKF) [3]. Other meth- ods such as particle filters [4] or the Unscented Kalman1 Near-Optimal deterministic filtering on the Rotation Group Mohammad Zamani, Jochen Trumpf, Member
Diagnosis of a Roller Bearing Using Deterministic Particle Filtering
Boyer, Edmond
Diagnosis of a Roller Bearing Using Deterministic Particle Filtering Ouafae Bennis 1 and Frédéric of a roller bearing is presented as a problem of optimal estimation of a hidden fault, via measures delivered are in a discrete set. To determine the state of the roller bearing, we estimate the value of this term using
Initializing An Unscented Kalman Filter Using A Particle Filter
Adam J. Dean; Jack W. Langelaan; Sean N. Brennan; PF UKF
This work develops an algorithm to initialize an Unscented Kalman Filter using a Particle Filter for applications with initial non-Gaussian probability density functions. The method is applied to estimating the position of a road vehicle along a one-mile test track using terrain-based localization where the pitch response of the vehicle is compared to a pre- measured pitch map of the
The Improved Particle Filter for Object Tracking
Qicong Wang; Jilin Liu
2006-01-01
We address the problem of object tracking encountered in video processing. The proposed approach is mainly composed of object modeling, the improved particle filter and mixture filtering. First, each of visual objects can be modeled by multi-part color likelihood model. To tackle self-occlusion of the tracked objects, the color distribution representing the tracked object can be updated over time. We
OPTIMIZATION OF ADVANCED FILTER SYSTEMS
R.A. Newby; M.A. Alvin; G.J. Bruck; T.E. Lippert; E.E. Smeltzer; M.E. Stampahar
2002-06-30
Two advanced, hot gas, barrier filter system concepts have been proposed by the Siemens Westinghouse Power Corporation to improve the reliability and availability of barrier filter systems in applications such as PFBC and IGCC power generation. The two hot gas, barrier filter system concepts, the inverted candle filter system and the sheet filter system, were the focus of bench-scale testing, data evaluations, and commercial cost evaluations to assess their feasibility as viable barrier filter systems. The program results show that the inverted candle filter system has high potential to be a highly reliable, commercially successful, hot gas, barrier filter system. Some types of thin-walled, standard candle filter elements can be used directly as inverted candle filter elements, and the development of a new type of filter element is not a requirement of this technology. Six types of inverted candle filter elements were procured and assessed in the program in cold flow and high-temperature test campaigns. The thin-walled McDermott 610 CFCC inverted candle filter elements, and the thin-walled Pall iron aluminide inverted candle filter elements are the best candidates for demonstration of the technology. Although the capital cost of the inverted candle filter system is estimated to range from about 0 to 15% greater than the capital cost of the standard candle filter system, the operating cost and life-cycle cost of the inverted candle filter system is expected to be superior to that of the standard candle filter system. Improved hot gas, barrier filter system availability will result in improved overall power plant economics. The inverted candle filter system is recommended for continued development through larger-scale testing in a coal-fueled test facility, and inverted candle containment equipment has been fabricated and shipped to a gasifier development site for potential future testing. Two types of sheet filter elements were procured and assessed in the program through cold flow and high-temperature testing. The Blasch, mullite-bonded alumina sheet filter element is the only candidate currently approaching qualification for demonstration, although this oxide-based, monolithic sheet filter element may be restricted to operating temperatures of 538 C (1000 F) or less. Many other types of ceramic and intermetallic sheet filter elements could be fabricated. The estimated capital cost of the sheet filter system is comparable to the capital cost of the standard candle filter system, although this cost estimate is very uncertain because the commercial price of sheet filter element manufacturing has not been established. The development of the sheet filter system could result in a higher reliability and availability than the standard candle filter system, but not as high as that of the inverted candle filter system. The sheet filter system has not reached the same level of development as the inverted candle filter system, and it will require more design development, filter element fabrication development, small-scale testing and evaluation before larger-scale testing could be recommended.
Estimation of optimal Kalman filter gain from non-optimal filter residuals
NASA Technical Reports Server (NTRS)
Chen, Chung-Wen; Huang, Jen-Kuang
1991-01-01
This paper presents a novel method of estimating the optimal steady state Kalman filter gain of a linear discrete time-invariant system from a non-optimal Kalman filter residual sequence. The relation between the optimal residual sequence and a signal derived from the non-optimal residual sequence is described by a Moving Average (MA) model whose coefficients are expressed in terms of the state space parameters and the optimal steady state Kalman filter gain. In order to identify the MA model, a whitening filter of the derived signal, which corresponds to an AutoRegressive (AR) model of the signal, is first identified using the least-squares method. Then the inverse filter of the whitening filter, which corresponds to the MA model, is calculated. From the coefficients of the identified MA model, the optimal steady state Kalman filter gain can be obtained. Numerical example is provided to illustrate the feasibility of this approach.
Riccardo Poli; James Kennedy; Tim Blackwell
2007-01-01
Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. As researchers have learned about\\u000a the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects\\u000a of the various parameters and aspects of the algorithm. This paper comprises a snapshot of particle swarming from the authors’\\u000a perspective, including variations in the
Distributed Particle Filter Implementation with Intermittent/Irregular Consensus Convergence
Mohammadi, Arash
2011-01-01
Motivated by non-linear, non-Gaussian, distributed multi-sensor/agent navigation and tracking applications, we propose a multi-rate consensus/fusion based framework for distributed implementation of the particle filter (CF/DPF). The CF/DPF framework is based on running localized particle filters to estimate the overall state vector at each observation node. Separate fusion filters are designed to consistently assimilate the local filtering distributions into the global posterior by compensating for the common past information between neighbouring nodes. The CF/DPF offers two distinct advantages over its counterparts. First, the CF/DPF framework is suitable for scenarios where network connectivity is intermittent and consensus can not be reached between two consecutive observations. Second, the CF/DPF is not limited to the Gaussian approximation for the global posterior density. A third contribution of the paper is the derivation of the optimal posterior Cram\\'er-Rao lower bound (PCRLB) for the distributed arc...
Box Particle Filtering: Theory and Applications Lyudmila Mihaylova
Mihaylova, Lyudmila
Box Particle Filtering: Theory and Applications Lyudmila Mihaylova Lancaster University, UK Email.mihaylova@lancaster.ac.uk Jointly wiBox particle filtering: Theory and Applications September 3-6, 2012 1 / 46 #12;Outline 1 About the Box Particle Filter - Motivation Element of Interval Analysis 2 Box Particle Filter: Derivation 3
Optimal Multiobjective Design of Digital Filters Using Taguchi Optimization Technique
NASA Astrophysics Data System (ADS)
Ouadi, Abderrahmane; Bentarzi, Hamid; Recioui, Abdelmadjid
2014-01-01
The multiobjective design of digital filters using the powerful Taguchi optimization technique is considered in this paper. This relatively new optimization tool has been recently introduced to the field of engineering and is based on orthogonal arrays. It is characterized by its robustness, immunity to local optima trapping, relative fast convergence and ease of implementation. The objectives of filter design include matching some desired frequency response while having minimum linear phase; hence, reducing the time response. The results demonstrate that the proposed problem solving approach blended with the use of the Taguchi optimization technique produced filters that fulfill the desired characteristics and are of practical use.
Optimal multiobjective design of digital filters using spiral optimization technique.
Ouadi, Abderrahmane; Bentarzi, Hamid; Recioui, Abdelmadjid
2013-01-01
The multiobjective design of digital filters using spiral optimization technique is considered in this paper. This new optimization tool is a metaheuristic technique inspired by the dynamics of spirals. It is characterized by its robustness, immunity to local optima trapping, relative fast convergence and ease of implementation. The objectives of filter design include matching some desired frequency response while having minimum linear phase; hence, reducing the time response. The results demonstrate that the proposed problem solving approach blended with the use of the spiral optimization technique produced filters which fulfill the desired characteristics and are of practical use. PMID:24083108
BOX-PARTICLE INTENSITY FILTER M. Schikora1,3
Mihaylova, Lyudmila
BOX-PARTICLE INTENSITY FILTER M. Schikora1,3 , A. Gning2 , L. Mihaylova2 , D. Cremers3 , W. Koch1-Target Tracking, Box Particle Filters, Poisson Point Processes, Intensity Filter, Interval Measure- ments Abstract This paper develops a novel approach for multi-target track- ing, called box-particle intensity filter (box
An implicit particle filter for large dimensional data assimilation problems
NASA Astrophysics Data System (ADS)
Morzfeld, M.; Chorin, A. J.; Tu, X.
2010-12-01
Particle filters for data assimilation are usually presented in terms of an Ito stochastic ordinary differential equation (SODE). The task is to estimate the state a(t) of the SODE, with additional information provided by noisy observations bn, n=1,2,..., of this state. In principle, the solution of this problem is known: the optimal estimate of the state is the expected value of the solution of the model conditioned on available observations. The conditional mean can be calculated once the conditional probability density function (pdf) pn+1=p(a(tn+1)|b0,..., bn) is known. A particle filters approximates pn+1 by sequential Monte Carlo. A Sampling-Importance-Resampling (SIR) filter constructs, at each time tn, a prior density by following replicas of the model (called particles). The prior is updated by sampling weights determined by the observations bn+1, to yield a posterior density that approximates pn+1. Because the observations are not used when constructing the prior, particles paths are very likely to stray into regions of low probability and the number of particles required can grow catastrophically, especially if the dimension of the SODE is large. The implicit particle filter is a new sequential Monte Carlo method to solve the data assimilation problem. The filter was devised for large dimensional, non-linear, and non-Gaussian problems in which current methods fail or yield poor results. In essence, the implicit filter reverses the standard procedure described above. It first assigns a probability to each particle and then finds a sample that assumes it. This reversed procedure focusses all particles towards the observations and, thus, generates a thin particle beam to keep the particles in the high probability domain. Because the filter produces high probability samples only, the number of particles required remains manageable. We present a new and very efficient implementation of the implicit particle filter for use in large dimensional data assimilation problems. Our implementation relies on a clever non-linear change of variables. The change of variables reduces a data assimilation problem of arbitrary size to solving a single algebraic equation in only one variable. We demonstrate the performance of our filter by applying it to the stochastic Kuramoto-Sivashinsky (SKS) equation. This equation is known to exhibit space-time chaos. We project its solution into an N-dimensional subspace spanned by N Fourier modes to obtain an N-dimensional Ito-Galerkin approximation. We vary the viscosity and the continuity (in space) of the noise process driving the equation to generate a variety of test problems, with dimensions ranging from 32 to 512. Linear and nonlinear observation operators are considered. We also outline how to deal with observations that are sparse in space and time. The performance of the implicit filters is compared to the performance of an SIR filter. The numerical results confirm that the implicit filter gives accurate state estimates by tracking only very few, but sharply focused, particles. The implicit filter also outperforms SIR in all cases considered.
Optimization of Cosine Modulated Filter Bank for Narrowband RFI
Rajan, Dinesh
Optimization of Cosine Modulated Filter Bank for Narrowband RFI Yingsi Liang Department frequency interfer- ence (RFI). The conventionally used optimization criterion for bandpass filtering ripple. The proposed optimization scheme is designed particularly to combat RFI with completely known
Bridging the ensemble Kalman filter and particle filters: the adaptive Gaussian mixture filter
Andreas S. Stordal; Hans A. Karlsen; Geir Nævdal; Hans J. Skaug; Brice Vallès
2011-01-01
The nonlinear filtering problem occurs in many scientific areas. Sequential Monte Carlo solutions with the correct asymptotic\\u000a behavior such as particle filters exist, but they are computationally too expensive when working with high-dimensional systems.\\u000a The ensemble Kalman filter (EnKF) is a more robust method that has shown promising results with a small sample size, but the\\u000a samples are not guaranteed
Assignment-based particle labeling for PHD particle filter
NASA Astrophysics Data System (ADS)
Danu, Daniel G.; Lang, Thomas; Kirubarajan, Thia
2009-08-01
The probability hypothesis density (PHD) filter is an estimator that approximates, on a given scenario, the multitarget distribution through its first-order multitarget moment. This paper presents two particles labeling algorithms for the PHD particle filter, through which the information on individual targets identity (otherwise hidden within the first-order multitarget moment) is revealed and propagated over time. By maintaining all particles labeled at any time, the individual target distribution estimates are obtained under the form of labeled particle clouds, within the estimated PHD. The partitioning of the PHD into distinct clouds, through labeling, provides over time information on confirmed tracks identity, tracks undergoing initiation or deletion at a given time frame, and clutter regions, otherwise not available in a regular PHD (or track-labeled PHD). Both algorithms imply particles tagging since their inception, in the measurements sampling step, and their re-tagging once they are merged into particle clouds of already confirmed tracks, or are merged for the purpose of initializing new tracks. Particles of a confirmed track cloud preserve their labels over time frames. Two data associations are involved in labels management; one assignment merges measurement clouds into particle clouds of already confirmed tracks, while the following 2D-assignment associates particle clouds corresponding to non-confirmed tracks over two frames, for track initiation. The algorithms are presented on a scenario containing two targets with close and crossing trajectories, with the particle labeled PHD filter tracking under measurement origin uncertainty due to observations variance and clutter.
Modifed Resampling Based Particle Filter for Visual Tracking with MPH
Chi-Min Oh; Chil-Woo Lee
2010-01-01
In this paper we propose a particle filter based strategic approach to enhance the performance of visual tracking system with a new re-sampling algorithm. In any particle filter based application especially in visual tracking system, re-sampling is a vital process in the implementation of particle filtering. Usually it is a linear function of particle weight calculation to know the number
Particle Filtering for Robust Single Camera Localisation
Mark Pupilli; Andrew Calway
This paper summarises recent work on vision based localisation of a moving camera using particle filtering. We are interested in real-time opera- tion for applications in mobile and wearable computing, in which the camera is worn or held by a user. Specifically, we aim for localisation algorithms which are robust to the real-life motions associated with human activity and to
Visual tracking with singular value particle filter
Xiling Luo; Yan Huang
2010-01-01
Robust tracking is an important and challenging problem in computer vision. Most existing algorithms do not work well if there are confusing objects in the surrounding environment or the target appearance has a significant change. This paper describes a novel particle filter for object tracking. First, we treat the blob image of the object as a matrix and adopt singular
Adaptive Hybrid Mean Shift and Particle Filter
Phong Le; Anh Duc Duong; Hai Quan Vu; Nam Trung Pham
2009-01-01
The changing of dynamic models in object tracking can cause high errors in state estimation algorithms. In this paper, we propose a method, adaptive hybrid mean shift and particle filter (AHMSPF), to solve this problem. AHMSPF consists of three stages. First, the mean shift algorithm is employed to search an object candidate near the target state. Then, if this candidate
A field transition particle filter tracking algorithm
De-Jiang Xu; Ze-Lin Shi; Xin-Rong Yu; Qing-Hai Ding; Hai-Bo Luo
2011-01-01
Visual tracking is a critical task in many computer vision applications such as surveillance, vehicle tracking, and motion analysis. The challenges in designing a robust visual tracking algorithm are caused by the presence of background clutter, occlusion, and illumination changes. In this paper, we propose a visual tracking algorithm in a particle filter framework to overcome these three challenging issues.
Particle Filter Theory and Practice with Positioning
LeGland, FranÃ§ois
come from real-time implementations. This part also provides complete code examples. Manuscript, IEEE Linkoping University Sweden The particle filter (PF) was introduced in 1993 as a numerical the part of the theory that is most important for applications and to survey a number of illustrative
A robust particle filter for state estimation — with convergence results
Xiao-Li Hu; T. B. Schon; Lennart Ljung
2007-01-01
Particle filters are becoming increasingly important and useful for state estimation in nonlinear systems. Many filter versions have been suggested, and several results on convergence of filter properties have been reported. However, apparently a result on the convergence of the state estimate itself has been lacking. This contribution describes a general framework for particle filters for state estimation, as well
Optimal subband filters to maximize coding gain
NASA Astrophysics Data System (ADS)
Tanimoto, Masayuki; Yamada, Akio; Wakatsuki, Norio
1993-10-01
The optimal analysis/synthesis filters giving the maximum coding gain are derived in subband schemes. The optimal analysis filters consist of the emphasis of the picture signal and ideal band-splitting. The characteristics of the emphasis is determined by the spectrum of the picture signal. A large improvement of coding gain is achieved by the subband scheme with the optimal subband filters obtained here. Approximated emphasis characteristic determined from a spectrum model of picture signals can be used and the ideal band-splitting filters can be replaced by conventional subband filters since the degradation of coding gain due to these approximations is small. Computer simulation of super HD image coding by the proposed scheme is performed. SN ratio of the reconstructed image is increased and edges are reconstructed very well compared to the conventional subband scheme. The proposed scheme is very suited to super HD image coding since the improvement of SN ratio is large for images with high correlation between the neighboring pixels.
MEDOF - MINIMUM EUCLIDEAN DISTANCE OPTIMAL FILTER
NASA Technical Reports Server (NTRS)
Barton, R. S.
1994-01-01
The Minimum Euclidean Distance Optimal Filter program, MEDOF, generates filters for use in optical correlators. The algorithm implemented in MEDOF follows theory put forth by Richard D. Juday of NASA/JSC. This program analytically optimizes filters on arbitrary spatial light modulators such as coupled, binary, full complex, and fractional 2pi phase. MEDOF optimizes these modulators on a number of metrics including: correlation peak intensity at the origin for the centered appearance of the reference image in the input plane, signal to noise ratio including the correlation detector noise as well as the colored additive input noise, peak to correlation energy defined as the fraction of the signal energy passed by the filter that shows up in the correlation spot, and the peak to total energy which is a generalization of PCE that adds the passed colored input noise to the input image's passed energy. The user of MEDOF supplies the functions that describe the following quantities: 1) the reference signal, 2) the realizable complex encodings of both the input and filter SLM, 3) the noise model, possibly colored, as it adds at the reference image and at the correlation detection plane, and 4) the metric to analyze, here taken to be one of the analytical ones like SNR (signal to noise ratio) or PCE (peak to correlation energy) rather than peak to secondary ratio. MEDOF calculates filters for arbitrary modulators and a wide range of metrics as described above. MEDOF examines the statistics of the encoded input image's noise (if SNR or PCE is selected) and the filter SLM's (Spatial Light Modulator) available values. These statistics are used as the basis of a range for searching for the magnitude and phase of k, a pragmatically based complex constant for computing the filter transmittance from the electric field. The filter is produced for the mesh points in those ranges and the value of the metric that results from these points is computed. When the search is concluded, the values of amplitude and phase for the k whose metric was largest, as well as consistency checks, are reported. A finer search can be done in the neighborhood of the optimal k if desired. The filter finally selected is written to disk in terms of drive values, not in terms of the filter's complex transmittance. Optionally, the impulse response of the filter may be created to permit users to examine the response for the features the algorithm deems important to the recognition process under the selected metric, limitations of the filter SLM, etc. MEDOF uses the filter SLM to its greatest potential, therefore filter competence is not compromised for simplicity of computation. MEDOF is written in C-language for Sun series computers running SunOS. With slight modifications, it has been implemented on DEC VAX series computers using the DEC-C v3.30 compiler, although the documentation does not currently support this platform. MEDOF can also be compiled using Borland International Inc.'s Turbo C++ v1.0, but IBM PC memory restrictions greatly reduce the maximum size of the reference images from which the filters can be calculated. MEDOF requires a two dimensional Fast Fourier Transform (2DFFT). One 2DFFT routine which has been used successfully with MEDOF is a routine found in "Numerical Recipes in C: The Art of Scientific Programming," which is available from Cambridge University Press, New Rochelle, NY 10801. The standard distribution medium for MEDOF is a .25 inch streaming magnetic tape cartridge (Sun QIC-24) in UNIX tar format. MEDOF was developed in 1992-1993.
Optimal design of active EMC filters
NASA Astrophysics Data System (ADS)
Chand, B.; Kut, T.; Dickmann, S.
2013-07-01
A recent trend in automotive industry is adding electrical drive systems to conventional drives. The electrification allows an expansion of energy sources and provides great opportunities for environmental friendly mobility. The electrical powertrain and its components can also cause disturbances which couple into nearby electronic control units and communication cables. Therefore the communication can be degraded or even permanently disrupted. To minimize these interferences, different approaches are possible. One possibility is to use EMC filters. However, the diversity of filters is very large and the determination of an appropriate filter for each application is time-consuming. Therefore, the filter design is determined by using a simulation tool including an effective optimization algorithm. This method leads to improvements in terms of weight, volume and cost.
Sampling Strategies for Particle Filtering SLAM Kristopher R. Beevers
Bystroff, Chris
. A particle filter for SLAM represents the posterior distribution of the robot's trajectory using a setSampling Strategies for Particle Filtering SLAM Kristopher R. Beevers Department of Computer strategies for Rao-Blackwellized particle filtering SLAM. Two of the strategies, called fixed-lag roughening
Robust particle filters via sequential pairwise reparameterized Gibbs sampling
Columbia University
1 Robust particle filters via sequential pairwise reparameterized Gibbs sampling Liam Paninski, Kamiar Rahnama Rad, and Michael Vidne Abstract--Sequential Monte Carlo ("particle filtering") meth- ods, and therefore standard particle filtering methods can fail in these settings. We present a fil- tering method
Research on fuzzy robust adaptive unscented particle filtering
Yi Gao; Shesheng Gao
2011-01-01
This paper present a new fuzzy robust adaptive Unscented particle filtering method based on the fuzzy control theory. This method absorbs the advantages of the fuzzy control theory, the robust adaptive filtering and the Unscented particle filtering. Using the influence of the gross errors in the observation vectors on the state vector parameters to obtain the robust adaptive Unscented particle
Face detection and tracking using a Boosted Adaptive Particle Filter
Wenlong Zheng; Suchendra M. Bhandarkar
2009-01-01
A novel algorithm, termed a Boosted Adaptive Particle Filter (BAPF), for integrated face detection and face tracking is proposed. The proposed algorithm is based on the synthesis of an adaptive particle filtering algorithm and the AdaBoost face detection algorithm. An Adaptive Particle Filter (APF), based on a new sampling technique, is proposed. The APF is shown to yield more accurate
A Boosted Adaptive Particle Filter for Face Detection and Tracking
Wenlong Zheng; Suchendra M. Bhandarkar
2006-01-01
A novel algorithm, termed a Boosted Adaptive Particle Filter (BAPF), for integrated face detection and face tracking is proposed. The proposed algorithm is based on the synthesis of an adaptive particle filtering algorithm and an AdaBoost face detection algorithm. A novel Adaptive Particle Filter (APF), based on a new sampling technique, is proposed to obtain accurate estimates of the proposal
A Robust Particle Filter for State Estimation with Convergence Results
Schön, Thomas
A Robust Particle Filter for State Estimation with Convergence Results Xiao-Li Hu, Thomas B. Sch function we refer to Section V. The contribution of this paper is to provide a robust particle filter¨on and Lennart Ljung Abstract-- Particle filters are becoming increasingly impor- tant and useful for state
Tracking target based on particle filtering and Mean Shift
Yun Liao; Hua Zhou; Zhihong Liang; Yin Zhang; JunHui Liu; Lei Su
2011-01-01
Tracking object in video raises issue when the object makes random and rapid movement. This article presented a four-way prediction tracking algorithm based on particle filtering algorithm and Mean Shift algorithm. The algorithm combines respective advantages from both particle filtering algorithm and Mean Shift Algorithm. First, it use particle filtering algorithm to predict the possible region of target object. After
Unified Particle Swarm Optimization for Solving Constrained Engineering Optimization Problems
Parsopoulos, Konstantinos
Unified Particle Swarm Optimization for Solving Constrained Engineering Optimization Problems K investigate the performance of the recently proposed Uni- fied Particle Swarm Optimization method and global variant of Particle Swarm Optimization are re- ported and discussed. 1 Introduction Many
A field transition particle filter tracking algorithm
NASA Astrophysics Data System (ADS)
Xu, De-jiang; Shi, Ze-lin; Yu, Xin-rong; Ding, Qing-hai; Luo, Hai-bo
2011-08-01
Visual tracking is a critical task in many computer vision applications such as surveillance, vehicle tracking, and motion analysis. The challenges in designing a robust visual tracking algorithm are caused by the presence of background clutter, occlusion, and illumination changes. In this paper, we propose a visual tracking algorithm in a particle filter framework to overcome these three challenging issues. Particle filter is an inference technique for estimating the unknown motion state from a noisy collection of observations, so we employ particle filter to learn the trajectory of a target. The proposed algorithm depends on the learned trajectory to predict the position of a target at a new frame, and corrects the predication by a process that can be entitled field transition. At the beginning of the tracking stage, a set of disturbance templates around the target template are accurately selected and defined as particles. During tracking, a position of the tracked target is firstly predicted based on the learned motion state, and then we take the normalized cross-correlation coefficient as a level to select the most suitable field transition parameters of the predicted position from the corresponding parameters of the particles. After judging the target is not occluded, we apply the named field transition with the selected parameters to compensate the predicted position to the accurate location of the target, meanwhile, we make use of the calculated cross-correlation coefficient as a posterior knowledge to update the weights of all the particles for the next prediction. In order to evaluate the performance of the proposed tracking algorithm, we test the approach on challenging sequences involving heavy background clutter, severe occlusions, and drastic illumination changes. Comparative experiments have demonstrated that this method makes a more significant improvement in efficiency and accuracy than two previously proposed algorithms: the mean shift tracking algorithm (MS) and the covariance tracking algorithm (CT).
Improved particle filter for object tracking
Tao Zhang; Shu-min Fei
2011-01-01
Robust real-time tracking of non-rigid objects is a challenging task. Color is a powerful feature for tracking deformable objects in image sequences with complex backgrounds. Color distribution is applied, as it is robust to partial occlusion, is rotation and scale invariant and computationally efficient. Particle filter has been proven very successful for non-linear and non-Gaussian estimation tracking problems. The article
MULTIOBJECTIVE OPTIMIZATION USING PARALLEL VECTOR EVALUATED PARTICLE SWARM OPTIMIZATION
Coello, Carlos A. Coello
MULTIOBJECTIVE OPTIMIZATION USING PARALLEL VECTOR EVALUATED PARTICLE SWARM OPTIMIZATION K Swarm Optimization (VEPSO) method for multiobjective problems. Experiments on well known and widely used Particle Swarm Optimization, Multiobjective Optimiza- tion, PVM 1 Introduction Multiobjective optimization
A Box Particle Filter for Stochastic and Set-theoretic Measurements with Association
Mihaylova, Lyudmila
measurements with association uncertainty. The optimal theoretical solution can be formulated in the framework of random set theory as the Bernoulli filter for interval measurements. The straightforward particle filter to as the set-theoretic uncertainty [3], vagueness [4] or imprecision [5]. The importance and distinctness
Bayesian auxiliary particle filters for estimating neural tuning parameters
John Mountney; Marc Sobel; Iyad Obeid
2009-01-01
A common challenge in neural engineering is to track the dynamic parameters of neural tuning functions. This work introduces the application of Bayesian auxiliary particle filters for this purpose. Based on Monte-Carlo filtering, Bayesian auxiliary particle filters use adaptive methods to model the prior densities of the state parameters being tracked. The observations used are the neural firing times, modeled
Robust channel estimation using the hybrid particle filter
Ronald A. Iltis
2001-01-01
Mobile wireless channels are subject to multipath with potentially large Doppler spreads. Previous joint channel and delay estimation algorithms, based for example on the extended Kalman filter (EKF), are subject to divergence. We present a hybrid particle filter (HPF), also called the particle extended Kalman filter (PEKF) for delay\\/channel estimation in direct-sequence CDMA applications. The PEKF uses a sampling technique
Sequential Particle Swarm Optimization for Visual Tracking
Maybank, Steve
in the evolutionary computing, we propose a sequential particle swarm based searching strategy for robust visual tracking. Unlike the independent particles in the conventional particle filter, the particles in our the sample impoverishment problem suffered by particle filters. Experimental results demonstrate that
Semi-supervised particle filter for visual tracking
Huaping Liu; Fuchun Sun
2009-01-01
In this paper, a semi-supervised particle filter approach is proposed for visual tracking. The combination of semi-supervised learning and particle filter is very natural since the unlabelled samples are generated by particle propagation. In addition, the proposed semi-supervised particle filter can online select different features for robust tracking. To the best knowledge of the authors, this is the first time
Particle Filtering Applied to Musical Tempo Tracking
2004-11-07
EURASIP Journal on Applied Signal Processing 2004:15, 2385–2395 c © 2004 Hindawi Publishing Corporation Particle Filtering Applied toMusical Tempo Tracking StephenW. Hainsworth Department of Engineering, University of Cambridge, Cambridge CB2 1PZ... . Autocorrelative meth- ods have also been tried and Tzanetakis [3] or Foote [9] are 1A comprehensive literature review can be found in Seppa¨nen [5] or Hainsworth [6]. examples, though these tend to only find the average tempo and not the phase (as defined...
Customized optimal filter for eliminating operator's tremor
NASA Astrophysics Data System (ADS)
Gonzalez, Juan G.; Heredia, Edwin A.; Rahman, Tariq; Barner, Kenneth E.; Arce, Gonzalo R.
1995-12-01
Remote manually operated tasks such as those found in teleoperation, virtual reality, or joystick-based computer access, require the generation of an intermediate signal which is transmitted to the controlled subsystem (robot arm, virtual environment or cursor). When man-machine movements are distorted by tremor, performance can be improved by digitally filtering the intermediate signal before it reaches the controlled device. This paper introduces a novel filtering framework in which digital equalizers are optimally designed after pursuit tracking task experiments. Due to inherent properties of the man-machine system, the design of tremor suppression equalizers presents two serious problems: (1) performance criteria leading to optimizations that minimize mean-squared error are not efficient for tremor elimination, and (2) movement signals show highly ill-conditioned autocorrelation matrices, which often result in useless or unstable solutions. A new performance indicator is introduced, namely the F-MSEd, and the optimal equalizer according to this new criterion is developed. Ill-condition of the autocorrelation matrix is overcome using a novel method which we call pulled-optimization. Experiments performed with both a person with tremor disability, and a vibration inducing device, show significant results.
Program Computes SLM Inputs To Implement Optimal Filters
NASA Technical Reports Server (NTRS)
Barton, R. Shane; Juday, Richard D.; Alvarez, Jennifer L.
1995-01-01
Minimum Euclidean Distance Optimal Filter (MEDOF) program generates filters for use in optical correlators. Analytically optimizes filters on arbitrary spatial light modulators (SLMs) of such types as coupled, binary, fully complex, and fractional-2pi-phase. Written in C language.
Envelope-constrained H? filter design: An LMI optimization approach
Zhiqiang Tan; Yeng Chai Soh; Lihua Xie
2000-01-01
In this correspondence, we solve the envelope-constrained H? filter design problem by minimizing the H? norm of the filtering error transfer function subject to the constraint that the filter output is contained in a prescribed envelope. The filter design is transformed into a standard linear matrix inequality (LMI) optimization problem
PCA-based adaptive particle filter for tracking
Guanglin Yuan; Mogen Xue; Pucheng Zhou; Kai Xie
2010-01-01
The particle filter is a popular tool for visual tracking. Traditionally, the number of particles used is typically fixed, and the motion model is simply a random walk with fixed noise variance. All these factors make the visual tracker unstable. To stabilize the tracker and guarantee the real-time tracking, an adaptive particle filter algorithm which estimates the motion model parameters
Switching Particle Filters for Efficient Real-time Visual Tracking
Takashi Bando; Tomohiro Shibata; Kenji Doya; Shin Ishii
2004-01-01
Particle filtering is an approach to Bayesian estimation of intractable posterior distributions from time series sig- nals distributed by non-Gaussian noise. A couple of variant particle filters have been proposed to approximate Bayesian computation withfinite particles. However, the performance of such algorithms has not been fully evaluated under cir- cumstances specific to real-time vision systems. In this article, we focus
Improved Object Tracking with Particle Filter and Mean Shift
Ke jia Bai; Weiming Liu
2007-01-01
In this paper, we present a new object tracking algorithm based on particle filtering technique and the mean shift algorithm. The particle filtering technique is a powerful technique for tracking objects in image sequences with complex background. It has been proved to be a robust method of tracking in non-linear and non-Gaussian case. But two common problems of the particle
NASA Astrophysics Data System (ADS)
Hirpa, F. A.; Gebremichael, M.; LEE, H.; Hopson, T. M.
2012-12-01
Hydrologic data assimilation techniques provide a means to improve river discharge forecasts through updating hydrologic model states and correcting the atmospheric forcing data via optimally combining model outputs with observations. The performance of the assimilation procedure, however, depends on the data assimilation techniques used and the amount of uncertainty in the data sets. To investigate the effects of these, we comparatively evaluate three data assimilation techniques, including ensemble Kalman filter (EnKF), particle filter (PF) and variational (VAR) technique, which assimilate discharge and synthetic soil moisture data at various uncertainty levels into the Sacramento Soil Moisture accounting (SAC-SMA) model used by the National Weather Service (NWS) for river forecasting in The United States. The study basin is Greens Bayou watershed with area of 178 km2 in eastern Texas. In the presentation, we summarize the results of the comparisons, and discuss the challenges of applying each technique for hydrologic applications.
Simulations of acoustic tomography using a particle filter
NASA Astrophysics Data System (ADS)
Zhang, Ming; Xu, Wen; Li, Jianlong
2012-11-01
This paper uses the state-space model to track the sound speed profile between a moving source suspended from a ship and a fixed vertical linear array. The particle filtering approach is presented to handle this nonlinear and non-Gaussian inverse problem. Simulation results show that the particle filter with a high particle number outperforms the extended Kalman filter; however, the performance degrades when the dimension of the state increases.
Algorithmic and Architectural Design Methodology for Particle Filters in Hardware
Aswin C. Sankaranarayanan; Rama Chellappa; Ankur Srivastava
2005-01-01
In this paper we present algorithmic and architectural methodology for building Particle Filters in hardware. Particle filtering is a new paradigm for fil- tering in presence of non-Gaussian non-linear state evolution and observation models. This technique has found wide-spread application in tracking, navi- gation, detection problems especially in a sensing environment. So far most particle filtering implementations are not lucrative
Object Tracking Using Genetic Evolution Based Kernel Particle Filter
Qicong Wang; Jilin Liu; Zhigang Wu
2006-01-01
A new particle filter, which combines genetic evolution and kernel density estimation, is proposed for moving object tracking.\\u000a Particle filter (PF) solves non-linear and non-Gaussian state estimation problems in Monte Carlo simulation using importance\\u000a sampling. Kernel particle filter (KPF) improves the performance of PF by using density estimation of broader kernel. However,\\u000a it has the problem which is similar to
Particle swarm optimization in electromagnetics
Jacob Robinson; Yahya Rahmat-Samii
2004-01-01
The particle swarm optimization (PSO), new to the electromagnetics community, is a robust stochastic evolutionary computation technique based on the movement and intelligence of swarms. This paper introduces a conceptual overview and detailed explanation of the PSO algorithm, as well as how it can be used for electromagnetic optimizations. This paper also presents several results illustrating the swarm behavior in
Nonuniform principal component filter banks: definitions, existence, and optimality
Sony J. Akkarakaran; Palghat P. Vaidyanathan
2000-01-01
The optimality of principal component filter banks (PCFBs) for data compression has been observed in many works to varying extents. Recent work by the authors has made explicit the precise connection between the optimality of uniform orthonormal filter banks (FBs) and the principal component property: The PCFB is optimal whenever the minimization objective is a concave function of the subband
SubOptimal Kalman Filter for Multimodeling Systems
Hiroaki Mukaidani; Yasuhiro Kawata; Yoshiyuki Tanaka; Hua Xu
2003-01-01
In this paper, we study the optimal Kalman filtering problem for multiparameter singularly perturbed sys- tem (MSPS). The attention is focused on the design of the high-order approximate Kalman filters. It is shown that the resulting filters in fact remove ill-conditioning of the original full-order singularly perturbed Kalman filters. In addition the resulting filters can be used compared with the
The Design of Optimal Convolutional Filters via Linear Programming
RALPH K. CAVIN; C. H. Ray; V. THOMAS RHYNE
1969-01-01
Computational algorithms are given for the design of optimal, finite-length, convolutional filters with finite-length input sequences. Design techniques are developed for minimum-weighted-mean-square-error filters (MWMSE), for minimum-weighted-absolute-error filters (MWAE), and for filters which minimize the maximum output error (minimax). It is shown that the coefficients of the MWAE and minimax filters can be obtained by using standard linear programming methods. Next,
Groupwise surface correspondence using particle filtering
NASA Astrophysics Data System (ADS)
Li, Guangxu; Kim, Hyoungseop; Tan, Joo Kooi; Ishikawa, Seiji
2015-03-01
To obtain an effective interpretation of organic shape using statistical shape models (SSMs), the correspondence of the landmarks through all the training samples is the most challenging part in model building. In this study, a coarse-tofine groupwise correspondence method for 3-D polygonal surfaces is proposed. We manipulate a reference model in advance. Then all the training samples are mapped to a unified spherical parameter space. According to the positions of landmarks of the reference model, the candidate regions for correspondence are chosen. Finally we refine the perceptually correct correspondences between landmarks using particle filter algorithm, where the likelihood of local surface features are introduced as the criterion. The proposed method was performed on the correspondence of 9 cases of left lung training samples. Experimental results show the proposed method is flexible and under-constrained.
Image Classification using Chaotic Particle Swarm Optimization
Krishna Chandramouli; Ebroul Izquierdo
2006-01-01
Particle Swarm Optimization is one of several meta- heuristic algorithms inspired by biological systems. The chaotic modeling of particle swarm optimization is presented in this paper with application to image classification. The performance of this modified particle swarm optimization algorithm is compared with standard particle swarm optimization. Numerical results of this comparative study are performed on binary classes of images
Visual Tracking Using Particle Filters with Gaussian Process Regression
Hongwei Li; Yi Wu; Hanqing Lu
2009-01-01
\\u000a Particle degeneracy is one of the main problems when particle filters are applied to visual tracking. The effective solution\\u000a methods on the degeneracy phenomenon include good choice of proposal distribution and use of resampling. In this paper, we\\u000a propose a novel visual-tracking algorithm using particle filters with Gaussian process regression and resampling techniques,\\u000a which effectively abate the influence of particle
Compound Particle Swarm Optimization in Dynamic Environments
Yang, Shengxiang
applications of evolutionary algorithms. In this paper, a compound particle swarm optimization (CPSOCompound Particle Swarm Optimization in Dynamic Environments Lili Liu1 , Dingwei Wang1 been an increasing concern on investigating evolu- tionary algorithms (EAs) for dynamic optimization
Bayesian auxiliary particle filters for estimating neural tuning parameters.
Mountney, John; Sobel, Marc; Obeid, Iyad
2009-01-01
A common challenge in neural engineering is to track the dynamic parameters of neural tuning functions. This work introduces the application of Bayesian auxiliary particle filters for this purpose. Based on Monte-Carlo filtering, Bayesian auxiliary particle filters use adaptive methods to model the prior densities of the state parameters being tracked. The observations used are the neural firing times, modeled here as a Poisson process, and the biological driving signal. The Bayesian auxiliary particle filter was evaluated by simultaneously tracking the three parameters of a hippocampal place cell and compared to a stochastic state point process filter. It is shown that Bayesian auxiliary particle filters are substantially more accurate and robust than alternative methods of state parameter estimation. The effects of time-averaging on parameter estimation are also evaluated. PMID:19963911
Occlusion Handling Based on Particle Filter in Surveillance System
Xinting Pan; Xiaobo Chen; Aidong Men
2010-01-01
Object tracking with occlusion handling is a challenging problem in intelligent video surveillance system. Among various tracking algorithms, particle filter (PF) is a robust and accurate one for different applications. In this paper, a new approach based on particle filter is presented for tracking object accurately and steadily when the target encountering occlusion in video sequences. First, the object pixels
Particle filtering for partially observed Gaussian state space models
Christophe Andrieu
2000-01-01
Summary. Solving Bayesian estimation problems where the posterior distribution evolves over time through the accumulation of data has many applications for dynamic models. A large num- ber of algorithms based on particle filtering methods, also known as sequential Monte Carlo algorithms, have recently been proposed to solve these problems.We propose a special particle filtering method which uses random mixtures of
Particle filtering for partially observed Gaussian state space models
Christophe Andrieu; Arnaud Doucet
2002-01-01
Solving Bayesian estimation problems where the posterior distribution evolves over time through the accumulation of data has many applications for dynamic models. A large number of algorithms based on particle filtering methods, also known as sequential Monte Carlo algorithms, have recently been proposed to solve these problems. We propose a special particle filtering method which uses random mixtures of normal
Particle Filters for Very High-Dimensional Systems
NASA Astrophysics Data System (ADS)
van Leeuwen, P. J.
2014-12-01
Nonlinear data assimilation for high-dimensional geophysical systems is a rapidly evolving field. Particle filters seem to be the most promising methods as they do not require long chains of model runs to start sampling the posterior probability density function (pdf). Up to very recently developments in particle filtering has been hampered by the 'curse of dimensionality', roughly meaning that the number of particles needed to avoid weight collapse growths exponentially with the dimension of the system. However it has been realised that for particle filtering it is not the dimension of the state vector but the number of independent observations that is the problem. Furthermore, proposal densities that ensure better positioning of the particles in state space before observations are encountered lead to much better performance. Recently particle filters have been proposed that do not suffer from weight collapse by construction. In this talk I will present several of these new filters, including the equivalent-weights particle filters, new combinations with the implicit particle filter, and filters using large-deviation theory. I will present basic ideas and applications to very high-dimensional systems, including a full climate model. Emphasis will be on the fruitful forward directions and on areas that still need attention, as we haven't solved the problem yet.
Real time face tracking using particle filtering and mean shift
Fang Xu; Jun Cheng; Chao Wang
2008-01-01
Particle filter is widely used in object tracking. However, it has one notable weaknesses that is sample degeneracy problem. This paper proposes a novel algorithm to overcome this problem by incorporating mean shift into particle filtering. Mean shift reacting on sample herds the samples in the reference mode area, which could make less samples be used while tracking. The proposed
Real Time Moving Object Tracking by Particle Filter
M. Z. Islam; Chi-min Oh; Chil-Woo Lee
2008-01-01
Robust and real time moving object tracking is a tricky job in computer vision problems. Particle filtering has been proven very successful for non-Gaussian and non-linear estimation problems. In this paper, we first try to develop a color based particle filter. In this approach, the object tracking system relies on the deterministic search of window, whose color content matches a
New Models For Real-Time Tracking Using Particle Filtering
Ng Ka Ki; Edward J. Delp
2009-01-01
This paper presents new methods for efficient object tracking in video sequences using multiple f eatures and particle filtering. A histogram-based framework is used to describe the features. Histograms are useful because have the property that they allow changes in the object appearance while the histograms remain the same. Particle filtering is used because it is very robust for non-linear
An Improved Color-Based Particle Filter for Object Tracking
Yuan Chen; Shengsheng Yu; Jun Fan; Wenxin Chen; Hongxing Li
2008-01-01
The object tracking problem in a nonlinear and\\/or non-Gaussian circumstance can be solved by particle filter estimation based on the concept of sequential importance sampling and the use of Bayesian theory. An improved object tracking scheme is proposed, which is based on the Markov chain Monte Carlo (MCMC) particle filter and object color distribution. This scheme is robust to clutter,
A Novel Particle Filter Method for Mobile Robot Localization
Bo Yin; Zhiqiang Wei; Yanping Cong; Tao Xu
2010-01-01
Particle filter is a powerful tool for mobile robot localization based on Sequential Monte Carlo framework. However, it needs a large number of samples to properly approximate the posterior density of the state evolution, which makes it computational expensive. In this paper, an improved particle filter is proposed by adopting an EKF proposal distribution and Support Vector Regression (SVR). The
Modified unscented particle filter using variance reduction factor
E. Baser; I. Bilik
2010-01-01
Sequential Monte Carlo based estimators, also known as particle filters (PF), have been widely used in nonlinear and non-Gaussian estimation problems. However, efficient distribution of the limited number of random samples remains a critical issue in design of the sequential Monte Carlo based estimation algorithms. In this work, we derive a modified unscented particle filter based on variance reduction factor
Multiple Objects Tracking Circuit using Particle Filters with Multiple Features
Jung Uk Cho; Seung Hun Jin; Dai Pham Xuan; Jae Wook Jeon
2007-01-01
Object tracking is a challenging problem in a number of computer vision applications. A number of approaches have been proposed and implemented to track moving objects in image sequences. The particle filter, which recursively constructs the posterior probability distributions of the state space, is the most popular approach. In the particle filter, many kinds of features are used for tracking
Maneuvering target tracking using cost reference particle filtering
M. F. Bugallo; Shanshan Xu; J. Miguez; P. M. Djuric
2004-01-01
Target tracking is a highly nonlinear problem that has been successfully addressed in recent years using sequential Monte Carlo (SMC) methods, usually called particle filters. We investigate the application of a new class of SMC techniques, termed cost reference particle filters (CRPFs), to the tracking of a high-speed maneuvering target. The new CRPF methodology drops all probabilistic assumptions (i.e., prior
Fast Multiple Object Tracking via a Hierarchical Particle Filter
Changjiang Yang; Ramani Duraiswami; Larry S. Davis
2005-01-01
A very efficient and robust visual object tracking algo- rithm based on the particle filter is presented. The method characterizes the tracked objects using color and edge ori- entation histogram features. While the use of more features and samples can improve the robustness, the computational load required by the particle filter increases. To acceler- ate the algorithm while retaining robustness
Vehicle Counting and Trajectory Detection Based on Particle Filtering
Alessandro Bof de Oliveira; Jacob Scharcanski
2010-01-01
This paper proposes a new road traffic monitoring method based on image processing and particle filtering. The proposed method detects and classifies automatically moving vehicles in previously defined classes. The detected vehicles are tracked using a new particle filtering algorithm to determine their positions on the road at each time, and then the vehicle positions are used to estimate its
Tracking Object by Combining Particle Filters and SIFT Features
Bin Feng; Bing Zeng; Jinbo Qiu
2009-01-01
An object tracking algorithm based on the particle filter framework is proposed in this paper for video surveillance applications. The color histogram is combined with a scale invariant feature transform (SIFT) descriptor to represent the likelihood between the candidates and observed objects. They are then incorporated into the particle filter based tracking algorithm in order to achieve more robust and
Recent Developments in Auxiliary Particle Filtering Nick Whiteley1
Johansen, Adam
Chapter 3 Recent Developments in Auxiliary Particle Filtering Nick Whiteley1 and Adam M. Johansen2 on an algorithm known as the auxiliary particle filter (APF). The APF has seen widespread use in several 1 of the state space. The performance of the method is demonstrated in the context of a switching stochastic
Particle Swarm Optimization - A Survey
NASA Astrophysics Data System (ADS)
Kameyama, Keisuke
Particle Swarm Optimization (PSO) is a search method which utilizes a set of agents that move through the search space to find the global minimum of an objective function. The trajectory of each particle is determined by a simple rule incorporating the current particle velocity and exploration histories of the particle and its neighbors. Since its introduction by Kennedy and Eberhart in 1995, PSO has attracted many researchers due to its search efficiency even for a high dimensional objective function with multiple local optima. The dynamics of PSO search has been investigated and numerous variants for improvements have been proposed. This paper reviews the progress of PSO research so far, and the recent achievements for application to large-scale optimization problems.
Synthesizing Optimal Filters for Crosstalkcancellation for HighSpeed Buses
Greenstreet, Mark
Synthesizing Optimal Filters for Crosstalkcancellation for HighSpeed Buses Jihong Ren and Mark in crosstalk cancellation for highbandwidth, digital commu nication. In practice, filter design the structure of a typical channel with a preequalization filter for crosstalk cancellation
Application of particle filtering algorithm in image reconstruction of EMT
NASA Astrophysics Data System (ADS)
Wang, Jingwen; Wang, Xu
2015-07-01
To improve the image quality of electromagnetic tomography (EMT), a new image reconstruction method of EMT based on a particle filtering algorithm is presented. Firstly, the principle of image reconstruction of EMT is analyzed. Then the search process for the optimal solution for image reconstruction of EMT is described as a system state estimation process, and the state space model is established. Secondly, to obtain the minimum variance estimation of image reconstruction, the optimal weights of random samples obtained from the state space are calculated from the measured information. Finally, simulation experiments with five different flow regimes are performed. The experimental results have shown that the average image error of reconstruction results obtained by the method mentioned in this paper is 42.61%, and the average correlation coefficient with the original image is 0.8706, which are much better than corresponding indicators obtained by LBP, Landweber and Kalman Filter algorithms. So, this EMT image reconstruction method has high efficiency and accuracy, and provides a new method and means for EMT research.
Optimal filters for detecting cosmic bubble collisions
NASA Astrophysics Data System (ADS)
McEwen, J. D.; Feeney, S. M.; Johnson, M. C.; Peiris, H. V.
2012-05-01
A number of well-motivated extensions of the ?CDM concordance cosmological model postulate the existence of a population of sources embedded in the cosmic microwave background. One such example is the signature of cosmic bubble collisions which arise in models of eternal inflation. The most unambiguous way to test these scenarios is to evaluate the full posterior probability distribution of the global parameters defining the theory; however, a direct evaluation is computationally impractical on large datasets, such as those obtained by the Wilkinson Microwave Anisotropy Probe (WMAP) and Planck. A method to approximate the full posterior has been developed recently, which requires as an input a set of candidate sources which are most likely to give the largest contribution to the likelihood. In this article, we present an improved algorithm for detecting candidate sources using optimal filters, and apply it to detect candidate bubble collision signatures in WMAP 7-year observations. We show both theoretically and through simulations that this algorithm provides an enhancement in sensitivity over previous methods by a factor of approximately two. Moreover, no other filter-based approach can provide a superior enhancement of these signatures. Applying our algorithm to WMAP 7-year observations, we detect eight new candidate bubble collision signatures for follow-up analysis.
Particle Swarms for Dynamic Optimization Problems
Li, Xiaodong
Particle Swarms for Dynamic Optimization Problems Tim Blackwell1 , J¨urgen Branke2 , and Xiaodong. An optimization algorithm, therefore, has to both find and subsequently track the changing optimum. Examples of particle swarm optimization. Particle swarm optimization (PSO) is a versatile population-based opti
A modified particle swarm optimization algorithm
Junjun Li; Xihuai Wang
2004-01-01
A modified particle swarm optimization (PSO) algorithms is proposed. This method integrates the particle swarm optimization with the simulated annealing algorithm. It can solve the problem of local minimum of the particle swarm optimization, and narrow the field of search continually, so it has higher efficiency of search. This algorithm is applied to the function optimization problem and simulation shows
Human-Manipulator Interface Using Particle Filter
Wang, Xueqian
2014-01-01
This paper utilizes a human-robot interface system which incorporates particle filter (PF) and adaptive multispace transformation (AMT) to track the pose of the human hand for controlling the robot manipulator. This system employs a 3D camera (Kinect) to determine the orientation and the translation of the human hand. We use Camshift algorithm to track the hand. PF is used to estimate the translation of the human hand. Although a PF is used for estimating the translation, the translation error increases in a short period of time when the sensors fail to detect the hand motion. Therefore, a methodology to correct the translation error is required. What is more, to be subject to the perceptive limitations and the motor limitations, human operator is hard to carry out the high precision operation. This paper proposes an adaptive multispace transformation (AMT) method to assist the operator to improve the accuracy and reliability in determining the pose of the robot. The human-robot interface system was experimentally tested in a lab environment, and the results indicate that such a system can successfully control a robot manipulator. PMID:24757430
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
Modular particle filtering FPGA hardware architecture for brain machine interfaces.
Mountney, John; Obeid, Iyad; Silage, Dennis
2011-01-01
As the computational complexities of neural decoding algorithms for brain machine interfaces (BMI) increase, their implementation through sequential processors becomes prohibitive for real-time applications. This work presents the field programmable gate array (FPGA) as an alternative to sequential processors for BMIs. The reprogrammable hardware architecture of the FPGA provides a near optimal platform for performing parallel computations in real-time. The scalability and reconfigurability of the FPGA accommodates diverse sets of neural ensembles and a variety of decoding algorithms. Throughput is significantly increased by decomposing computations into independent parallel hardware modules on the FPGA. This increase in throughput is demonstrated through a parallel hardware implementation of the auxiliary particle filtering signal processing algorithm. PMID:22255366
Some issues and results on the EnKF and particle filters for meteorological models
Baehr, Christophe
Some issues and results on the EnKF and particle filters for meteorological models Chaos 2009KF and particle filters for meteorological models #12;The nonlinear filtering problem Particle Filter resolution C. Baehr & O. Pannekoucke EnKF and particle filters for meteorological models #12;2 / 26 Nonlinear
Simplex-based particles swarm optimizer
Hongfeng Xiao; Guanzheng Tan
2009-01-01
The velocity term of Particle Swarm Optimizer (PSO) easily results in particles' dispersing and thus decreasing in computation precision. In view of PSO, the simplex-based Particle Swarm Optimizer (Simplex PSO) is derived from the Nelder-Mead simplex method. In Simplex PSO, the velocity term is abandoned and its reference objectives are the centroid of particles and the best particle. The centroid
Vehicle tracking using stochastic fusion-based particle filter
Huaping Liu; Fuchun Sun; Liping Yu; Kezhong He
2007-01-01
In this article, we propose a new observation model combination approach under particle filtering scheme, which allows robust and accurate visual tracking under typical circumstances of real-time visual tracking. This scheme stochastically selects single observation model to evaluate the likelihood of some particle. Since only one single observation likelihood is evaluated for any one particle, the time-cost can be reduced
Particle Filtering for Large Dimensional State Spaces with Multimodal Observation Likelihoods
Namrata Vaswani
2008-01-01
Abstract—We study efficient importance sampling techniques for particle filtering (PF) when either (a) the observation likelihood (OL) is frequently multimodal or heavy-tailed, or (b) the state space dimension is large or both. When the OL is multimodal, but the state transition pdf (STP) is narrow enough, the optimal importance density is usually unimodal. Under this assumption, many techniques have been
A new hybrid Bayesian-variational particle filter with application to mitotic cell tracking
Ricard Delgado-Gonzalo; Nicolas Chenouard; Michael Unser
2011-01-01
Tracking algorithms are traditionally based on either a variational approach or a Bayesian one. In the variational case, a cost function is established between two consecutive frames and minimized by standard optimization algorithms. In the Bayesian case, a stochastic motion model is used to maintain temporal consistency. Among the Bayesian methods we focus on the particle filter, which is especially
A novel particle filtering approach and its application to target tracking
J. Miguez; Shanshan Xu; M. F. Bugallo; P. M. Djuric
2004-01-01
Particle filters provide asymptotically optimal numerical solutions in problems that can be cast as estimation of unobserved time-varying states of dynamic systems. Such methods rely on knowledge of the prior probability distributions of the initial state and the noise processes that affect the analyzed system, and require ability to evaluate the likelihood function and the state transition density. We describe
Forward-looking infrared 3D target tracking via combination of particle filter and SIFT
NASA Astrophysics Data System (ADS)
Li, Xing; Cao, Zhiguo; Yan, Ruicheng; Li, Tuo
2013-10-01
Aiming at the problem of tracking 3D target in forward-looking infrared (FLIR) image, this paper proposes a high-accuracy robust tracking algorithm based on SIFT and particle filter. The main contribution of this paper is the proposal of a new method of estimating the affine transformation matrix parameters based on Monte Carlo methods of particle filter. At first, we extract SIFT features on infrared image, and calculate the initial affine transformation matrix with optimal candidate key points. Then we take affine transformation parameters as particles, and use SIR (Sequential Importance Resampling) particle filter to estimate the best position, thus implementing our algorithm. The experiments demonstrate that our algorithm proves to be robust with high accuracy.
Nonmonotone Filter Method for Nonlinear Optimization
2009-10-14
Oct 14, 2009 ... The sequential quadratic programming (SQP) method is an iterative method for ... Gould and Toint (2003) introduced a nonmonotone trust-region filter al- ...... g- and l-filter with M = 2, though we have also experimented with.
Adapting Particle Filter Algorithms to Many-Core Architectures
Kuzmanov, Georgi
in the system model and in the measurement model. For systems where the amount of non-linearity is limited on Monte Carlo simulation. It is ideal for non-linear, non- Gaussian dynamical systems with applications. By varying filtering and model parameters, we evaluate our particle filter extensively and derive rules
Particle filter theory and practice with positioning applications
Fredrik Gustafsson
2010-01-01
The particle filter (PF) was introduced in 1993 as a numerical approximation to the nonlinear Bayesian filtering problem, and there is today a rather mature theory as well as a number of successful applications described in literature. This tutorial serves two purposes: to survey the part of the theory that is most important for applications and to survey a number
Application of the implicit particle filter to a model of nearshore circulation
NASA Astrophysics Data System (ADS)
Miller, R. N.; Ehret, L. L.
2014-04-01
The implicit particle filter is applied to a stochastically forced shallow water model of nearshore flow, and found to produce reliable state estimates with tens of particles. The state vector of this model consists of a height anomaly and two horizontal velocity components at each point on a 128 × 98 regular rectangular grid, making for a state dimension O(104). The particle filter was applied to the model with two parameter choices representing two distinct dynamical regimes, and performed well in both. Demands on computing resources were manageable. Simulations with as many as a hundred particles ran overnight on a modestly configured workstation. In this case of observations defined by a linear function of the state vector, taken every time step of the numerical model, the implicit particle filter is equivalent to the optimal importance filter, i.e., at each step any given particle is drawn from the density of the system conditioned jointly upon observations and the state of that particle at the previous time. Even in this ideal case, the sample occasionally collapses to a single particle, and resampling is necessary. In those cases, the sample rapidly reinflates, and the analysis never loses track. In both dynamical regimes, the ensembles of particles deviated significantly from normality.
Occlusion processing using particle filter and background subtraction algorithms
NASA Astrophysics Data System (ADS)
Guo, Tongqing; Rong, Jian; Lu, Kui; Zhong, Xiaochun
We present an algorithm based on the Particle Filter algorithmand Background Subtraction algorithm. Particle Filter can track target robustly under kinds of noise conditions, and it's robust to partial occlusion.However, it cannot recover from large proportion of occlusion and total occlusion.Background Subtraction algorithmcan detectnew target which emergeon a relatively stable background.The hybrid algorithm can recover fromlarge proportion of occlusion and total occlusion. A new occlusion measurement factor is imported to switchthe Particle Filter algorithm to Background subtraction algorithm when the target is occluded largely or totally, and switch Background subtraction algorithm to the Particle Filter algorithm when the target went out of the occlusion. The experimental results show that the hybrid algorithm was robust to partial and total occlusions.
Particle filtering with Lagrangian data in a point vortex model
Mitra, Subhadeep
2012-01-01
Particle filtering is a technique used for state estimation from noisy measurements. In fluid dynamics, a popular problem called Lagrangian data assimilation (LaDA) uses Lagrangian measurements in the form of tracer positions ...
An adaptive simple particle swarm optimization algorithm
Fan Chunxia; Wan Youhong
2008-01-01
The particle swarm optimization algorithm with constriction factor (CFPSO) has some demerits, such as relapsing into local extremum, slow convergence velocity and low convergence precision in the late evolutionary. An adaptive simple particle swarm optimization with constriction factor (AsCFPSO) is combined with chaotic optimization, then a new CFPSO is developed, i.e., a chaotic optimization-based adaptive simple particle swarm optimization equation
Multi-Objective Particles Swarm Optimization Approaches
Parsopoulos, Konstantinos
20 Chapter II Multi-Objective Particles Swarm Optimization Approaches Konstantinos E. Parsopoulos- rithmsthatcantacklesuchproblemseffectively,withthesmallestpossiblecomputationalburden.Particle Swarm Optimization has attracted the interest-objective optimization problems. Up-to-date, there are a significant number of multi-objective Particle Swarm
Maneuvering Target Tracking with Simplified Cost Reference Particle Filters
Shanshan Xu; M. F. Bugallo; P. M. Djuric
2006-01-01
In this paper, we investigate different variants of the recently proposed cost-reference particle filters (CRPFs) and study their application to the problem of tracking of a high-speed maneuvering target in the two-dimensional space. CRPFs drop all probabilistic assumptions required by conventional particle filters and, as a consequence, lead to practically more robust algorithms. We introduce some suitable and natural modifications
MANEUVERING TARGET TRACKING USING COST REFERENCE PARTICLE FILTERING M
F. Bugallo; Shanshan Xu; Joaqu ´ i; nM ´ iguez; Campus de Elvina
Target tracking is a highly nonlinear problem that has been successfully addressed in recent years using sequential Monte Carlo (SMC) methods, usually called particle filters. In this paper, we investigate the application of a new class of SMC techniques, termed cost-reference particle filters (CRPFs), to tracking of a high-speed maneuvering target. The new CRPF methodology drops all probabilistic assumptions (i.e.,
Design of passive filter circuit based on robust optimization
NASA Astrophysics Data System (ADS)
Zhao, Hong; Chen, Gang
2013-03-01
In view on this change of filter performance by the deviation of circuit component parameter values from its design values, the concept of robust optimization design for the passive filter circuit is presented. The function, that is to minimize the ripples and maximal variations of system performance is chosen as the objective function. The optimization strategy by combining random direction searching method with compound optimum was adopted for solving this nonlinear programming problem with two-level optimization. This theory is used on an 800MHz transmitter bandpass filter circuit. By comparing with original design and conventional optimization, passband performance of the robust optimized circuit is more flat and its fluctuation is more small when component parameters change within their rated tolerance. So filter performance of the circuit is improved, and the method mentioned in this paper is effective and superior.
Comparing a Kalman Filter and a Particle Filter in a Multiple Objects Tracking Application
M. Marron; J.C. Garcia; M. A. Sotelo; M. Cabello; D. Pizarro; F. Huerta; J. Cerro
2007-01-01
Two of the most important solutions in position estimation are compared, in this paper, in order to test their efficiency in a multi-tracking application in an unstructured and complex environment. A particle filter is extended and adapted with a clustering process in order to track a variable number of objects. The other approach is to use a Kalman filter with
A Modified Particle Swarm Optimizer Algorithm
Yang Guangyou
2007-01-01
This paper presented a modified particle swarm optimizer algorithm (MPSO). The aggregation degree of the particle swarm was introduced. The particles' diversity was improved through periodically monitoring aggregation degree of the particle swarm. On the later development of the PSO algorithm, it has been taken strategy of the Gaussian mutation to the best particle's position, which enhanced the particles' capacity
Optimization design of biorthogonal filter banks for image compression
Yi Shang; Longzhuang Li; Benjamin W. Wah
2001-01-01
In this paper, we present a new approach for designing filter banks for image compression. This approach has two major components: optimization and generalization. In the optimization phase, we formulate the design problem as a nonlinear optimization problem whose objective consists of both the performance metrics of the image coder, such as the peak signal-to-noise ratio (PSNR), and those of
OPTIMIZATION OF AUTOMOTIVE VALVE TRAIN COMPONENTS WITH IMPLICT FILTERING \\Lambda
in this application. In x 3 we present the details of the valve train model and the optimization problems to be solvedOPTIMIZATION OF AUTOMOTIVE VALVE TRAIN COMPONENTS WITH IMPLICT FILTERING \\Lambda T. D. CHOI y , O identification and optimization in automotive valve train design. We extend our previous work by using a more
Clubs-based Particle Swarm Optimization
Wesam Elshamy; Hassan M. Emara; A. Bahgat
2007-01-01
This paper introduces a new dynamic neighborhood network for particle swarm optimization. In the proposed clubs-based particle swarm optimization (C-PSO) algorithm, each particle initially joins a default number of what we call 'clubs'. Each particle is affected by its own experience and the experience of the best performing member of the clubs it is a member of. Clubs membership is
FILTER-BANK OPTIMIZATION WITH CONVEX OBJECTIVES, AND THE OPTIMALITY OF PRINCIPAL COMPONENT FORMS1
Sony Akkarakaran; P. P. Vaidyanathan
This paper proposes a general framework for the optimization of orthonormal filter banks (FB's) for given input statistics. This includes as special cases, many recent results on filter bank optimization for compression. It also solves problems that have not been considered thus far. FB optimization for coding gain maximization (for compression applications) has been well studied before. The optimum FB
Silicon oxide nano-particles doped PQ-PMMA for volume holographic imaging filters
Luo, Yuan; Russo, Juan M.; Kostuk, Raymond K.; Barbastathis, George
2011-01-01
Holographic imaging filters are required to have high Bragg selectivity to obtain spatial-spectral information within a three-dimensional object. In this Letter, we present the design of holographic imaging filters formed using silicon oxide nano-particles (nano-SiO2) in PQ-PMMA polymer recording material. This combination offers greater angular and spectral selectivity and increases the diffraction efficiency of holographic filters. The holographic filters with optimized ratio of nano-SiO2 in PQ-PMMA can significantly improve the performance of Bragg selectivity and diffraction efficiency by 53% and 16%, respectively. We present experimental results and data analysis demonstrating this technique in use for holographic spatial-spectral imaging filters. PMID:20410989
Contrasting Particle Clogging in Soils and Granular Media Filters
NASA Astrophysics Data System (ADS)
Mays, D. C.
2005-12-01
Deposition of colloidal particles leads to permeability reduction (or clogging) in the soil geomembrane, which reduces fluxes, alters flow patterns, and limits both colloid-associated contaminant transport and delivery of colloidal reactants for purposes of remediation. Comparison of experimental results for soils and granular media filters reveals qualitatively different clogging phenomena with regard to (1) particle stabilization, (2) fluid velocity, and (3) the fractal dimension of particle deposits. These differences have important implications for contaminant hydrology, because the classical approach for understanding particles in natural environments is taken from the filtration literature, which is based on clean granular media. Accordingly, many of the relevant experiments have been performed with granular filters using media such as glass beads or quartz sand. In such filters, clogging is associated with destabilized particles, slower fluid velocity and deposits with smaller fractal dimensions. In contrast, in soils clogging is associated with stabilized particles, faster fluid velocity and deposits with larger fractal dimensions. With regard to these variables, soils are opposite to filters but identical to cake filtration. Numerous examples will be presented from the filtration literature and the soil science literature to illustrate these differing viewpoints. This analysis demonstrates that experiments on clean granular media filters should not be expected to predict particle clogging in soils, sandstones or other natural porous materials containing more than a few percent fines.
Contour extracting with combination particle filtering and EM algorithm
NASA Astrophysics Data System (ADS)
Meng, Bo; Zhu, Ming
2008-03-01
The problem of extracting continuous structures from images is a difficult issue in early pattern recognition and image processing. Tracking with contours in a filtering framework requires a dynamical model for prediction. Recently, Particle filter, is widely used because its multiple hypotheses and versatility within framework. However, the good choice of the propagation function is still its main problem. In this paper, an improved particle filter, EM-PF algorithm is proposed which using the EM (Expectation-Maximization) algorithm to learn the dynamical models. The EM algorithm can explicitly learn the parameters of the dynamical models from training sequences. The advantage of using the EM algorithm in particle filter is that it is capable of improve tracking contour by having accurate model parameters. Though the experiment results, we show how our EM-PF can be applied to produces more robust and accurate extracting.
Pump and filtering optimization in Mamyshev regenerator
NASA Astrophysics Data System (ADS)
Supe, Andis; Fernandes, Gil M.; Muga, Nelson J.; Pinto, Armando N.; Ferreira, Mario F. S.
2013-11-01
In this paper we present results from the study of optical signal regeneration using Mamyshev type regenerator. We have performed the simulations and experimental characterization of regenerator by obtaining it`s transfer function and output optical signal to noise ratio measurements for two different filters - fixed and a tunable optical filter. Investigated regenerator setup consists of a high power erbium doped fiber amplifier, highly nonlinear fiber and a single stage optical filtering. Signal used for regeneration was an on-off keying return to zero code 40 Gbps pulse sequence. To find out optimum filter pass-band shift from signal`s central wavelength the regenerator`s transfer function was measured. Results show that highest output signal to noise ratio improvement for the fixed filter is at 0.6nm shift and amplifier output power set to 63 mW. While the tunable filter shift is 0.7nm at the 100 mW power level.
COMPUTATIONS ON THE PERFORMANCE OF PARTICLE FILTERS AND ELECTRONIC AIR CLEANERS
The paper discusses computations on the performance of particle filters and electronic air cleaners (EACs). he collection efficiency of particle filters and ACs is calculable if certain factors can be assumed or calibrated. or fibrous particulate filters, measurement of collectio...
COMPUTATIONS ON THE PERFORMANCE OF PARTICLE FILTERS AND ELECTRONIC AIR CLEANERS
The paper discusses computations on the performance of particle filters and electronic air cleaners (EACs). The collection efficiency of particle filters and ACs is calculable if certain factors can be assumed or calibrated. For fibrous particulate filters, measurement of colle...
Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets
Robert Singer
1970-01-01
The majority of tactical weapons systems require that manned maneuverable vehicles, such as aircraft, ships, and submarines, be tracked accurately. An optimal Kalman filter has been derived for this purpose using a target model that is simple to implement and that represents closely the motions of maneuvering targets. Using this filter, parametric tracking accuracy data have been generated as a
Robustness of optimal binary filters: analysis and design
Grigoryan, Artyom M
1999-01-01
and these are governed by parameterized probability laws. The optimal filter is found relative to these laws. Qualitatively, a filter is said to be robust when its performance degradation is acceptable for processes statistically close to the one for which it has been...
A study of particle swarm optimization particle trajectories
F. Van Den Bergh; Andries Petrus Engelbrecht
2006-01-01
Particle swarm optimization (PSO) has shown to be an efficient, robust and simple optimization algorithm. Most of the PSO studies are empirical, with only a few theoret- ical analyses that concentrate on understanding particle trajectories. These theoretical studies concentrate mainly on simplified PSO systems. This paper overviews current the- oretical studies, and extend these studies to investigate particle trajectories for
Adaptive Multifeature Tracking in a Particle Filtering Framework
Emilio Maggio; Fabrizio Smerladi; Andrea Cavallaro
2007-01-01
In this paper, we propose a tracking algorithm based on an adaptive multifeature statistical target model. The features are combined in a single particle filter by weighting their contributions using a novel reliability measure derived from the particle distribution in the state space. This measure estimates the reliability of the information by measuring the spatial uncertainty of features. A modified
Robust body parts tracking using particle filter and dynamic template
Matilde Gonzalez; Christophe Collet
2011-01-01
This paper presents a novel body parts tracking algorithm using particle filters and a dynamic head template to handle occlusions. Hands and head tracking is a challenging problem because of the similarity of colour, the presence of noise and occlusions. Two different particle models are considered; a pixel and a rectangular region. Head is modelled as a rectangle and hands
Optimization of Synthesis Oversampled Complex Filter Banks
Paris-Sud XI, Université de
property on the synthesis side, which is serviceable to processing real-valued signals. As an invertible localization, frequency localiza- tion, lapped transforms, modulated filter banks. I. INTRODUCTION Since the 70s, filter banks (FBs) have become a central tool in signal/image processing and commu- nications
Interacting Particle Filtering With Discrete Observations
Del Moral , Pierre
in the nonlinear filtering problem (in short NLF). That is, we want to find the one step predictor conditional the two types of NLF problems covered by our work. . Case A: The state signal (X n ) n#IN is an E A crucial practical advantage of the first category of NLF problems is that it leads to a natural IPS
Nonlinear Filtering: Interacting Particle P. Del Moral
Del Moral , Pierre
assume that the observation noise V and the state plant X are independent. For simplicity. Introduction The basic model for the general nonlinear filtering problem consists of a non- linear plant X with state noise W and nonlinear observation Y with observation noise V . Let (X, Y ) be the Markov process
Particle swarm optimization: surfing the waves
Ender Ozcan; Chilukuri K. Mohan
1999-01-01
A new optimization method has been proposed by J. Kennedy and R.C. Eberhart (1997; 1995), called Particle Swarm Optimization (PSO). This approach combines social psychology principles and evolutionary computation. It has been applied successfully to nonlinear function optimization and neural network training. Preliminary formal analyses showed that a particle in a simple one-dimensional PSO system follows a path defined by
Square Root Unscented Particle Filtering for Grid Mapping
NASA Astrophysics Data System (ADS)
Zandara, Simone; Nicholson, Ann
In robotics, a key problem is for a robot to explore its environment and use the information gathered by its sensors to jointly produce a map of its environment, together with an estimate of its position: so-called SLAM (Simultaneous Localization and Mapping) [12]. Various filtering methods - Particle Filtering, and derived Kalman Filter methods (Extended, Unscented) - have been applied successfully to SLAM. We present a new algorithm that adapts the Square Root Unscented Transformation [13], previously only applied to feature based maps [5], to grid mapping. We also present a new method for the so-called pose-correction step in the algorithm. Experimental results show improved computational performance on more complex grid maps compared to an existing grid based particle filtering algorithm.
NASA Astrophysics Data System (ADS)
Sambaer, Wannes; Zatloukal, Martin; Kimmer, Dusan
2013-04-01
Realistic SEM image based 3D filter model considering transition/free molecular flow regime, Brownian diffusion, aerodynamic slip, particle-fiber and particle-particle interactions together with a novel Euclidian distance map based methodology for the pressure drop calculation has been utilized for a polyurethane nanofiber based filter prepared via electrospinning process in order to more deeply understand the effect of particle-fiber friction coefficient on filter clogging and basic filter characteristics. Based on the performed theoretical analysis, it has been revealed that the increase in the fiber-particle friction coefficient causes, firstly, more weaker particle penetration in the filter, creation of dense top layers and generation of higher pressure drop (surface filtration) in comparison with lower particle-fiber friction coefficient filter for which deeper particle penetration takes place (depth filtration), secondly, higher filtration efficiency, thirdly, higher quality factor and finally, higher quality factor sensitivity to the increased collected particle mass. Moreover, it has been revealed that even if the particle-fiber friction coefficient is different, the cake morphology is very similar.
Tracking of geoacoustic parameters using Kalman and particle filters.
Yardim, Caglar; Gerstoft, Peter; Hodgkiss, William S
2009-02-01
This paper incorporates tracking techniques such as the extended Kalman, unscented Kalman, and particle (PF) filters into geoacoustic inversion problems. This enables spatial and temporal tracking of environmental parameters and their underlying probability densities, making geoacoustic tracking a natural extension to geoacoustic inversion techniques. Water column and seabed properties are tracked in simulation for both vertical (VLA) and horizontal (HLA) line arrays using the three tracking filters. Filter performances are compared in terms of filter efficiencies using the posterior Cramer-Rao lower bound. Tracking capabilities of the geoacoustic filters under slowly and quickly changing environments are studied in terms of divergence statistics. Geoacoustic tracking can provide continuously environmental estimates and their uncertainties using only a fraction of the computational power of classical geoacoustic inversion schemes. Interfilter comparison show that while a high-particle-number PF outperforms the Kalman filters, there are many cases where all three filters perform equally well depending on the inversion configuration (such as the HLA versus VLA and frequency) and the tracked parameters. PMID:19206852
Estimate the Electromechanical States Using Particle Filtering and Smoothing
Meng, Da; Zhou, Ning; Lu, Shuai; Lin, Guang
2012-07-22
Accurate knowledge of electromechanical states is critical for efficient and reliable control of a power system. This paper proposes a particle filtering approach to estimate the electromechanical states of power systems from Phasor Measurement Unit (PMU) data. Without having to go through laborious linearization procedure, the proposed particle filtering techniques can estimate states of a complex power system, which is often non-linear and has non-Gaussian noise. The proposed method is evaluated using a multi-machine system with both large and small disturbances. Sensitivity studies of the dynamic state estimation performance are also presented to show the robustness of the proposed method. The inherent decoupling properties of particle filtering make it highly scalable and the potential to reduce computational time through parallel implementation is very promising.
Robust tracking algorithm using mean-shift and particle filter
NASA Astrophysics Data System (ADS)
Wang, Jianhua; Liang, Wei
2011-12-01
Aiming to the problems that Mean-Shift algorithm costs low computation, but easy to fall into local maximum, and huge computation of Particle Filter tracking algorithm leads to low real-time processing capacity, according to the need of real stereo vision measurement system, a kind of tracking algorithm which combines Mean-Shift and Particle Filter by essentiality function is proposed. Under the condition without occlusion, Mean-Shift is used to track object. When object is occluded, Particle Filter is applied to accomplish the later object tracking. These two algorithms alternate by a defined threshold. The tracking algorithm is used into real stereo vision measurement system. Experiment result indicates that the algorithm takes on high efficiency, so it is of high practicability.
An Improved Particle Filter for Service Robot Self-Localization
NASA Astrophysics Data System (ADS)
Cen, Guanghui; Matsuhira, Nobuto; Hirokawa, Junko; Ogawa, Hideki; Hagiwara, Ichiro
Mobile robot localization is a problem of determining a robot's pose in an environment, which is also one of the most basic problems in mobile robot applications. Recently, introduction of particle filters becomes the most popular approach in mobile robot localization and has been applied with great success to a variety of state estimation problems. In this paper, an particle filter is applied in the authors' service robot position tracking and global localization. Moreover, the posterior distribution of a robot pose in global localization is usually a multi-model due to the symmetry of the environment and ambiguous detected features. Considering these characteristics, we propose an improved cluster particle filter to increase the global localization robustness and accuracy. On-line experiments based detailed analysis of coordinate errors and algorithm efficiency are given. On-line experimental results also show the efficiency and robustness of the approach in the authors' service robot ApriAlpha™ Platform.
Contour Tracking in 2D Images Using Particle Filtering Donka Angelova, Pavlina Konstantinova
Mihaylova, Lyudmila
association filtering (PDAF) [2]. A robust particle filtering algorithm for contour following is developedContour Tracking in 2D Images Using Particle Filtering Donka Angelova, Pavlina Konstantinova 1 model Particle Filter (PF) for progressive contour growing (tracking) from a starting point is designed
Sequential Bearings-Only-Tracking Initiation with Particle Filtering Method
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. PMID:24453865
Principal component filter banks for optimal multiresolution analysis
Michail K. Tsatsanis; Georgios B. Giannakis
1995-01-01
An important issue in multiresolution analysis is that of optimal basis selection. An optimal P-band perfect reconstruction filter bank (PRFB) is derived in this paper, which minimizes the approximation error (in the mean-square sense) between the original signal and its low-resolution version. The resulting PRFB decomposes the input signal into uncorrelated, low-resolution principal components with decreasing variance. Optimality issues are
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.
Nonlinear Statistical Signal Processing: A Particle Filtering Approach
Candy, J
2007-09-19
A introduction to particle filtering is discussed starting with an overview of Bayesian inference from batch to sequential processors. Once the evolving Bayesian paradigm is established, simulation-based methods using sampling theory and Monte Carlo realizations are discussed. Here the usual limitations of nonlinear approximations and non-gaussian processes prevalent in classical nonlinear processing algorithms (e.g. Kalman filters) are no longer a restriction to perform Bayesian inference. It is shown how the underlying hidden or state variables are easily assimilated into this Bayesian construct. Importance sampling methods are then discussed and shown how they can be extended to sequential solutions implemented using Markovian state-space models as a natural evolution. With this in mind, the idea of a particle filter, which is a discrete representation of a probability distribution, is developed and shown how it can be implemented using sequential importance sampling/resampling methods. Finally, an application is briefly discussed comparing the performance of the particle filter designs with classical nonlinear filter implementations.
Robust Particle Filtering for Object Tracking
Daniel Rowe; Ignasi Rius; Jordi Gonzàlez; Juan José Villanueva
2005-01-01
This paper addresses the ltering problem when no assump- tion about linearity or gaussianity is made on the involved density func- tions. This approach, widely known as particle ltering , has been ex- plored by several previous algorithms, including Condensation. Although it represents a new paradigm and some results have been achieved, it has several unpleasant behaviours. We highlight these
Recovering Particle Diversity in a Rao-Blackwellized Particle Filter for SLAM
Burgard, Wolfram
-Blackwellized particle filter to maintain multiple hypotheses about potential trajectories of the robot and corresponding its own map. The individual maps are computed based on the trajectory of the corresponding particle further information about A. Since each particle represents a whole trajectory of the robot, also
Estimating the full posterior pdf with particle filters
NASA Astrophysics Data System (ADS)
Ades, Melanie; van Leeuwen, Peter Jan
2013-04-01
The majority of data assimilation schemes rely on linearity assumptions. However as the resolution and complexity of both the numerical models and observations increases, these linearity assumptions become less appropriate. A need is arising for fully non-linear data assimilation schemes, such as particle filters. Recently, new particle filter schemes have been generated that explore the freedom in proposal densities and that are quite effective in estimating the mean of the posterior probability density function (pdf), even in very high dimensional systems. However, in non-linear data assimilation the solution to the data assimilation problem is the full posterior pdf. At the same time we can only afford a limited number of particles. Here we concentrate on the equivalent weights particle filter in conjunction with a 65,000 dimensional Barotropic Vorticity model. Specifically we test the ability of the scheme to represent the posterior in three important areas. In many actual geophysical applications, observations will be sparse and may well be unevenly distributed. We discuss the effect of changing the frequency, number and distribution of the observed variables on the ensemble representation of the posterior pdf. Specifically we show that the filter has remarkably good convergence in marginal and joint pdfs with ensemble size, and the rank histograms are quite flat, even with low observation numbers and low observation frequencies. Only when the observation frequency is much larger than the typical decorrelation time scale of the system do we see underdispersive ensembles when using 32 particles. The second area attempts to replicate the realistic situation of using a geophysical model designed without a full understanding of the error statistics of the truth. This is done by using deliberately erroneous error statistics in the ensemble equations compared to those used to generate the truth. Specifically we consider changes in the correlation length-scales and variances in the model error statistics. Again the filter is remarkably successful in generating correct posterior pdfs, although rank histograms tend to point to under- or overdispersive ensembles. One of the interesting results is that when we overestimate the model error amplitude the ensemble is underdispersive. We present an explanation for this counter-intuitive phenomenon. Finally we show that the computational effort involved in the equivalent-weights particle filter is comparable to running a simple resampling particle filter with the same number of particles.
Design of optimal correlation filters for hybrid vision systems
NASA Technical Reports Server (NTRS)
Rajan, Periasamy K.
1990-01-01
Research is underway at the NASA Johnson Space Center on the development of vision systems that recognize objects and estimate their position by processing their images. This is a crucial task in many space applications such as autonomous landing on Mars sites, satellite inspection and repair, and docking of space shuttle and space station. Currently available algorithms and hardware are too slow to be suitable for these tasks. Electronic digital hardware exhibits superior performance in computing and control; however, they take too much time to carry out important signal processing operations such as Fourier transformation of image data and calculation of correlation between two images. Fortunately, because of the inherent parallelism, optical devices can carry out these operations very fast, although they are not quite suitable for computation and control type operations. Hence, investigations are currently being conducted on the development of hybrid vision systems that utilize both optical techniques and digital processing jointly to carry out the object recognition tasks in real time. Algorithms for the design of optimal filters for use in hybrid vision systems were developed. Specifically, an algorithm was developed for the design of real-valued frequency plane correlation filters. Furthermore, research was also conducted on designing correlation filters optimal in the sense of providing maximum signal-to-nose ratio when noise is present in the detectors in the correlation plane. Algorithms were developed for the design of different types of optimal filters: complex filters, real-value filters, phase-only filters, ternary-valued filters, coupled filters. This report presents some of these algorithms in detail along with their derivations.
Design of optimal correlation filters for hybrid vision systems
NASA Astrophysics Data System (ADS)
Rajan, Periasamy K.
1990-12-01
Research is underway at the NASA Johnson Space Center on the development of vision systems that recognize objects and estimate their position by processing their images. This is a crucial task in many space applications such as autonomous landing on Mars sites, satellite inspection and repair, and docking of space shuttle and space station. Currently available algorithms and hardware are too slow to be suitable for these tasks. Electronic digital hardware exhibits superior performance in computing and control; however, they take too much time to carry out important signal processing operations such as Fourier transformation of image data and calculation of correlation between two images. Fortunately, because of the inherent parallelism, optical devices can carry out these operations very fast, although they are not quite suitable for computation and control type operations. Hence, investigations are currently being conducted on the development of hybrid vision systems that utilize both optical techniques and digital processing jointly to carry out the object recognition tasks in real time. Algorithms for the design of optimal filters for use in hybrid vision systems were developed. Specifically, an algorithm was developed for the design of real-valued frequency plane correlation filters. Furthermore, research was also conducted on designing correlation filters optimal in the sense of providing maximum signal-to-nose ratio when noise is present in the detectors in the correlation plane. Algorithms were developed for the design of different types of optimal filters: complex filters, real-value filters, phase-only filters, ternary-valued filters, coupled filters. This report presents some of these algorithms in detail along with their derivations.
Design and performance optimization of fiber optic adaptive filters.
Paparao, P; Ghosh, A; Allen, S D
1991-05-10
There is a great need for easy-to-fabricate and versatile fiber optic signal processing systems in which optical fibers are used for the delay and storage of wideband guided lightwave signals. We describe the design of the least-mean-square algorithm-based fiber optic adaptive filters for processing guided lightwave signals in real time. Fiber optic adaptive filters can learn to change their parameters or to process a set of characteristics of the input signal. In our realization we employ as few electronic devices as possible and use optical computation to utilize the advantages of optics in the processing speed, parallelism, and interconnection. Many schemes for optical adaptive filtering of electronic signals are available in the literature. The new optical adaptive filters described in this paper are for optical processing of guided lightwave signals, not electronic signals. We analyzed the convergence or learning characteristics of the adaptive filtering process as a function of the filter parameters and the fiber optic hardware errors. From this analysis we found that the effects of the optical round-off errors and noise can be reduced, and the learning speed can be comparatively increased in our design through an optimal selection of the filter parameters. A general knowledge of the fiber optic hardware, the statistics of the lightwave signal, and the desired goal of the adaptive processing are enough for this optimum selection of the parameters. Detailed computer simulations validate the theoretical results of performance optimization. PMID:20700365
The Marginalized Particle Filter for Automotive Tracking Applications
Gustafsson, Fredrik
1 The Marginalized Particle Filter for Automotive Tracking Applications Andreas Eidehall Thomas Sch surroundings (lane geometry and the position of other vehicles), which is needed for intelligent automotive in a nonlinear estimation problem. For automotive tracking systems, these problems are traditionally handled
The Marginalized Particle Filter for Automotive Tracking Applications
Schön, Thomas
The Marginalized Particle Filter for Automotive Tracking Applications Andreas Eidehall Thomas B surroundings (lane geometry and the position of other vehicles), which is needed for intelligent automotive in a nonlinear estimation problem. For automotive tracking systems, these problems are traditionally handled
Model Adaptation for Prognostics in a Particle Filtering Framework
NASA Technical Reports Server (NTRS)
Saha, Bhaskar; Goebel, Kai Frank
2011-01-01
One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particle filters works in most part due to the fact that they are not subject to the "curse of dimensionality", i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for "well-designed" particle filters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particle filter.
Multi-robot Simultaneous Localization and Mapping using Particle Filters
Del Moral , Pierre
Multi-robot Simultaneous Localization and Mapping using Particle Filters Andrew Howard NASA Jet Propulsion Laboratory Pasadena, California 91109, U.S.A. Email: abhoward@robotics.jpl.nasa.gov Abstract-- This paper describes an on-line algorithm for multi- robot simultaneous localization and mapping (SLAM). We
Particle filter based DOA estimation for multiple source tracking (MUST)
Thomas Wiese; Heiko Claussen; Justinian Rosca
2011-01-01
Direction of arrival estimation is a well researched topic and represents an important building block for higher level interpretation of data. The Bayesian algorithm proposed in this paper (MUST) can estimate and track the direction of multiple, possibly correlated, wideband sources. MUST approximates the posterior probability density function of the source directions in time-frequency domain with a particle filter. In
Geoacoustic and source tracking using particle filtering: Experimental results
Gerstoft, Peter
ocean acoustic environment using a moving acoustic source. This approach treats both the environmental using a forward model such as a normal mode or a parabolic equation model. To perform geoacoustic A particle filtering PF approach is presented for performing sequential geoacoustic inversion of a complex
Image Restoration Based on Adaptive MCMC Particle Filter
Hui Tian; Tingzhi Shen; Bing Hao; Yu Hu; Nan Yang
2009-01-01
In this paper, particle filter is applied in image restoration which can be posed as a recursive Bayesian estimation problem, in order to remove degeneracy phenomenon and alleviate the sample impoverishment problem, the convergence of Markov chain Monte Carlo (MCMC) method is introduced and used in resampling step, meanwhile a simple KLD sampling which separated from resampling step is combined
Vehicle Detection under Various Lighting Conditions by Incorporating Particle Filter
Yi-Ming Chan; Shih-Shinh Huang; Li-Chen Fu; Pei-Yung Hsiao
2007-01-01
We propose an automatic system to detect preceding vehicles on the highway under various lighting and different weather conditions based on the computer vision technologies. To adapt to different characteristics of vehicle appearance at daytime and nighttime, four cues including underneath, vertical edge, symmetry and taillight are fused for the preceding vehicle detection. By using particle filter with four cues
Transductive inference for color-based particle filter tracking
Jiang Li; Chin-seng Chua
2003-01-01
Robust real-time tracking of non-rigid objects in a dynamic environment is a challenging task. Among various cues in tracking, color can provide an efficient visual cue for this type of tracking problem because of its invariance in the presence of changing complex shapes and appearances. To track the color object, a particle filter uses several hy- potheses simultaneously and weights
Homogeneity Localization Using Particle Filters With Application to Noise Estimation
Mohammed Ghazal; Aishy Amer
2011-01-01
This paper proposes a method for localizing homo- geneity and estimating additive white Gaussian noise (AWGN) variance in images. The proposed method uses spatially and sparsely scattered initial seeds and utilizes particle filtering tech- niques to guide their spatial movement towards homogeneous locations. This way, the proposed method avoids the need to per- form the full search associated with block-based
Particle Filter Based Object Tracking with Sift and Color Feature
Saeid Fazli; Hamed Moradi Pour; Hamed Bouzari
2009-01-01
Visual object tracking is an important topic in multimedia technologies. This paper presents robust implementation of an object tracker using a vision system that takes into consideration partial occlusions, rotation and scale for a variety of different objects. A scale invariant feature transform (SIFT) based color particle filter algorithm is proposed for object tracking in real scenarios. The Scale Invariant
Point Set Registration via Particle Filtering and Stochastic Dynamics
Romeil Sandhu; Samuel Dambreville; Allen Tannenbaum
2010-01-01
In this paper, we propose a particle filtering approach for the problem of registering two point sets that differ by a rigid body transformation. Typically, registration algorithms compute the transformation parameters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem, in which
Application of particle filters to MIMO wireless communications
Kris Huber; Simon Haykin
2003-01-01
The implementation of current space-time codes is often performed under the assumption that the additive channel noise is white and Gaussian, and that the receiver has precise knowledge of the realization of the fading process. Here we study the application of particle filters to MIMO systems in order to reduce the uncertainty in the estimation of the channel fading coefficients.
Fast, parallel implementation of particle filtering on the GPU architecture
NASA Astrophysics Data System (ADS)
Gelencsér-Horváth, Anna; Tornai, Gábor János; Horváth, András; Cserey, György
2013-12-01
In this paper, we introduce a modified cellular particle filter (CPF) which we mapped on a graphics processing unit (GPU) architecture. We developed this filter adaptation using a state-of-the art CPF technique. Mapping this filter realization on a highly parallel architecture entailed a shift in the logical representation of the particles. In this process, the original two-dimensional organization is reordered as a one-dimensional ring topology. We proposed a proof-of-concept measurement on two models with an NVIDIA Fermi architecture GPU. This design achieved a 411- ?s kernel time per state and a 77-ms global running time for all states for 16,384 particles with a 256 neighbourhood size on a sequence of 24 states for a bearing-only tracking model. For a commonly used benchmark model at the same configuration, we achieved a 266- ?s kernel time per state and a 124-ms global running time for all 100 states. Kernel time includes random number generation on the GPU with curand. These results attest to the effective and fast use of the particle filter in high-dimensional, real-time applications.
Particle filtering for passive fathometer tracking Zoi-Heleni Michalopoulou
Gerstoft, Peter
, California 92093-0238 cyardim@ucsd.edu, gerstoft@ucsd.edu Abstract: Seabed interface depths and fathometer by the multiple model particle filter even for noisy fathometer outputs. VC 2012 Acoustical Society of America the seabed. This requires a coherent processing tech- nique such as beamforming on the vertical line array
Localization using omnivision-based manifold particle filters
NASA Astrophysics Data System (ADS)
Wong, Adelia; Yousefhussien, Mohammed; Ptucha, Raymond
2015-01-01
Developing precise and low-cost spatial localization algorithms is an essential component for autonomous navigation systems. Data collection must be of sufficient detail to distinguish unique locations, yet coarse enough to enable real-time processing. Active proximity sensors such as sonar and rangefinders have been used for interior localization, but sonar sensors are generally coarse and rangefinders are generally expensive. Passive sensors such as video cameras are low cost and feature-rich, but suffer from high dimensions and excessive bandwidth. This paper presents a novel approach to indoor localization using a low cost video camera and spherical mirror. Omnidirectional captured images undergo normalization and unwarping to a canonical representation more suitable for processing. Training images along with indoor maps are fed into a semi-supervised linear extension of graph embedding manifold learning algorithm to learn a low dimensional surface which represents the interior of a building. The manifold surface descriptor is used as a semantic signature for particle filter localization. Test frames are conditioned, mapped to a low dimensional surface, and then localized via an adaptive particle filter algorithm. These particles are temporally filtered for the final localization estimate. The proposed method, termed omnivision-based manifold particle filters, reduces convergence lag and increases overall efficiency.
Efficient Particle Filtering for Road-Constrained Target Tracking
Singh, Tarunraj
Efficient Particle Filtering for Road-Constrained Target Tracking Yang Cheng Department characteristic of ground target tracking is that prior nonstandard information such as target speed con- straints of multiple model esti- mators to ground target tracking were presented in Ref. [1], [3], [4], [5], [6
Multiple Target Tracking Using Particle Filtering and Adaptive Waveform Design
Nehorai, Arye
Multiple Target Tracking Using Particle Filtering and Adaptive Waveform Design I. Kyriakides, T.i@unic.ac.cy, ttrueblo@asu.edu, morrell@asu.edu, papandreou@asu.edu Abstract--In multiple target tracking with radar stronger targets. The result is lost tracks and deteriorated joint tracking performance. In this work, we
Multiple Target Tracking With Constrained Motion Using Particle Filtering Methods
Del Moral , Pierre
Multiple Target Tracking With Constrained Motion Using Particle Filtering Methods A Qualifying successfully used in target tracking appli- cations when the state and measurement models are nonlinear and the associated noise is non-Gaussian. Among the most difficult scenarios of target tracking, are multiple target
Linkping University Post Print Particle Filter Theory and Practice with
Del Moral , Pierre
come from real-time implementations. This part also provides complete code examples. Manuscript Linkoping University Sweden The particle filter (PF) was introduced in 1993 as a numerical approximation of successful applications described in literature. This tutorial serves two purposes: to survey the part
Optimal Filtering in the Salamander Retina
Fred Rieke; W. Geoffrey Owen; William Bialek
The dark-adapted visual system can count photons with a reliability lim- ited by thermal noise in the rod photoreceptors - the processing circuitry between the rod cells and the brain is essentially noiseless and in fact may be close to optimal. Here we design an optimal signal processor which estimates the time-varying light intensity at the retina based on the
Boundary Conditions in Particle Swarm Optimization Revisited
Shenheng Xu; Yahya Rahmat-Samii
2007-01-01
In order to enforce particles to search inside the solution space of interest during the optimization procedure, various boundary conditions are currently used in particle swarm optimization (PSO) algorithms. The performances, however, vary considerably with the dimensionality of the problem and the location of the global optimum in the solution space. In this paper, different boundary conditions are categorized into
A New Particle Swarm Optimization Technique
Simon, Dan
· Particle Swarm Optimization (PSO) Invented by Eberhart & Kennedy in 1995 Motivated by social behavior PSO · Discrete PSO Flip each bit probabilistically Research has been done on benchmark functions · Individual and groups #12;9/8/2005 7 New Particle Swarm Optimization · Formulae are similar to PSO · pid
A new optimizer using particle swarm theory
Russell Eberhart; James Kennedy
1995-01-01
The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed
Adding Local Search to Particle Swarm Optimization
Sanjoy Das; Praveen Koduru; Min Gui; Michael Cochran; Austin Wareing; Stephen M. Welch; Bruce R. Babin
2006-01-01
Particle swarm optimization is a stochastic algorithm for optimizing continuous functions. It uses a population of particles that follow trajectories through the search space towards good optima. This paper proposes adding a local search component to PSO to improve its convergence speed. Two possible methods are discussed. The first adds a term containing estimated gradient information to the velocity of
Particle Optimization with Metamodel for Crack Characterization
Paris-Sud XI, Université de
testing is introduced. It is based on particle swarm optimization coupled with a metamodel. This metamodelParticle Optimization with Metamodel for Crack Characterization R´emi Douvenot, Marc Lambert to feed learning algorithms [3], [4] that give real-time inverse results. Also, one might bypass adaptive
An improved particle swarm optimization algorithm
Yan Jiang; Tiesong Hu; Chongchao Huang; Xianing Wu
2007-01-01
An improved particle swarm optimization (IPSO) is proposed in this paper. In the new algorithm, a population of points sampled randomly from the feasible space. Then the population is partitioned into several sub-swarms, each of which is made to evolve based on particle swarm optimization (PSO) algorithm. At periodic stages in the evolution, the entire population is shuffled, and then
Optimal operational planning for cogeneration system using particle swarm optimization
Tatsuya Tsukada; T. Tamura; Shinji Kitagawa; Yoshikazu Fukuyama
2003-01-01
This paper proposes optimal operational planning for a cogeneration system (CGS) using particle swarm optimization (PSO). CGS is usually connected to various facilities such as refrigerators, reservoirs, and cooling towers. In order to generate optimal operational planning for CGS, startup and shutdown status, and input values of the facilities for each control interval should be determined. The facilities may have
Solving constrained optimization problems with hybrid particle swarm optimization
Erwie Zahara; Chia-Hsin Hu
2008-01-01
Constrained optimization problems (COPs) are very important in that they frequently appear in the real world. A COP, in which both the function and constraints may be nonlinear, consists of the optimization of a function subject to constraints. Constraint handling is one of the major concerns when solving COPs with particle swarm optimization (PSO) combined with the Nelder-Mead simplex search
Adaptive Multi-user Detection Based on Robust Particle Filter in CDMA System
Yafei Han; Guolong Liang
2009-01-01
In this paper, we proposed a robust particle filter algorithm for adaptive multi-user detection (MUD) in synchronous system over fading code division multiple access (CDMA) channels. Particle Filter (PF) is an efficient method for MUD. The sequential importance sampling and re-sampling (SISR) filter is the representative of PF. AS the importance sampling density of SISR filter is independent of measurement
Vacuum Chuck Holds Filter Pad For Counting Particles
NASA Technical Reports Server (NTRS)
Berry, Anthony; Herren, Billy H.
1991-01-01
Microscope-stage holder keeps filter pad flat to keep it in focus. Specimen holder is special vacuum chuck that applies suction through flat screen. Suction keeps filter pad flat against specimen holder while microscope stage moving to scan areas denoted by grid. In microscope system equipped with video camera, image-analyzing/particle-counting computer, and automatic focus, use of stage speeds count considerably by eliminating need to stop frequently for manual refocusing. Technician free to perform other tasks while computer controls translation of stage and takes count automatically.
A unified optimization framework for equalization filter synthesis
Jihong Ren; Mark R. Greenstreet
2005-01-01
We present a novel method for jointly optimizing FIR filters for pre-equalization, decision feedback equalization, and near-end crosstalk cancellation. The unified optimization problem is a linear program, and we describe sparse matrix techniques for its efficient solution. We illustrate our approach with uni- and bi-directional buses using differential signaling in both intra-board and cross-backplane scenarios.
Robust line tracking using a particle filter for camera pose estimation
Fakhreddine Ababsa; Malik Mallem
2006-01-01
This paper presents a robust line tracking approach for camera pose estimation which is based on particle filtering framework. Particle filters are sequential Monte Carlo methods based on point mass (or \\
ON LOW-POWER ANALOG IMPLEMENTATION OF PARTICLE FILTERS FOR TARGET TRACKING
Odam, Kofi
- grammable Gate Array (FPGA) prototype for a particle filter algo- rithm. This was followed by VLSI]. The remainder of this paper is organized as follows. Section 2 describes the particle filter based tracking
Optimal filtering in multipulse sequences for nuclear quadrupole resonance detection
NASA Astrophysics Data System (ADS)
Osokin, D. Ya.; Khusnutdinov, R. R.; Mozzhukhin, G. V.; Rameev, B. Z.
2014-05-01
The application of the multipulse sequences in nuclear quadrupole resonance (NQR) detection of explosive and narcotic substances has been studied. Various approaches to increase the signal to noise ratio (SNR) of signal detection are considered. We discussed two modifications of the phase-alternated multiple-pulse sequence (PAMS): the 180° pulse sequence with a preparatory pulse and the 90° pulse sequence. The advantages of optimal filtering to detect NQR in the case of the coherent steady-state precession have been analyzed. It has been shown that this technique is effective in filtering high-frequency and low-frequency noise and increasing the reliability of NQR detection. Our analysis also shows the PAMS with 180° pulses is more effective than PSL sequence from point of view of the application of optimal filtering procedure to the steady-state NQR signal.
Source optimization using particle swarm optimization algorithm in photolithography
NASA Astrophysics Data System (ADS)
Wang, Lei; Li, Sikun; Wang, Xiangzhao; Yan, Guanyong; Yang, Chaoxing
2015-03-01
In recent years, with the availability of freeform sources, source optimization has emerged as one of the key techniques for achieving higher resolution without increasing the complexity of mask design. In this paper, an efficient source optimization approach using particle swarm optimization algorithm is proposed. The sources are represented by pixels and encoded into particles. The pattern fidelity is adopted as the fitness function to evaluate these particles. The source optimization approach is implemented by updating the velocities and positions of these particles. The approach is demonstrated by using two typical mask patterns, including a periodic array of contact holes and a vertical line/space design. The pattern errors are reduced by 66.1% and 39.3% respectively. Compared with the source optimization approach using genetic algorithm, the proposed approach leads to faster convergence while improving the image quality at the same time. The robustness of the proposed approach to initial sources is also verified.
Na-Faraday rotation filtering: The optimal point
Kiefer, Wilhelm; Löw, Robert; Wrachtrup, Jörg; Gerhardt, Ilja
2014-01-01
Narrow-band optical filtering is required in many spectroscopy applications to suppress unwanted background light. One example is quantum communication where the fidelity is often limited by the performance of the optical filters. This limitation can be circumvented by utilizing the GHz-wide features of a Doppler broadened atomic gas. The anomalous dispersion of atomic vapours enables spectral filtering. These, so-called, Faraday anomalous dispersion optical filters (FADOFs) can be by far better than any commercial filter in terms of bandwidth, transition edge and peak transmission. We present a theoretical and experimental study on the transmission properties of a sodium vapour based FADOF with the aim to find the best combination of optical rotation and intrinsic loss. The relevant parameters, such as magnetic field, temperature, the related optical depth, and polarization state are discussed. The non-trivial interplay of these quantities defines the net performance of the filter. We determine analytically the optimal working conditions, such as transmission and the signal to background ratio and validate the results experimentally. We find a single global optimum for one specific optical path length of the filter. This can now be applied to spectroscopy, guide star applications, or sensing. PMID:25298251
Na-Faraday rotation filtering: the optimal point.
Kiefer, Wilhelm; Löw, Robert; Wrachtrup, Jörg; Gerhardt, Ilja
2014-01-01
Narrow-band optical filtering is required in many spectroscopy applications to suppress unwanted background light. One example is quantum communication where the fidelity is often limited by the performance of the optical filters. This limitation can be circumvented by utilizing the GHz-wide features of a Doppler broadened atomic gas. The anomalous dispersion of atomic vapours enables spectral filtering. These, so-called, Faraday anomalous dispersion optical filters (FADOFs) can be by far better than any commercial filter in terms of bandwidth, transition edge and peak transmission. We present a theoretical and experimental study on the transmission properties of a sodium vapour based FADOF with the aim to find the best combination of optical rotation and intrinsic loss. The relevant parameters, such as magnetic field, temperature, the related optical depth, and polarization state are discussed. The non-trivial interplay of these quantities defines the net performance of the filter. We determine analytically the optimal working conditions, such as transmission and the signal to background ratio and validate the results experimentally. We find a single global optimum for one specific optical path length of the filter. This can now be applied to spectroscopy, guide star applications, or sensing. PMID:25298251
Multiple object tracking with energetic particle filtering and GVF-snake
Dong Chunli; Dong Yuning; Wang li; Liu jie
2008-01-01
A novel adaptive multi-object tracking algorithm with modified particle filtering and GVF-snake is proposed. Through the combination of modified particle filtering and GVF-snake, a novel energetic particle filtering (EPF) object tracking algorithm is proposed; and the multiple objects can be tracked with modified K-means clustering and energetic particle filtering (EPF). The tracking tactic for partially occluded objects is also proposed
Xiongbiao Luo; Mori, Kensaku
2014-01-01
Electromagnetically navigated endoscopy, which is increasingly applied in endoscopic interventions, utilizes an electromagnetic sensor attached at the endoscope tip to measure the endoscope movements and to navigate the endoscope in the region of interest in the body. Due to patient motion and magnetic field distortion, sensor electromagnetic tracking (EMT) measurement inaccuracy and dynamic jitter errors remain challenging for electromagnetic navigation. This paper proposes a new tracking framework of an animated particle filter that integrates adaptive particle swarm optimization into a generic particle filter to significantly boost electromagnetic trackers. We validate our method on a dynamic phantom and compare it to state-of-the-art EMT methods. Our experimental results demonstrate the effectiveness and robustness of our method, which provides position and orientation accuracy of 2.48 mm, 4.69° that significantly outperformed previous methods at least with tracking error of 4.19 mm, 7.75°. The tracking smoothness was improved from 4.09 mm, 3.37° to 1.84 mm, 2.52°. Our method successfully tackled the particle impoverishment better than standard particle filters. PMID:23934651
Accelerating Particle Filter using Randomized Multiscale and Fast Multipole Type Methods
Averbuch, Amir
1 Accelerating Particle Filter using Randomized Multiscale and Fast Multipole Type Methods Gil that accelerates the computation of particle filters. Unlike the conventional way, which calculates weights over Shabat, Yaniv Shmueli, Amit Bermanis and Amir Averbuch Abstract--Particle filter is a powerful method
Nonlinear system fault detection and isolation based on bootstrap particle filters
LeGland, François
Nonlinear system fault detection and isolation based on bootstrap particle filters Qinghua ZHANG, France. Email: zhang@irisa.fr Abstract-- A particle filter based method for nonlinear system fault from the basic bootstrap particle filter, and capable of rejecting a subset of the faults possibly
Parameter estimation in the stochastic Morris-Lecar neuronal model with particle filter methods
Samson, Adeline
with particle filter methods 2 Therefore, there is a growing demand for robust methods to estimate biophysicalParameter estimation in the stochastic Morris-Lecar neuronal model with particle filter methods model. In this paper, we propose a sequential Monte Carlo particle filter algorithm to impute
ROBUST VISUAL TRACKING VIA MCMC-BASED PARTICLE FILTERING D-N. Truong Cong1
Boyer, Edmond
ROBUST VISUAL TRACKING VIA MCMC-BASED PARTICLE FILTERING D-N. Truong Cong1 , F. Septier1 , C tracking, particle filtering, MCMC, HOG. 1. INTRODUCTION Visual tracking is one of the most fundamental- ture [1], particle filtering (PF), which was first introduced by Isard and Blake [2], has obtained
A Real-Time Object Tracking System Using a Particle Filter
Jung Uk Cho; Seung Hun Jin; Xuan Dai Pham; Jae Wook Jeon; Jong-eun Byun; Hoon Kang
2006-01-01
Particle filters have attracted much attention due to their robust tracking performance in cluttered environments. Particle filters maintain multiple hypotheses simultaneously and use a probabilistic motion model to predict the position of the moving object, and this constitutes a bottleneck to the use of particle filtering in real-time systems due to the expensive computations required. In order to track moving
Human Motion Tracking using a Color-Based Particle Filter Driven by Optical Flow
Paris-Sud XI, Université de
-based particle filter driven by optical flow. The proposed approach is robust to strong occlusion and fullHuman Motion Tracking using a Color-Based Particle Filter Driven by Optical Flow Tony Tung. In addition, we propose to integrate color and motion cues in a particle filter framework to track human body
GATE: A Novel Robust Object Tracking Method Using the Particle Filtering and Level Set Method
New South Wales, University of
GATE: A Novel Robust Object Tracking Method Using the Particle Filtering and Level Set Method Cheng histogram enables particle filters, to some extent, to be more robust and insensitive to the changes tracking based on the particle filtering method employed in recursive Bayesian estimation and image
Markov Chain Distributed Particle Filters (MCDPF) Sun Hwan Lee* and Matthew West
West, Matthew
-- Distributed particle filters (DPF) are known to provide robustness for the state estimation problem and canMarkov Chain Distributed Particle Filters (MCDPF) Sun Hwan Lee* and Matthew West Abstract message path. In this paper, the Markov Chain Distributed Particle Filter (MCDPF) algorithm is pro- posed
A particle filter for joint detection and tracking of color objects
Jacek Czyz; Branko Ristic; Benoit M. Macq
2007-01-01
Color is a powerful feature for tracking deformable objects in image sequences with complex backgrounds. The color particle filter has proven to be an efficient, simple and robust tracking algorithm. In this paper, we present a hybrid valued sequential state estimation algorithm, and its particle filter-based implementation, that extends the standard color particle filter in two ways. First, target detection
Widyawardana Adiprawita; Adang Suwandi Ahmad; Jaka Sembiring; Bambang R. Trilaksono
2011-01-01
This paper present a particle filter also known as Monte Carlo Localization (MCL) to solve the localization problem presented before. A new resampling mechanism is proposed. This new resampling mechanism enables the particle filter to converge quicker and more robust to kidnaping problem. This particle filter is simulated in MATLAB and also experimented physically using a simple autonomous mobile robot
HYBRID PARTICLE FILTER AND MEAN SHIFT TRACKER WITH ADAPTIVE TRANSITION MODEL
Cavallaro, Andrea
with a new adaptive state transition model. Particle Filter is robust to partial and total occlusions, canHYBRID PARTICLE FILTER AND MEAN SHIFT TRACKER WITH ADAPTIVE TRANSITION MODEL Emilio Maggio propose a tracking algorithm based on a combination of Particle Filter and Mean Shift, and enhanced
Design and implementation of embedded computer vision systems based on particle filters
Sankalita Saha; Neal K. Bambha; Shuvra S. Bhattacharyya
2010-01-01
Particle filtering methods are gradually attaining significant importance in a variety of embedded computer vision applications. For example, in smart camera systems, object tracking is a very important application and particle filter based tracking algorithms have shown promising results with robust tracking performance. However, most particle filters involve vast amount of computational complexity, thereby intensifying the challenges faced in their
Robust Tracking-by-Detection using a Detector Confidence Particle Filter Michael D. Breitenstein1
Robust Tracking-by-Detection using a Detector Confidence Particle Filter Michael D. Breitenstein1 approach for multi-person tracking- by-detection in a particle filtering framework. In addition to final Monte Carlo methods (or Particle Filters) [8] offer a framework for representing the tracking
A particle filter based algorithm for robust tracking of hands and face under occlusion
Oya Aran; Lale Akarun
2008-01-01
This paper presents a particle filter based algorithm for tracking face and hands of a signer. During signing, the hands and the face occlude each other frequently and a proper multiple object tracking algorithm is needed for accurate results. We use separate particle filters for the two hands and the face, where each filter effects the particle weights of others.
Memory-based Particle Filter for face pose tracking robust under complex dynamics
Dan MIKAMI; Kazuhiro Otsuka; Junji Yamato
2009-01-01
A novel particle filter, the memory-based particle filter (M-PF), is proposed that can visually track moving objects that have complex dynamics. We aim to realize robustness against abrupt object movements and quick recovery from tracking failure caused by factors such as occlusions. To that end, we eliminate the Markov assumption from the previous particle filtering framework and predict the prior
The effect of particle charge on penetration in an electret filter
ROBERT A. FJELD; TIMOTHY M. OWENS
1988-01-01
Experiments were performed to identify collection mechanisms for 0.5 ?m diameter particles in electret filter media and to determine the effect of particle charge on penetration. Highly monodisperse polystyrene particles were charged to various levels, and their penetration through charged to various levels, and their penetration through charged and discharged electret filters was measured with an optical particle counter. Particle
Optimal design of Biquad Switched-Capacitor Active Filters
Jo, Han Cheol
1986-01-01
objectives. 33 Set of realizable objects Optimal g tradeofi' curve Figure 4-2 Realizable multiple objectives and optimal trade-off curve. F=minimized C~, & with fixed S& and f /fp DMG k (finite) Figure 4-3 Construction of optimal tradeoff-curve (C... in Biquadratic SC filters is presented. There are two cost functions considered, one is a single objective function, the other is a multiple objective function. A weighting function is used in the last case to emphasize the importance of the variables...
Homogeneity localization using particle filters with application to noise estimation.
Ghazal, Mohammed; Amer, Aishy
2011-07-01
This paper proposes a method for localizing homogeneity and estimating additive white Gaussian noise (AWGN) variance in images. The proposed method uses spatially and sparsely scattered initial seeds and utilizes particle filtering techniques to guide their spatial movement towards homogeneous locations. This way, the proposed method avoids the need to perform the full search associated with block-based noise estimation methods. To achieve this, the paper proposes for the particle filter a dynamic model and a homogeneity observation model based on Laplacian structure detectors. The variance of AWGN is robustly estimated from the variances of blocks in the detected homogeneous areas. A proposed adaptive trimmed-mean based robust estimator is used to account for the reduction in estimation samples from the full search approach. Our results show that the proposed method reduces the number of homogeneity measurements required by block-based methods while achieving more accuracy. PMID:21138803
Optimal color image restoration: Wiener filter and quaternion Fourier transform
NASA Astrophysics Data System (ADS)
Grigoryan, Artyom M.; Agaian, Sos S.
2015-03-01
In this paper, we consider the model of quaternion signal degradation when the signal is convoluted and an additive noise is added. The classical model of such a model leads to the solution of the optimal Wiener filter, where the optimality with respect to the mean square error. The characteristic of this filter can be found in the frequency domain by using the Fourier transform. For quaternion signals, the inverse problem is complicated by the fact that the quaternion arithmetic is not commutative. The quaternion Fourier transform does not map the convolution to the operation of multiplication. In this paper, we analyze the linear model of the signal and image degradation with an additive independent noise and the optimal filtration of the signal and images in the frequency domain and in the quaternion space.
NASA Astrophysics Data System (ADS)
Hirpa, F. A.; Gebremichael, M.; Hopson, T. M.; Wojick, R.
2011-12-01
We present results of data assimilation of ground discharge observation and remotely sensed soil moisture observations into Sacramento Soil Moisture Accounting (SACSMA) model in a small watershed (1593 km2) in Minnesota, the Unites States. Specifically, we perform assimilation experiments with Ensemble Kalman Filter (EnKF) and Particle Filter (PF) in order to improve streamflow forecast accuracy at six hourly time step. The EnKF updates the soil moisture states in the SACSMA from the relative errors of the model and observations, while the PF adjust the weights of the state ensemble members based on the likelihood of the forecast. Results of the improvements of each filter over the reference model (without data assimilation) will be presented. Finally, the EnKF and PF are coupled together to further improve the streamflow forecast accuracy.
Mobile robot based odor source localization via particle filter
Ji-Gong Li; Qing-Hao Meng; Fei Li; Ming Zeng; Dorin Popescu
2009-01-01
We consider odor-source localization using a mobile robot in a time-variant airflow-field environment. Novel plume tracing and odor-source declaration methods are presented. When odor plume clue is found, an odor-patch path is estimated by a dynamic-window approach, and the robot traces the plume along a route planned from the odor-patch path. In parallel, a particle filter is used to localize
Particle Filter SLAM with High Dimensional Vehicle Model
David Törnqvist; Thomas B. Schön; Rickard Karlsson; Fredrik Gustafsson
2009-01-01
This work presents a particle filter method closely related to Fastslam for solving the simultaneous localization and mapping (slam) problem. Using the standard Fastslam algorithm, only low-dimensional vehicle models can be handled due to computational constraints. In this work, an extra factorization\\u000a of the problem is introduced that makes high-dimensional vehicle models computationally feasible. Results using experimental\\u000a data from an
Sequential approach to multisensor resource management using particle filters
Dawn E. Penny; Mark Williams
2000-01-01
Elements from data fusion, optimisation and particle filtering are brought together to form the Multi-Sensor Fusion Management (MSFM) algorithm. The algorithm provides a framework for combining the information from multiple sensors and producing good solutions to the problem of how best to deploy\\/use these and\\/or other sensors to optimise some criteria in the future. A problem from Anti-Submarine Warfare (ASW)
An Improved Particle Filter for Tracking Color Object
Tao Zhang; Shumin Fei; Xiaodong Li; Hong Lu
2008-01-01
Robust real-time tracking of non-rigid objects is a challenging task. Color is a powerful feature for tracking deformable objects in image sequences with complex backgrounds. Color distribution is applied, as it is robust to partial occlusion, is rotation and scale invariant and computationally efficient. Particle filter has been proven very successful for non-linear and non-Gaussian estimation tracking problems. The article
Variational Particle Filter for Multi-Object Tracking
Yonggang Jin; Farzin Mokhtarian
2007-01-01
The paper proposes an edge-based multi-object tracking framework which deals with tracking multiple objects with occlusions using a variational particle filter. Object is modelled by a mixture of a non-parametric contour model and a non-parametric edge model using kernel density estimation. Visual tracking with a mixture model is formulated as a Bayesian incomplete data problem, where measurements in an image
Object Tracking with Particle Filter Using Color Information
Peihua Li; Haijing Wang
2007-01-01
Color-based particle filter for object tracking has been an active research topic in recent years. Despite great efforts of\\u000a many researchers, there still remains to be solved the problem of contradiction between efficiency and robustness. The paper\\u000a makes an attempt to partially solve this problem. Firstly, the Integral Histogram Image is introduced by which histogram of any rectangle region can
High Frequency Financial Time Series Forecasting via Particle Filtering
Zhang Gaoyu; Li Qiongfei; Luo Qing; Zhou Zhizhao
2009-01-01
Of the strong non-Gauss characteristic, the high frequency financial time series could not be analyzed and forecasted by traditional statistics method any more. For inaccurately estimating the realized volatility using the limited high frequency data created by the market operation, a novel forecasting method is proposed: after modeling the realized volatility, the particle filtering technology for non-Gauss non-liner process is
Peyrodie, Laurent; Szurhaj, William; Bolo, Nicolas; Pinti, Antonio; Gallois, Philippe
2014-01-01
Muscle artifacts constitute one of the major problems in electroencephalogram (EEG) examinations, particularly for the diagnosis of epilepsy, where pathological rhythms occur within the same frequency bands as those of artifacts. This paper proposes to use the method dual adaptive filtering by optimal projection (DAFOP) to automatically remove artifacts while preserving true cerebral signals. DAFOP is a two-step method. The first step consists in applying the common spatial pattern (CSP) method to two frequency windows to identify the slowest components which will be considered as cerebral sources. The two frequency windows are defined by optimizing convolutional filters. The second step consists in using a regression method to reconstruct the signal independently within various frequency windows. This method was evaluated by two neurologists on a selection of 114 pages with muscle artifacts, from 20 clinical recordings of awake and sleeping adults, subject to pathological signals and epileptic seizures. A blind comparison was then conducted with the canonical correlation analysis (CCA) method and conventional low-pass filtering at 30?Hz. The filtering rate was 84.3% for muscle artifacts with a 6.4% reduction of cerebral signals even for the fastest waves. DAFOP was found to be significantly more efficient than CCA and 30?Hz filters. The DAFOP method is fast and automatic and can be easily used in clinical EEG recordings. PMID:25298967
Filtering of windborne particles by a natural windbreak
NASA Astrophysics Data System (ADS)
Bouvet, Thomas; Loubet, Benjamin; Wilson, John D.; Tuzet, Andree
2007-06-01
New measurements of the transport and deposition of artificial heavy particles (glass beads) to a thick ‘shelterbelt’ of maize (width/height ratio W/ H ? 1.6) are used to test numerical simulations with a Lagrangian stochastic trajectory model driven by the flow field from a RANS (Reynolds-averaged, Navier-Stokes) wind and turbulence model. We illustrate the ambiguity inherent in applying to such a thick windbreak the pre-existing (Raupach et al. 2001; Atmos. Environ. 35, 3373-3383) ‘thin windbreak’ theory of particle filtering by vegetation, and show that the present description, while much more laborious, provides a reasonably satisfactory account of what was measured. A sizeable fraction of the particle flux entering the shelterbelt across its upstream face is lifted out of its volume by the mean updraft induced by the deceleration of the flow in the near-upstream and entry region, and these particles thereby escape deposition in the windbreak.
A nonmonotone filter trust region method for nonlinear constrained optimization
NASA Astrophysics Data System (ADS)
Su, Ke; Pu, Dingguo
2009-01-01
In this paper, we present a nonmonotone filter trust region algorithm for solving nonlinear equality constrained optimization. Similar to Bryd-Omojokun class of algorithms, each step is composed of a quasi-normal step and a tangential step. This new method has more flexibility for the acceptance of the trial step compared to the filter methods, and requires less computational costs compared with the monotone methods. Under reasonable conditions, we give the globally convergence properties. Numerical tests are presented that confirm the efficiency of the approach.
Carneiro, Gustavo
of the search process. In this paper we tackle the second problem by using a deep belief network learning. Keywords: Deep belief Networks, lip segmentation, optimization algorithms, search methods, tracking 1EFFICIENT SEARCH METHODS AND DEEP BELIEF NETWORKS WITH PARTICLE FILTERING FOR NON-RIGID TRACKING
Models of filter-based particle light absorption measurements
NASA Astrophysics Data System (ADS)
Hamasha, Khadeejeh M.
Light absorption by aerosol is very important in the visible, near UN, and near I.R region of the electromagnetic spectrum. Aerosol particles in the atmosphere have a great influence on the flux of solar energy, and also impact health in a negative sense when they are breathed into lungs. Aerosol absorption measurements are usually performed by filter-based methods that are derived from the change in light transmission through a filter where particles have been deposited. These methods suffer from interference between light-absorbing and light-scattering aerosol components. The Aethalometer is the most commonly used filter-based instrument for aerosol light absorption measurement. This dissertation describes new understanding of aerosol light absorption obtained by the filter method. The theory uses a multiple scattering model for the combination of filter and particle optics. The theory is evaluated using Aethalometer data from laboratory and ambient measurements in comparison with photoacoustic measurements of aerosol light absorption. Two models were developed to calculate aerosol light absorption coefficients from the Aethalometer data, and were compared to the in-situ aerosol light absorption coefficients. The first is an approximate model and the second is a "full" model. In the approximate model two extreme cases of aerosol optics were used to develop a model-based calibration scheme for the 7-wavelength Aethalometer. These cases include those of very strong scattering aerosols (Ammonium sulfate sample) and very absorbing aerosols (kerosene soot sample). The exponential behavior of light absorption in the strong multiple scattering limit is shown to be the square root of the total absorption optical depth rather than linear with optical depth as is commonly assumed with Beer's law. 2-stream radiative transfer theory was used to develop the full model to calculate the aerosol light absorption coefficients from the Aethalometer data. This comprehensive model allows for studying very general cases of particles of various sizes embedded on arbitrary filter media. Application of this model to the Reno Aerosol Optics Study (Laboratory data) shows that the aerosol light absorption coefficients are about half of the Aethalometer attenuation coefficients, and there is a reasonable agreement between the model calculated absorption coefficients at 521 nm and the measured photoacoustic absorption coefficients at 532 nm. For ambient data obtained during the Las Vegas study, it shows that the model absorption coefficients at 521 nm are larger than the photoacoustic coefficients at 532 nm. Use of the 2-stream model shows that particle penetration depth into the filter has a strong influence on the interpretation of filter-based aerosol light absorption measurements. This is likely explanation for the difference found between model results for filter-based aerosol light absorption and those from photoacoustic measurements for ambient and laboratory aerosol.
Improving the LPJ-GUESS modelled carbon balance with a particle filter data assimilation technique
NASA Astrophysics Data System (ADS)
McRobert, Andrew; Scholze, Marko; Kemp, Sarah; Smith, Ben
2015-04-01
The recent increases in anthropogenic carbon dioxide (CO_2) emissions have disrupted the equilibrium in the global carbon cycle pools with the ocean and terrestrial pools increasing their respective storages to accommodate roughly half of the anthropogenic increase. Dynamic global vegetation models (DGVM) have been developed to quantify the modern carbon cycle changes. In this study, a particle filter data assimilation technique has been used to calibrate the process parameters in the DGVM LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator). LPJ-GUESS simulates individual plant function types (pft) as a competitive balance within high resolution forest patches. Thirty process parameters have been optimized twice, using both a sequential and iterative method of particle filter. The iterative method runs the model for the full time period of thirteen years and then evaluates the cost function from the mismatch of observations and model results before adjusting the parameters and repeating the full time period. The sequential method runs the model and particle filter for each year of the time series in order, adjusting the parameters between each year, then loops back to beginning of the series to repeat. For each particle, the model output of NEP (Net Ecosystem Productivity) is compared to eddy flux measurements from ICOS flux towers to minimize the cost function. A high-resolution regional carbon balance has been simulated for central Sweden using a network of several ICOS flux towers.
Document clustering using particle swarm optimization
Xiaohui Cui; Thomas E. Potok; Paul Palathingal
2005-01-01
Fast and high-quality document clustering algorithms play an important role in effectively navigating, summarizing, and organizing information. Recent studies have shown that partitional clustering algorithms are more suitable for clustering large datasets. However, the K-means algorithm, the most commonly used partitional clustering algorithm, can only generate a local optimal solution. In this paper, we present a particle swarm optimization (PSO)
Unified Particle Swarm Optimization in Dynamic Environments
Parsopoulos, Konstantinos
Introduction Particle Swarm Optimization (PSO) is a stochastic optimization algorithm that belongs to the category of swarm intelligence methods [1,2]. PSO has attained in- creasing popularity due to its ability a thorough investigation of PSO on a large number of dynamic test problems. Modifications of PSO that can
A modification to particle swarm optimization algorithm
Huiyuan Fan
2002-01-01
In this paper, a modification strategy is proposed for the particle swarm optimization (PSO) algorithm. The strategy adds an adaptive scaling term into the algorithm, which aims to increase its convergence rate and thereby to obtain an acceptable solution with a lower number of objective function evaluations. Such an improvement can be useful in many practical engineering optimizations where the
A social learning particle swarm optimization algorithm for scalable optimization
Jin, Yaochu
into particle swarm optimization (PSO) to develop a social learning PSO (SL-PSO). Unlike classical PSO variants in the proposed SL-PSO learns from any better particles (termed demonstrators) in the current swarm. In addition, to ease the burden of parameter settings, the proposed SL-PSO adopts a dimension-dependent parameter
Particle swarm optimization with Gaussian mutation
Natsuki Higashi; Hitoshi Iba
2003-01-01
In this paper we present particle swarm optimization with Gaussian mutation combining the idea of the particle swarm with concepts from evolutionary algorithms. This method combines the traditional velocity and position update rules with the ideas of Gaussian mutation. This model is tested and compared with the standard PSO and standard GA. The comparative experiments have been conducted on unimodal
GECCO'07, London 1 Particle Swarm Optimization
Li, Xiaodong
to PSO Original PSO, Inertia weight, constriction coefficient Particle Trajectories Simplified PSO; one or two particles Convergence aspects FIPS, Bare-bones, and other PSO variants Communication topologies Speciation and niching methods in PSO PSO for optimization in dynamic environments PSO for multiobjective
Robust visual tracking using multiple cues and improved particle filter
NASA Astrophysics Data System (ADS)
Tian, Guodong; Yang, Bo; Wang, Hongling
2009-10-01
A robust visual tracking method which can be used in complex environments is presented in this paper. The color cue and the shape cue are utilized to represent the target and fused together by democratic integration method. The multi-cue object representation is incorporated into the framework of particle filter which is a powerful probabilistic method for visual tracking. To each sample of the particle filter a mean shift operation is applied, which make the samples more effective such that the number of particles needed is significantly decreased. Unlike regular mean shift, in our method the number of mean shift iterations is limited according to the reliability of the color cue for two purposes. One is to prevent the particles from being misled by mean shift when the color cue is unreliable. The other is to reduce the waste of computation. Experimental results show that our method greatly improves the robustness and reduces the computational cost compared with the state-of-art methods.
Object Tracking Based on Particle Filter and Scale Invariant Feature Transform
Min Jiang; Lei Zhang; Yanli Huang
2010-01-01
Particle filter is a popular stochastic tracker for object tracking. In this paper, we proposed a novel algorithm for object tracking based on particle filter and Scale Invariant Feature Transform (SIFT). The result of SIFT matching does not adopt to reweight the particles as previous methods, we adopts a hybrid schema to supplement the particle distribution of traditional factor sampling
Information Gain-based Exploration Using Rao-Blackwellized Particle Filters
Grisetti, Giorgio
in which each particle represents a potential trajectory. Each particle furthermore carries its own map which is computed based on the associated trajectory. Whereas a Rao-Blackwellized particle filterInformation Gain-based Exploration Using Rao-Blackwellized Particle Filters Cyrill Stachniss
Joint tracking algorithm using particle filter and mean shift with target model updating
Bo Zhang; Weifeng Tian; Zhihua Jin
2006-01-01
Roughly, visual tracking algorithms can be divided into two main classes: deterministic tracking and stochastic tracking. Mean shift and particle filter are their typical representatives, respectively. Recently, a hybrid tracker, seamlessly integrating the respective advantages of mean shift and particle filter (MSPF) has achieved impressive success in robust tracking. The pivot of MSPF is to sample fewer particles using particle
Efficient particle filtering using RANSAC with application to 3D face tracking
Le Lu; Xiangtian Dai; Gregory D. Hager
2006-01-01
Particle filtering is a very popular technique for sequen- tial state estimation. However, in high-dimensional cases where the state dynamics are complex or poorly modeled, thousands of particles are usually required for real applica- tions. This paper presents a hybrid sampling solution that combines RANSAC and particle filtering. In this approach, RANSAC provides proposal particles that, with high proba- bility,
Near-optimal Kalman filters for multiparameter singularly perturbed linear systems
Hiroaki Mukaidani
2003-01-01
In this brief, we study the near-optimal Kalman filtering problem for multiparameter singularly perturbed system (MSPS). The attention is focused on the design of the near-optimal Kalman filters. It is shown that the resulting filters in fact remove ill-conditioning of the original full-order singularly perturbed Kalman filters. In addition the resulting filters can be used compared with the previously proposed
A Peptide Filtering Relation Quantifies MHC Class I Peptide Optimization
Goldstein, Leonard D.; Howarth, Mark; Cardelli, Luca; Emmott, Stephen; Elliott, Tim; Werner, Joern M.
2011-01-01
Major Histocompatibility Complex (MHC) class I molecules enable cytotoxic T lymphocytes to destroy virus-infected or cancerous cells, thereby preventing disease progression. MHC class I molecules provide a snapshot of the contents of a cell by binding to protein fragments arising from intracellular protein turnover and presenting these fragments at the cell surface. Competing fragments (peptides) are selected for cell-surface presentation on the basis of their ability to form a stable complex with MHC class I, by a process known as peptide optimization. A better understanding of the optimization process is important for our understanding of immunodominance, the predominance of some T lymphocyte specificities over others, which can determine the efficacy of an immune response, the danger of immune evasion, and the success of vaccination strategies. In this paper we present a dynamical systems model of peptide optimization by MHC class I. We incorporate the chaperone molecule tapasin, which has been shown to enhance peptide optimization to different extents for different MHC class I alleles. Using a combination of published and novel experimental data to parameterize the model, we arrive at a relation of peptide filtering, which quantifies peptide optimization as a function of peptide supply and peptide unbinding rates. From this relation, we find that tapasin enhances peptide unbinding to improve peptide optimization without significantly delaying the transit of MHC to the cell surface, and differences in peptide optimization across MHC class I alleles can be explained by allele-specific differences in peptide binding. Importantly, our filtering relation may be used to dynamically predict the cell surface abundance of any number of competing peptides by MHC class I alleles, providing a quantitative basis to investigate viral infection or disease at the cellular level. We exemplify this by simulating optimization of the distribution of peptides derived from Human Immunodeficiency Virus Gag-Pol polyprotein. PMID:22022238
Bystroff, Chris
of visual tracking fidelity, we designed a particle filter that incorporates tactile sensor data. The filter a database, has a vision system that can track the object, and tactile sensors that can aid tracking whenThe Application of Particle Filtering to Grasping Acquisition with Visual Occlusion and Tactile
Carmela Plebani; Stefano Listrani; Giovanna Tranfo; Francesca Tombolini
2012-01-01
Several studies show the increase of penetration through electrostatic filters during the exposure to an aerosol flow because of particle deposition on filter fibers. We studied the effect of increasing loads of paraffin oil aerosol on the penetration of selected particle sizes through a model of electrostatic filtering facepiece. FFP2 facepieces were exposed for 8 hr to a flow rate
IMAGE RESTORATION USING A HYBRID COMBINATION OF PARTICLE FILTERING AND WAVELET DENOISING
Havlicek, Joebob
IMAGE RESTORATION USING A HYBRID COMBINATION OF PARTICLE FILTERING AND WAVELET DENOISING Yan Zhai filter with wavelet shrinkage to achieve robust performance against inhomogeneous noise mix- tures. Specifically, the particle filter acts to suppress outlier-rich components of the noise while, in a subsequent
An Object-Tracking Algorithm Based on Multiple-Model Particle Filtering With State Partitioning
Yan Zhai; Mark B. Yeary; Samuel Cheng; Nasser D. Kehtarnavaz
2009-01-01
As evidenced by the recent works of many researchers, the particle-filtering (PF) framework has revolutionized probabilistic visual target tracking. In this paper, we present a new particle filter tracking algorithm that incorporates the multiple-model (MM) paradigm and the technique of state partitioning with parallel filters. Traditionally, most tracking algorithms assume that a target operates according to a single dynamic model.
A geometry-based particle filtering approach to white matter tractography
A geometry-based particle filtering approach to white matter tractography Peter Savadjiev1 for a robust inference of local tract geometry, which, in the context of the causal filter estima- tion, guides.g. [2], or particle filters, e.g. [1, 7]. In this paper, we propose a novel tractography approach which
A Framework to Integrate Particle Filters for Robust Tracking in Non-Stationary Environments
Moreno-Noguer, Francesc
A Framework to Integrate Particle Filters for Robust Tracking in Non-Stationary Environments changes of the target's position or appearance. Particle filters have been demonstrated to be robust propose a new framework to integrate several parti- cle filters, in order to obtain a robust tracking
A blind particle filtering detector of signals transmitted over flat fading channels
Yufei Huang; Petar M. Djuric
2004-01-01
A new particle filtering detector (PFD) is proposed for blind signal detection over flat Rayleigh fading channels whose model coefficients are unknown. The detector employs a hybrid importance function and a mixture Kalman filter. It also incorporates an auxiliary particle filtering strategy with a smoothing kernel in the resampling step. Further, by considering practical information of communication systems and the
Ridge filter design for a particle therapy line
NASA Astrophysics Data System (ADS)
Kim, Chang Hyeuk; Han, Garam; Lee, Hwa-Ryun; Kim, Hyunyong; Jang, Hong Suk; Kim, Jeong Hwan; Park, Dong Wook; Jang, Sea Duk; Hwang, Won Taek; Kim, Geun-Beom; Yang, Tae-Keun
2014-05-01
The beam irradiation system for particle therapy can use a passive or an active beam irradiation method. In the case of an active beam irradiation, using a ridge filter would be appropriate to generate a spread-out Bragg peak (SOBP) through a large scanning area. For this study, a ridge filter was designed as an energy modulation device for a prototype active scanning system at MC-50 in Korea Institute of Radiological And Medical Science (KIRAMS). The ridge filter was designed to create a 10 mm of SOBP for a 45-MeV proton beam. To reduce the distal penumbra and the initial dose, [DM] determined the weighting factor for Bragg Peak by applying an in-house iteration code and the Minuit Fit package of Root. A single ridge bar shape and its corresponding thickness were obtained through 21 weighting factors. Also, a ridge filter was fabricated to cover a large scanning area (300 × 300 mm2) by Polymethyl Methacrylate (PMMA). The fabricated ridge filter was tested at the prototype active beamline of MC-50. The SOBP and the incident beam distribution were obtained by using HD-810 GaF chromatic film placed at a right triangle to the PMMA block. The depth dose profile for the SOBP can be obtained precisely by using the flat field correction and measuring the 2-dimensional distribution of the incoming beam. After the flat field correction is used, the experimental results show that the SOBP region matches with design requirement well, with 0.62% uniformity.
Selectively-informed particle swarm optimization
NASA Astrophysics Data System (ADS)
Gao, Yang; Du, Wenbo; Yan, Gang
2015-03-01
Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors.
Selectively-informed particle swarm optimization.
Gao, Yang; Du, Wenbo; Yan, Gang
2015-01-01
Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors. PMID:25787315
Particle swarm optimization incorporating simplex search and center particle for global optimization
Chen-Chien Hsu; Chun-Hwui Gao
2008-01-01
This paper proposes a hybrid approach incorporating an enhanced Nelder-Mead simplex search scheme into a particle swarm optimization (PSO) with the use of a center particle in a swarm for effectively solving multi-dimensional optimization problems. Because of the strength of PSO in performing exploration search and NM simplex search in exploitation search, in addition to the help of a center
Managing Particle Spread via Hybrid Particle Filter\\/Kernel Mean Shift Tracking
Asad Naeem; Tony P. Pridmore; Steven Mills
2007-01-01
Particle filtering provides a well-developed and widely adopted approach to visual tracking. For effective tracking in real-world environments the particle set must sample widely enough that it can represent alternative target states in areas of ambiguity. It must not, however, become diffuse, spreading across the image plane rather than clustering around the object(s) of interest. A key issue in the
Cooperative MicroParticle Swarm Optimization Konstantinos E. Parsopoulos
Parsopoulos, Konstantinos
. Comparisons with the standard Par- ticle Swarm Optimization algorithm are also reported and discussed Keywords Particle Swarm Optimization, Cooperative, MicroEvolutio- nary Algorithms, Swarm Intelligence 1Cooperative MicroParticle Swarm Optimization Konstantinos E. Parsopoulos Department of Mathematics
TEMPORAL REGISTRATION OF PARTIAL DATA USING PARTICLE FILTERING.
Nir, Guy; Tannenbaum, Allen
2011-01-01
We propose a particle filtering framework for rigid registration of a model image to a time-series of partially observed images. The method incorporates a model-based segmentation technique in order to track the pose dynamics of an underlying observed object with time. An applicable algorithm is derived by employing the proposed framework for registration of a 3D model of an anatomical structure, which was segmented from preoperative images, to consecutive axial 2D slices of a magnetic resonance imaging (MRI) scan, which are acquired intraoperatively over time. The process is fast and robust with respect to image noise and clutter, variations of illumination, and different imaging modalities. PMID:23744132
Scale-invariant visual tracking by particle filtering
NASA Astrophysics Data System (ADS)
Nakhmani, Arie; Tannenbaum, Allen
2008-10-01
Visual tracking is an important task that has received a lot of attention in recent years. Robust generic tracking tools are of major interest for applications ranging from surveillance and security to image guided surgery. In these applications, the objects of interest may be translated and scaled. We present here an algorithm that uses scaled normalized cross-correlation matching as the likelihood within the particle filtering framework. We do not need color and contour cues in our algorithm. Experimental results with constant rectangular templates show that the method is reliable for noisy and cluttered scenarios, and provides accurate and smooth trajectories in cases of target translation and scaling.
Efficient Failure Detection for Mobile Robots Using Mixed-Abstraction Particle Filters
Stachniss, Cyrill
filter or a particle filter with a simplistic odometry-based motion model, which is formally given in Section 3.3. In odometry-based models, the next system state xt is directly predicted from the odometry
On the optimal and suboptimal nonlinear filtering problem for discrete-time systems
M. L. ANDRADE NETTO; L. Gimeno; M. J. MENDES
1978-01-01
This paper examines optimal and suboptimal algorithms for the state filtering problem in discrete-time nonlinear systems. The optimal equations of sequential filtering are analyzed and conditions are obtained which ensure a multimodal character for the a posteriori densities. This analysis is utilized in the discussion of the performance of suboptimal linearized filters, and suggestions are made for their improvement in
A Hierarchical Particle Swarm Optimizer for Dynamic Optimization Problems
Stefan Janson; Martin Middendorf
2004-01-01
\\u000a Particle Swarm Optimization (PSO) methods for dynamic function optimization are studied in this paper. We compare dynamic\\u000a variants of standard PSO and Hierarchical PSO (H-PSO) on different dynamic benchmark functions. Moreover, a new type of hierarchical\\u000a PSO, called Partitioned H-PSO (PH-PSO), is proposed. In this algorithm the hierarchy is partitioned into several sub-swarms\\u000a for a limited number of generations after
The particle swarm optimization algorithm in size and shape optimization
P. C. Fourie; A. A. Groenwold
2002-01-01
. Shape and size optimization problems instructural design are addressed using the particle swarm optimization algorithm (PSOA).\\u000a In our implementation of the PSOA, the social behaviour of birds is mimicked. Individual birds exchange information about\\u000a their position, velocity and fitness, and the behaviour of the flock is then influenced to increase the probability of migration\\u000a to regions of high fitness.
Texture decomposition with particle swarm optimization method
Jian-Guo Tang; Xin-Mingm Zhang; Yun-Lai Deng; Yu-Xuan Du; Zhi-Yong Chen
2006-01-01
The newly developed optimization algorithm-particle swarm optimization (PSO) algorithm is introduced into the crystallographic texture decomposition. With the linear correlation factor as the evaluation parameter, both the PSO algorithm and the Nelder–Mead Simplex (NMS) algorithm are evaluated in this paper. The evaluation result reveals that the PSO algorithm is more effective when it comes to the complicated multi-component textures, i.e.,
Symmetric Phase-Only Filtering in Particle-Image Velocimetry
NASA Technical Reports Server (NTRS)
Wemet, Mark P.
2008-01-01
Symmetrical phase-only filtering (SPOF) can be exploited to obtain substantial improvements in the results of data processing in particle-image velocimetry (PIV). In comparison with traditional PIV data processing, SPOF PIV data processing yields narrower and larger amplitude correlation peaks, thereby providing more-accurate velocity estimates. The higher signal-to-noise ratios associated with the higher amplitude correlation peaks afford greater robustness and reliability of processing. SPOF also affords superior performance in the presence of surface flare light and/or background light. SPOF algorithms can readily be incorporated into pre-existing algorithms used to process digitized image data in PIV, without significantly increasing processing times. A summary of PIV and traditional PIV data processing is prerequisite to a meaningful description of SPOF PIV processing. In PIV, a pulsed laser is used to illuminate a substantially planar region of a flowing fluid in which particles are entrained. An electronic camera records digital images of the particles at two instants of time. The components of velocity of the fluid in the illuminated plane can be obtained by determining the displacements of particles between the two illumination pulses. The objective in PIV data processing is to compute the particle displacements from the digital image data. In traditional PIV data processing, to which the present innovation applies, the two images are divided into a grid of subregions and the displacements determined from cross-correlations between the corresponding sub-regions in the first and second images. The cross-correlation process begins with the calculation of the Fourier transforms (or fast Fourier transforms) of the subregion portions of the images. The Fourier transforms from the corresponding subregions are multiplied, and this product is inverse Fourier transformed, yielding the cross-correlation intensity distribution. The average displacement of the particles across a subregion results in a displacement of the correlation peak from the center of the correlation plane. The velocity is then computed from the displacement of the correlation peak and the time between the recording of the two images. The process as described thus far is performed for all the subregions. The resulting set of velocities in grid cells amounts to a velocity vector map of the flow field recorded on the image plane. In traditional PIV processing, surface flare light and bright background light give rise to a large, broad correlation peak, at the center of the correlation plane, that can overwhelm the true particle- displacement correlation peak. This has made it necessary to resort to tedious image-masking and background-subtraction procedures to recover the relatively small amplitude particle-displacement correlation peak. SPOF is a variant of phase-only filtering (POF), which, in turn, is a variant of matched spatial filtering (MSF). In MSF, one projects a first image (denoted the input image) onto a second image (denoted the filter) as part of a computation to determine how much and what part of the filter is present in the input image. MSF is equivalent to cross-correlation. In POF, the frequency-domain content of the MSF filter is modified to produce a unitamplitude (phase-only) object. POF is implemented by normalizing the Fourier transform of the filter by its magnitude. The advantage of POFs is that they yield correlation peaks that are sharper and have higher signal-to-noise ratios than those obtained through traditional MSF. In the SPOF, these benefits of POF can be extended to PIV data processing. The SPOF yields even better performance than the POF approach, which is uniquely applicable to PIV type image data. In SPOF as now applied to PIV data processing, a subregion of the first image is treated as the input image and the corresponding subregion of the second image is treated as the filter. The Fourier transforms from both the firs and second- image subregions are normalized by the square roots of their respective magnitudes.
Nonlinear EEG Decoding Based on a Particle Filter Model
Hong, Jun
2014-01-01
While the world is stepping into the aging society, rehabilitation robots play a more and more important role in terms of both rehabilitation treatment and nursing of the patients with neurological diseases. Benefiting from the abundant contents of movement information, electroencephalography (EEG) has become a promising information source for rehabilitation robots control. Although the multiple linear regression model was used as the decoding model of EEG signals in some researches, it has been considered that it cannot reflect the nonlinear components of EEG signals. In order to overcome this shortcoming, we propose a nonlinear decoding model, the particle filter model. Two- and three-dimensional decoding experiments were performed to test the validity of this model. In decoding accuracy, the results are comparable to those of the multiple linear regression model and previous EEG studies. In addition, the particle filter model uses less training data and more frequency information than the multiple linear regression model, which shows the potential of nonlinear decoding models. Overall, the findings hold promise for the furtherance of EEG-based rehabilitation robots. PMID:24949420
An Emotional Particle Swarm Optimization Algorithm
Yang Ge; Zhang Rubo
2005-01-01
\\u000a This paper presents a modification of the particle swarm optimization algorithm (PSO) intended to introduce some psychology\\u000a factor of emotion into the algorithm. In the new algorithm, which is based on a simple perception and emotion psychology model,\\u000a each particle has its own feeling and reaction to the current position, and it also has specified emotional factor towards\\u000a the sense
Online Selecting Discriminative Tracking Features using Particle Filter Jianyu Wang1
Chen, Xilin
Online Selecting Discriminative Tracking Features using Particle Filter Jianyu Wang1 , Xilin Chen1 into the particle filtering process with the aid of existed "background" particles. Feature values from background}@jdl.ac.cn Abstract The paper proposes a method to keep the tracker robust to background clutters by online selecting
Robust Tracking in FLIR Imagery by Mean Shift Combined with Particle Filter Algorithm
Wei Yang; Shuangyan Hu; Junshan Li; Deqin Shi
2008-01-01
A novel target tracking algorithm for forward-looking infrared image sequences is proposed based on mean shift and particle filter algorithm. The mean shift algorithm is served as an efficient gradient estimation and mode seeking procedure in the particle filter. Particles move toward the modes of the posterior kernel density estimation. The infrared target is represented in the cascade grey space
Object tracking by applying mean-shift algorithm into particle filtering
Wang Hongling; Yang Bo; Tian Guodong; Men Aidong
2009-01-01
In the pursuit of robust object tracking, both particle filter and mean-shift algorithm have proven successful approaches. Also both of them have weaknesses. The article presents the integration of mean-shift algorithm with particle filtering during the moving object tracking. In our method mean-shift algorithm is used in the sampling steps of particle filtering, which efficiently reduces the number of sampled
Visual tracking for non-rigid objects using Rao-Blackwellized particle filter
Jungho Kim; In-So Kweon
2010-01-01
Particle filters have been used for visual tracking during long periods because they enable effective estimation for non-linear and non-Gaussian distributions. However, particle filter-based tracking approaches suffer from occlusion and deformation of the target objects, which result in the large difference between the current observations and the target model. Thus, we present a Rao-Blackwellized particle filter (RBPF)-based tracking algorithm that
Real-time object tracking using color-based Kalman particle filter
Ahmed Abdel-Hadi
2010-01-01
Robust real-time tracking of non-rigid object is a challenging task. Particle filtering has proven very successful for non-linear and non-Gaussian estimation problems. In this paper, a method for real-time tracking of moving objects which is characterized by a color probability distribution is presented. We applied Kaiman particle filter (KPF) to color-based tracking. This KPF is a particle filter including the
Adaptive Particle Filter Based on Energy Field for Robust Object Tracking in Complex Scenes
Xin Sun; Hongxun Yao; Shengping Zhang; Shaohui Liu
2010-01-01
\\u000a Particle filter (PF) based object tracking methods have been widely used in computer vision. However, traditional particle\\u000a filter trackers cannot effectively distinguish the target from the background in complex scenes since they only exploit appearance\\u000a information of observation to determine the target region. In this paper, we present an adaptive particle filter based on\\u000a energy field (EPF), which makes good
A Particle Swarm Optimization Algorithm with Crossover Operator
Zhi-Feng Hao; Zhi-Gang Wang; Han Huang
2007-01-01
Particle swarm optimization (PSO) is a method for tackling optimization functions. However, it is easily trapped into the local optimization when solving high-dimension functions. To overcome this shortcoming, a modified particle swarm optimization is proposed in this paper. In the proposed method, a crossover step is added to the standard PSO. The crossover is taken between each particle's individual best
An Adaptive Learning Particle Swarm Optimizer for Function Optimization
Yang, Shengxiang
Yang Abstract-- Traditional particle swarm optimization (PSO) suffers from the premature convergence problem, which usually results in PSO being trapped in local optima. This paper presents an adaptive learning PSO (ALPSO) based on a variant PSO learning strategy. In ALPSO, the learning mechanism of each
Numerical simulation of DPF filter for selected regimes with deposited soot particles
NASA Astrophysics Data System (ADS)
Lávi?ka, David; Kova?ík, Petr
2012-04-01
For the purpose of accumulation of particulate matter from Diesel engine exhaust gas, particle filters are used (referred to as DPF or FAP filters in the automotive industry). However, the cost of these filters is quite high. As the emission limits become stricter, the requirements for PM collection are rising accordingly. Particulate matters are very dangerous for human health and these are not invisible for human eye. They can often cause various diseases of the respiratory tract, even what can cause lung cancer. Performed numerical simulations were used to analyze particle filter behavior under various operating modes. The simulations were especially focused on selected critical states of particle filter, when engine is switched to emergency regime. The aim was to prevent and avoid critical situations due the filter behavior understanding. The numerical simulations were based on experimental analysis of used diesel particle filters.
An evolutionary game based particle swarm optimization algorithm
Wei-Bing Liu; Xian-Jia Wang
2008-01-01
Particle swarm optimization (PSO) is an evolutionary algorithm used extensively. This paper presented a new particle swarm optimizer based on evolutionary game (EGPSO). We map particles’ finding optimal solution in PSO algorithm to players’ pursuing maximum utility by choosing strategies in evolutionary games, using replicator dynamics to model the behavior of particles. And in order to overcome premature convergence a
Parameter Identification Problem Using Particle Swarm Optimization
An Liu; Erwie Zahara
2009-01-01
Ordinary differential equations have been a useful tool for describing the behavior of wide variety of dynamic physical systems. In this study, a method for solving parameter identification problem for ordinary second order differential equations using particle swarm optimization approach is presented.Experiments using two case problems are presented and compared with the best known solutions reported in the literature. The
A modified particle swarm optimization algorithm
Qian-Li Zhang; Xing Li; Quang-Ahn Tran
2005-01-01
A modified particle swarm optimization (PSO) algorithm is proposed in this paper to avoid premature convergence with the introduction of mutation operation. The performance of this algorithm is compared to the standard PSO algorithm and experiments indicate that it has better performance with little overhead.
Optimal filter design subject to output delobe constraints
NASA Technical Reports Server (NTRS)
Fortmann, T. E.; Athans, M.
1972-01-01
The design of filters for detection and estimation in radar and communications systems is considered, with inequality constraints on the maximum output sidelobe levels. A constrained optimization problem in Hilbert space is formulated, incorporating the sidelobe constraints via a partial ordering of continuous functions. Generalized versions (in Hilbert space) of the Kuhn-Tucker and Duality Theorems allow the reduction of this problem to an unconstrained one in the dual space of regular Borel measures. A convergent algorithm is presented for computational solution of the dual problem.
Electrostatic Respirator Filter Media: Filter Efficiency and Most Penetrating Particle Size Effects
Ernest S. Moyer
2000-01-01
New electrostatic filter media has been developed for use in 42 CFR 84 negative pressure particulate respirator filters. This respirator filter media was not available for evaluation prior to the change from 30 CFR 11 to 42 CFR 84. Thus, characterization of this filter media is warranted. In this study, the new 42 CFR 84 electrostatic respirator filters were investigated
Particle and substrate losses from Teflon® and quartz filters
NASA Astrophysics Data System (ADS)
Highsmith, V. Ross; Bond, Andrew E.; Howes, James E.
Teflon® and quartz particulate matter samples were collected at two special study sites. In order to estimate changes in mass corresponding to the various filter handling operations (for TSP and PM 10 samples), changes in both controlled and shipped filter weights were monitored at predetermined stages of the filter handling process. The study data indicate that folding the quartz filters did not significantly change the filter mass even if the filter broke along the seam. A weight loss was noted following shipment of the TSP quartz filters but not the corresponding PM 10 quartz filters. Volatilization losses were also noted with the high volume samples collected at one city. Based on mass determinations, the quartz filters did not appear to be affected by passive artifact formation. No significant weight change occurred in the routine handling of the study dichotomous Teflon® filters. The results indicate that quartz and Teflon® filter media can be used in future particulate monitoring programs.
Particle Swarm Optimization and Fitness Sharing to solve Multi-Objective Optimization Problems
Coello, Carlos A. Coello
.e.rowe@cs.bham.ac.uk Abstract- The particle swarm optimization algorithm has been shown to be a competitive heuristic to solve by that specific particle. Particle swarm optimization shares many similarities with genetic algorithms andParticle Swarm Optimization and Fitness Sharing to solve Multi-Objective Optimization Problems
Optimally stabilized PET image denoising using trilateral filtering.
Mansoor, Awais; Bagci, Ulas; Mollura, Daniel J
2014-01-01
Low-resolution and signal-dependent noise distribution in positron emission tomography (PET) images makes denoising process an inevitable step prior to qualitative and quantitative image analysis tasks. Conventional PET denoising methods either over-smooth small-sized structures due to resolution limitation or make incorrect assumptions about the noise characteristics. Therefore, clinically important quantitative information may be corrupted. To address these challenges, we introduced a novel approach to remove signal-dependent noise in the PET images where the noise distribution was considered as Poisson-Gaussian mixed. Meanwhile, the generalized Anscombe's transformation (GAT) was used to stabilize varying nature of the PET noise. Other than noise stabilization, it is also desirable for the noise removal filter to preserve the boundaries of the structures while smoothing the noisy regions. Indeed, it is important to avoid significant loss of quantitative information such as standard uptake value (SUV)-based metrics as well as metabolic lesion volume. To satisfy all these properties, we extended bilateral filtering method into trilateral filtering through multiscaling and optimal Gaussianization process. The proposed method was tested on more than 50 PET-CT images from various patients having different cancers and achieved the superior performance compared to the widely used denoising techniques in the literature. PMID:25333110
Solving constrained optimization problems with hybrid particle swarm optimization
NASA Astrophysics Data System (ADS)
Zahara, Erwie; Hu, Chia-Hsin
2008-11-01
Constrained optimization problems (COPs) are very important in that they frequently appear in the real world. A COP, in which both the function and constraints may be nonlinear, consists of the optimization of a function subject to constraints. Constraint handling is one of the major concerns when solving COPs with particle swarm optimization (PSO) combined with the Nelder-Mead simplex search method (NM-PSO). This article proposes embedded constraint handling methods, which include the gradient repair method and constraint fitness priority-based ranking method, as a special operator in NM-PSO for dealing with constraints. Experiments using 13 benchmark problems are explained and the NM-PSO results are compared with the best known solutions reported in the literature. Comparison with three different meta-heuristics demonstrates that NM-PSO with the embedded constraint operator is extremely effective and efficient at locating optimal solutions.
Sequential approach to multisensor resource management using particle filters
NASA Astrophysics Data System (ADS)
Penny, Dawn E.; Williams, Mark
2000-07-01
Elements from data fusion, optimisation and particle filtering are brought together to form the Multi-Sensor Fusion Management (MSFM) algorithm. The algorithm provides a framework for combining the information from multiple sensors and producing good solutions to the problem of how best to deploy/use these and/or other sensors to optimise some criteria in the future. A problem from Anti-Submarine Warfare (ASW) is taken as an example of the potential use of the algorithm. The algorithm is shown to make efficient use of a limited supply of passive sonobuoys in order to locate a submarine to the required accuracy. The results show that in the simulation the traditional strategies for sonobuoy deployment required approximately four times as many sonobuoys as the MSFM algorithm to achieve the required localisation.
Geoacoustic and source tracking using particle filtering: experimental results.
Yardim, Caglar; Gerstoft, Peter; Hodgkiss, William S
2010-07-01
A particle filtering (PF) approach is presented for performing sequential geoacoustic inversion of a complex ocean acoustic environment using a moving acoustic source. This approach treats both the environmental parameters [e.g., water column sound speed profile (SSP), water depth, sediment and bottom parameters] at the source location and the source parameters (e.g., source depth, range and speed) as unknown random variables that evolve as the source moves. This allows real-time updating of the environment and accurate tracking of the moving source. As a sequential Monte Carlo technique that operates on nonlinear systems with non-Gaussian probability densities, the PF is an ideal algorithm to perform tracking of environmental and source parameters, and their uncertainties via the evolving posterior probability densities. The approach is demonstrated on both simulated data in a shallow water environment with a sloping bottom and experimental data collected during the SWellEx-96 experiment. PMID:20649203
Ensemble neural network-based particle filtering for prognostics
NASA Astrophysics Data System (ADS)
Baraldi, P.; Compare, M.; Sauco, S.; Zio, E.
2013-12-01
Particle Filtering (PF) is used in prognostics applications by reason of its capability of robustly predicting the future behavior of an equipment and, on this basis, its Residual Useful Life (RUL). It is a model-driven approach, as it resorts to analytical models of both the degradation process and the measurement acquisition system. This prevents its applicability to the cases, very common in industry, in which reliable models are lacking. In this work, we propose an original method to extend PF to the case in which an analytical measurement model is not available whereas, instead, a dataset containing pairs «state-measurement» is available. The dataset is used to train a bagged ensemble of Artificial Neural Networks (ANNs) which is, then, embedded in the PF as empirical measurement model.
Robust Tracking Using Particle Filter with a Hybrid Feature
NASA Astrophysics Data System (ADS)
Zhao, Xinyue; Satoh, Yutaka; Takauji, Hidenori; Kaneko, Shun'ichi
This paper presents a novel method for robust object tracking in video sequences using a hybrid feature-based observation model in a particle filtering framework. An ideal observation model should have both high ability to accurately distinguish objects from the background and high reliability to identify the detected objects. Traditional features are better at solving the former problem but weak in solving the latter one. To overcome that, we adopt a robust and dynamic feature called Grayscale Arranging Pairs (GAP), which has high discriminative ability even under conditions of severe illumination variation and dynamic background elements. Together with the GAP feature, we also adopt the color histogram feature in order to take advantage of traditional features in resolving the first problem. At the same time, an efficient and simple integration method is used to combine the GAP feature with color information. Comparative experiments demonstrate that object tracking with our integrated features performs well even when objects go across complex backgrounds.
Object tracking with particle filter in UAV video
NASA Astrophysics Data System (ADS)
Yu, Wenshuai; Yin, Xiaodong; Chen, Bing; Xie, Jinhua
2013-10-01
Aerial surveillance is a main functionality of UAV, which is realized via video camera. During the operations, the mission assigned targets always are the kinetic objects, such as people or vehicles. Therefore, object tracking is taken as the key techniques for UAV sensor payload. Two difficulties for UAV object tracking are dynamic background and hardly predicting target's motion. To solve the problems, it employed the particle filter in the research. Modeling the target by its characteristics, for instance, color features, it approximates the possibility density of target state with weighting sample sets, and the state vector contains position, motion vector and region parameters. The experiments demonstrate the effectiveness and robustness of the proposed method in UAV video tracking.
A Hybrid Particle-Ensemble Kalman Filter for Lagrangian Data Assimilation
Sandstede, BjÃ¶rn
experiments on the linear shallow water equations. In these experiments, the hybrid filter consistentlyA Hybrid Particle-Ensemble Kalman Filter for Lagrangian Data Assimilation Laura Slivinski* , Elaine with widely- used data assimilation algorithms such as the ensemble Kalman filter (EnKF). Additionally
Kuldeep, B; Singh, V K; Kumar, A; Singh, G K
2015-01-01
In this article, a novel approach for 2-channel linear phase quadrature mirror filter (QMF) bank design based on a hybrid of gradient based optimization and optimization of fractional derivative constraints is introduced. For the purpose of this work, recently proposed nature inspired optimization techniques such as cuckoo search (CS), modified cuckoo search (MCS) and wind driven optimization (WDO) are explored for the design of QMF bank. 2-Channel QMF is also designed with particle swarm optimization (PSO) and artificial bee colony (ABC) nature inspired optimization techniques. The design problem is formulated in frequency domain as sum of L2 norm of error in passband, stopband and transition band at quadrature frequency. The contribution of this work is the novel hybrid combination of gradient based optimization (Lagrange multiplier method) and nature inspired optimization (CS, MCS, WDO, PSO and ABC) and its usage for optimizing the design problem. Performance of the proposed method is evaluated by passband error (?p), stopband error (?s), transition band error (?t), peak reconstruction error (PRE), stopband attenuation (As) and computational time. The design examples illustrate the ingenuity of the proposed method. Results are also compared with the other existing algorithms, and it was found that the proposed method gives best result in terms of peak reconstruction error and transition band error while it is comparable in terms of passband and stopband error. Results show that the proposed method is successful for both lower and higher order 2-channel QMF bank design. A comparative study of various nature inspired optimization techniques is also presented, and the study singles out CS as a best QMF optimization technique. PMID:25034647
Particle initialization effect of particle filter based track-before-detect algorithm
Na Wang; Guo-hong Wang; Hong-bo Yu; Dan Sheng
2011-01-01
The problem of detecting and tracking weak, stealthy or dim targets is a challenging one. The particle filter based track before detect (TBD) algorithm is appealing for its generality and potential for solving non-linear non-Gaussian problem. However, real-time implementation can be hardly realized by this class of algorithm. Current state-of-the-art research ubiquitously deal with computationally intensive problem by improving the
Multi-strategy coevolving aging particle optimization.
Iacca, Giovanni; Caraffini, Fabio; Neri, Ferrante
2014-02-01
We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization. In a memetic fashion, MS-CAP combines two components with complementary algorithm logics. In the first stage, each particle is perturbed independently along each dimension with a progressively shrinking (decaying) radius, and attracted towards the current best solution with an increasing force. In the second phase, the particles are mutated and recombined according to a multi-strategy approach in the fashion of the ensemble of mutation strategies in Differential Evolution. The proposed algorithm is tested, at different dimensionalities, on two complete black-box optimization benchmarks proposed at the Congress on Evolutionary Computation 2010 and 2013. To demonstrate the applicability of the approach, we also test MS-CAP to train a Feedforward Neural Network modeling the kinematics of an 8-link robot manipulator. The numerical results show that MS-CAP, for the setting considered in this study, tends to outperform the state-of-the-art optimization algorithms on a large set of problems, thus resulting in a robust and versatile optimizer. PMID:24344695
NASA Astrophysics Data System (ADS)
Moradkhani, H.; De Chant, C. M.
2012-04-01
Ensemble based hydrologic data assimilation has the potential to accurately quantify the uncertainty in streamflow predictions by accounting for many sources of uncertainty within the modeling framework. The four main sources of error, input data, observation, model structure and parameter, can all be addressed directly with data assimilation. Due to recent improvements in parameter estimation using data assimilation, state-parameter estimation techniques have become a popular topic in the hydrologic data assimilation community. Several studies, using both the Ensemble Kalman Filter (EnKF) and the Particle Filter (PF) to estimate both model states and parameters have been published in recent years. Though there is increasing interest and a growing literature in this area, relatively little research has been presented to examine the effectiveness and robustness of these methods to estimate uncertainty. This study highlights the need for state-parameter estimation studies to provide a more rigorous testing of these techniques with respect to uncertainty quantification. Using multiple calibration and validation replicates, a detailed analysis of the robustness of the methods is performed. The results from this study show the complexity of information from both the EnKF and the PF, and explain aspects of these techniques that have not been well documented in the current scientific literature.
Pseudoelectret filter for micrometer-sized particles in exhaust gases at 210°C
Ion I. Inculet; G. S. Peter Castle; Mircea Slanina; Mihai Duca
2002-01-01
The pseudoelectret fibers developed at the Applied Electrostatics Research Centre, University of Western Ontario, London, ON, Canada, have been used to build an unlimited-life high-efficiency filter for micron-sized particles entrained in up to 300°C hot exhaust gas. This pseudoelectret filter has considerable advantages when compared to mechanical or conventional electret-type filters. In a comparable unblinded mechanical filter, the pressure drop
Combining Colour and Orientation for Adaptive Particle Filter-based Tracking
Emilio Maggio; Fabrizio Smeraldi; Andrea Cavallaro
We propose an accurate tracking algorithm based on a multi-feature statistical model. The model combines in a single particle filter colour and gradient-based orientation informa- tion. A reliability measure derived from the particle distribution is used to adaptively weigh the contribution of the two features. Furthermore, information from the tracker is used to set the dimension of the filters for
Adapting the Sample Size in Particle Filters Through KLD-Sampling
Dieter Fox
2003-01-01
Over the last years, particle filters have been applied with great success to a variety of state estimation problems. In this paper we present a statistical approach to increasing the efficiency of particle filters by adapting the size of sample sets during the estimation pro- cess. The key idea of the KLD-sampling method is to bound the approximation error intro-
A Particle Filter for Monocular Vision-Aided Odometry Teddy Yap, Jr
Shelton, Christian R.
A Particle Filter for Monocular Vision-Aided Odometry Teddy Yap, Jr Washington State University-- We propose a particle filter-based algorithm for monocular vision-aided odometry for mobile robot localization. The algorithm fuses information from odometry with observa- tions of naturally occurring static
GATE: A Novel Robust Object Tracking Method Using the Particle Filtering and Level Set Method
Cheng Luo; Xiongcai Cai; Jian Zhang
2008-01-01
This paper presents a novel algorithm for robust object tracking based on the particle filtering method employed in recursive Bayesian estimation and image segmentation and optimisation techniques employed in active contour models and level set methods. The proposed Geometric Active contour-based Tracking Estimator, namely GATE, enables particle filters to track object of interest in complex environments using merely a simple
Hierarchical Kalman-particle filter with adaptation to motion changes for object tracking
Shimin Yin; Jin Hee Na; Jin Young Choi; Songhwai Oh
2011-01-01
We present a new tracking method with improved efficiency and accuracy based on the subspace representation and particle filter. The subspace representation has been successfully adopted in tracking, e.g., the Eigen-tracking algorithm, and it has shown considerable robustness for tracking an object with changing appearance. Particle filters are widely used for a wide range of tracking problems since they can
A NEW IMPORTANCE SAMPLING SCHEME BASED ON MOTION SEGMENTATION IN PARTICLE FILTERING
E. BICHOT; L. MASCARILLA; P. COURTELLEMONT
In this paper, we exploit motion segmentation to enhance the robustness of a particle filtering based tracking process. We first propagate hypotheses from particle filtering to blobs of similar motion to target to achieve a more accurate prediction of regions of interest in the state space. This makes a new importance sampling scheme. After having identified the moving target, a
Supervised particle filter for tracking 2D human pose in monocular video
Suman Sedai; Du Q. Huynh; Mohammed Bennamoun
2011-01-01
In this paper, we propose a hybrid method that combines supervised learning and particle filtering to track the 2D pose of a human subject in monocular video sequences. Our approach, which we call a supervised particle filter method, consists of two steps: the training step and the tracking step. In the training step, we use a supervised learning method to
Shane Butler; John Ringwood
2010-01-01
Prognostics is the ability to predict the remaining useful life of a specific system, or component, and represents a key enabler of any effective condition-based-maintenance strategy. Among methods for performing prognostics such as regression and artificial neural networks, particle filters are emerging as a technique with considerable potential. Particle filters employ both a state dynamic model and a measurement model,
Coupling particle filters with automatic speech recognition for speech feature enhancement
Friedrich Faubel; Matthias Wölfel
2006-01-01
This paper addresses robust speech feature extraction in combina- tion with statistical speech feature enhancement and couples the particle filter to the speech recognition hypotheses. To extract noise robust features the Fourier transformation is replaced by the warped and scaled minimum variance distortion- less response spectral envelope. To enhance the features, particle filtering has been used. Further, we show that
Hybrid Particle Filter and Mean Shift tracker with adaptive transition model
Emilio Maggio; Andrea Cavallaro
2005-01-01
We propose a tracking algorithm based on a combination of Particle Filter and Mean Shift, and enhanced with a new adaptive state transition model. Particle Filter is robust to partial and total occlusions, can deal with multi-modal pdf s and can recover lost tracks. However, its complexity dramatically increases with the dimensionality of the sampled pdf. Mean Shift has a
Color-Based Fingertip Tracking Using Modified Dynamic Model Particle Filtering Method
Del Moral , Pierre
Color-Based Fingertip Tracking Using Modified Dynamic Model Particle Filtering Method Thesis for the particle filtering algorithm based on the analysis of natural motion of human fingertip movement. Our high to be robust for a particular application usually resulting in a trade-off between robustness and efficiency
Robust object tracking using the particle filtering and level set methods: A comparative experiment
Cheng Luo; Xiongcai Cai; Jian Zhang
2008-01-01
Robust visual tracking has become an important topic of research in computer vision. A novel method for robust object tracking, GATE [11], improves object tracking in complex environments using the particle filtering and the level set-based active contour method. GATE creates a spatial prior in the state space using shape information of the tracked object to filter particles in the
Improving particle filter with support vector regression for efficient visual tracking
Guangyu Zhu; Dawei Liang; Yang Liu; Qingming Huang; Wen Gao
2005-01-01
Particle filter is a powerful visual tracking tool based on sequential Monte Carlo framework, and it needs large numbers of samples to properly approximate the posterior density of the state evolution. However, its efficiency will degenerate if too many samples are applied. In this paper, an improved particle filter is proposed by integrating support vector regression into sequential Monte Carlo
Target tracking with mobile sensors using cost-reference particle filtering
Yao Li; Petar M. Djuric
2008-01-01
Sequential Monte Carlo (SMC) methods, also referred to as particle filters, have been successfully applied to a variety of highly nonlinear problems such as target tracking with sensor networks. In this paper, we propose the application of a new class of SMC methods named cost-reference particle filters (CRPFs) to target tracking with mobile sensors. CRPF techniques have been shown to
Robust Head Tracking Based on a Multi-State Particle Filter , Haizhou AI1
Ai, Haizhou
Robust Head Tracking Based on a Multi-State Particle Filter Yuan LI1 , Haizhou AI1 , Chang HUANG1-known probabilistic tracking framework particle filter. For the face part, a vector-boosted multi-view face detector@mail.tsinghua.edu.cn Abstract This paper proposes a novel method for robust and auto- matic realtime head tracking by fusing
Roozbeh Mottaghi; Richard T. Vaughan
2007-01-01
We describe a novel method whereby a particle filter is used to create a potential field for robot control without prior clustering. We show an application of this technique to control a team of mobile robots to cooperatively locate and track a moving target. The particle filter models a probability distribution over the estimated location of the target, providing robust
Robust Vision based Lane Tracking using Multiple Cues and Particle Filtering 1
Apostoloff, Nicholas
Robust Vision based Lane Tracking using Multiple Cues and Particle Filtering 1 Nicholas Apostoloff tracking system running at 15Hz will be discussed, focusing on the particle filter and cue fusion system. This paper presents the development and application of a novel multiple-cue visual lane track
Xiaohong Sheng; Yu Hen Hu; Parameswaran Ramanathan
2005-01-01
Two novel distributed particle filters with Gaussian mixer approximation are proposed to localize and track multiple moving targets in a wireless sensor network. The distributed particle filters run on a set of uncorrelated sensor cliques that are dynamically organized based on moving target trajectories. These two algorithms differ in how the distributive computing is performed. In the first algorithm, partial
A Marginalized Particle Filter approach to an integrated INS\\/TAP system
T. Hektor; H. Karlsson; P.-J. Nordlund
2008-01-01
Accurate and reliable navigation systems will become increasingly important in future aircraft applications, in particular within unmanned aerial vehicle systems. This paper describes a particle filter approach of integrating an Inertial navigation system (INS) with a terrain-aided positioning system (TAP) to achieve such a system. The integrated system is realized applying a marginalized particle filter (MPF) where the highly nonlinear
Efficient Particle Filter-Based Tracking of Multiple Interacting Targets Using an MRF-based
Dellaert, Frank
Efficient Particle Filter-Based Tracking of Multiple Interacting Targets Using an MRF-based Motion hypothesis particle filter for tracking targets that will be influenced by the proximity and/or behavior for a social insect tracking application, where we model the domain knowledge that two targets cannot occupy
Joint tracking algorithm using particle filter and mean shift with target model updating
NASA Astrophysics Data System (ADS)
Zhang, Bo; Tian, Weifeng; Jin, Zhihua
2006-10-01
Roughly, visual tracking algorithms can be divided into two main classes: deterministic tracking and stochastic tracking. Mean shift and particle filter are their typical representatives, respectively. Recently, a hybrid tracker, seamlessly integrating the respective advantages of mean shift and particle filter (MSPF) has achieved impressive success in robust tracking. The pivot of MSPF is to sample fewer particles using particle filter and then those particles are shifted to their respective local maximum of target searching space by mean shift. MSPF not only can greatly reduce the number of particles that particle filter required, but can remedy the deficiency of mean shift. Unfortunately, due to its inherent principle, MSPF is restricted to those applications with little changes of the target model. To make MSPF more flexible and robust, an adaptive target model is extended to MSPF in this paper. Experimental results show that MSPF with target model updating can robustly track the target through the whole sequences regardless of the change of target model.
GREEN-PSO: Conserving Function Evaluations in Particle Swarm Optimization
Majercik, Stephen Michael
GREEN-PSO: Conserving Function Evaluations in Particle Swarm Optimization Stephen M. Majercik1 1: particle swarm optimization; swarm intelligence. Abstract: In the Particle Swarm Optimization (PSO the PSO algorithm more widely. Many function approximation techniques have been developed that address
Optimal design of wide band low loss SAW filters, using slanted interdigital transducers
S. M. Balashov; K. H. Baek
2000-01-01
Slanted finger SAW transducers allow one to design wide band filters with excellent characteristics. One of the most interesting modifications of such a filter is the slanted SPUDT. We present an approach to the analysis of such filters based on consistent use of the Y-matrix instead of the conventional P-matrix. Conditions of optimal matching of such SPUDT are obtained and
Synthesizing Optimal Filters for Crosstalk-cancellation for High-Speed Buses
Greenstreet, Mark
Synthesizing Optimal Filters for Crosstalk-cancellation for High-Speed Buses Jihong Ren and Mark in crosstalk cancellation for high-bandwidth, digital commu- nication. In practice, filter design the structure of a typical channel with a pre-equalization filter for crosstalk cancellation
Vorobeychik, Eugene
Optimal Personalized Filtering Against Spear-Phishing Attacks Aron Laszka and Yevgeniy Vorobeychik, attackers can use spear phishing to sidestep technical security mech- anisms by exploiting the privileges-user filtering thresholds for mitigating spear-phishing attacks. We formulate the problem of filtering targeted
Sun, Lei; Jia, Yun-xian; Cai, Li-ying; Lin, Guo-yu; Zhao, Jin-song
2013-09-01
The spectrometric oil analysis(SOA) is an important technique for machine state monitoring, fault diagnosis and prognosis, and SOA based remaining useful life(RUL) prediction has an advantage of finding out the optimal maintenance strategy for machine system. Because the complexity of machine system, its health state degradation process can't be simply characterized by linear model, while particle filtering(PF) possesses obvious advantages over traditional Kalman filtering for dealing nonlinear and non-Gaussian system, the PF approach was applied to state forecasting by SOA, and the RUL prediction technique based on SOA and PF algorithm is proposed. In the prediction model, according to the estimating result of system's posterior probability, its prior probability distribution is realized, and the multi-step ahead prediction model based on PF algorithm is established. Finally, the practical SOA data of some engine was analyzed and forecasted by the above method, and the forecasting result was compared with that of traditional Kalman filtering method. The result fully shows the superiority and effectivity of the PMID:24369656
Cantilever-based micro-particle filter with simultaneous single particle detection
NASA Astrophysics Data System (ADS)
Noeth, N.; Keller, S. S.; Boisen, A.
2011-05-01
Currently, separation of whole blood samples on lab-on-a-chip systems is achieved via filters followed by analysis of the filtered matter such as counting of blood cells. Here, a micro-chip based on cantilever technology is developed, which enables simultaneous filtration and counting of micro-particles from a liquid. A hole-array is integrated into a micro-cantilever, which is inserted into a microfluidic channel perpendicular to the flow. A metal pad at the apex of the cantilever enables an optical read-out of the deflection of the cantilever. When a micro-particle is too large to pass a hole in the cantilever, clogging of the holes increases the flow resistance of the cantilever. This causes a bending of the device, which can be detected by the optical read-out system. By arranging an array of such cantilevers with different hole sizes, separation by size can be achieved. In this paper a proof of concept of the device is demonstrated by filtering and counting 20 µm polystyrene beads dispersed in an aqueous solution.
A new immune particle filter algorithm for tracking a moving target
Hua Han; Yongsheng Ding; Kuangrong Hao
2010-01-01
In this paper, we first analyze the performance of standard particle filter algorithm, which mainly focuses on the sample impoverishment brought by re-sampling to resolve degeneracy phenomenon. In order to increase the diversity of particles and the number of meaningful particles, we consider the basic immune clonal selection algorithm and memory mechanism. We introduce artificial immune algorithm into particle re-sampling
Jeroen Lichtenauer; Marcel J. T. Reinders; Emile A. Hendriks
2004-01-01
Since the introduction of particle filtering for object tracking, a lot of improvements have been suggested. However, the definition of the observation likelihood function, needed for determining the particle weights, has received little attention. Because particle weights determine how the particles are re-sampled, the likelihood function has a strong influence on the tracking performance. We show experimental results for three
Kneissler, Jan; Drugowitsch, Jan; Friston, Karl; Butz, Martin V.
2015-01-01
Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF). PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than 10-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares. PMID:25983690
Optimal Filtering in Mass Transport Modeling From Satellite Gravimetry Data
NASA Astrophysics Data System (ADS)
Ditmar, P.; Hashemi Farahani, H.; Klees, R.
2011-12-01
Monitoring natural mass transport in the Earth's system, which has marked a new era in Earth observation, is largely based on the data collected by the GRACE satellite mission. Unfortunately, this mission is not free from certain limitations, two of which are especially critical. Firstly, its sensitivity is strongly anisotropic: it senses the north-south component of the mass re-distribution gradient much better than the east-west component. Secondly, it suffers from a trade-off between temporal and spatial resolution: a high (e.g., daily) temporal resolution is only possible if the spatial resolution is sacrificed. To make things even worse, the GRACE satellites enter occasionally a phase when their orbit is characterized by a short repeat period, which makes it impossible to reach a high spatial resolution at all. A way to mitigate limitations of GRACE measurements is to design optimal data processing procedures, so that all available information is fully exploited when modeling mass transport. This implies, in particular, that an unconstrained model directly derived from satellite gravimetry data needs to be optimally filtered. In principle, this can be realized with a Wiener filter, which is built on the basis of covariance matrices of noise and signal. In practice, however, a compilation of both matrices (and, therefore, of the filter itself) is not a trivial task. To build the covariance matrix of noise in a mass transport model, it is necessary to start from a realistic model of noise in the level-1B data. Furthermore, a routine satellite gravimetry data processing includes, in particular, the subtraction of nuisance signals (for instance, associated with atmosphere and ocean), for which appropriate background models are used. Such models are not error-free, which has to be taken into account when the noise covariance matrix is constructed. In addition, both signal and noise covariance matrices depend on the type of mass transport processes under investigation. For instance, processes of hydrological origin occur at short time scales, so that the input time series is typically short (1 month or less), which implies a relatively strong noise in the derived model. On the contrary, study of a long-term ice mass depletion requires a long time series of satellite data, which leads to a reduction of noise in the mass transport model. Of course, the spatial pattern (and therefore, the signal covariance matrices) of various mass transport processes are also very different. In the presented study, we compare various strategies to build the signal and noise covariance matrices in the context of mass transport modeling. In this way, we demonstrate the benefits of an accurate construction of an optimal filter as outlined above, compared to simplified strategies. Furthermore, we consider both models based on GRACE data alone and combined GRACE/GOCE models. In this way, we shed more light on a potential synergy of the GRACE and GOCE satellite mission. This is important nor only for the best possible mass transport modeling on the basis of all available data, but also for the optimal planning of future satellite gravity missions.
Wide-Field, Motion-Sensitive Neurons and Optimal Matched Filters for Optic Flow
Matthias O. Franz; Holger G. Krapp
1998-01-01
. We present a theory for the construction of an optimal matched filter for self-motion inducedoptic flow fields. The matched filter extracts local flow components along a set of pre-defined directionsand weights them according to an optimization principle which minimizes the difference between estimatedand real egomotion parameters. In contrast to previous approaches, prior knowledge about distance andtranslation statistics is incorporated
Optimizing gain and noise performance of EDFAs with insertion of a filter or an isolator
Jorn Hedegaard Povlsen; Anders Bjarklev; Ole Lumholt; Helle Vendeltorp-Pommer; Karsten Rottwitt; Thomas P. Rasmussen
1992-01-01
Filters and isolators placed within EDFAs can be used to increase the gain and decrease the noise figure. By use of an accurate model the placement of the filters and isolators is optimized. The optimization is performed for situations with pump lasers emitting at 0.980 micrometers and 1.480 micrometers , and signal wavelengths at either the emission cross section peak
Particle filtering methods for georeferencing panoramic image sequence in complex urban scenes
NASA Astrophysics Data System (ADS)
Ji, Shunping; Shi, Yun; Shan, Jie; Shao, Xiaowei; Shi, Zhongchao; Yuan, Xiuxiao; Yang, Peng; Wu, Wenbin; Tang, Huajun; Shibasaki, Ryosuke
2015-07-01
Georeferencing image sequences is critical for mobile mapping systems. Traditional methods such as bundle adjustment need adequate and well-distributed ground control points (GCP) when accurate GPS data are not available in complex urban scenes. For applications of large areas, automatic extraction of GCPs by matching vehicle-born image sequences with geo-referenced ortho-images will be a better choice than intensive GCP collection with field surveying. However, such image matching generated GCP's are highly noisy, especially in complex urban street environments due to shadows, occlusions and moving objects in the ortho images. This study presents a probabilistic solution that integrates matching and localization under one framework. First, a probabilistic and global localization model is formulated based on the Bayes' rules and Markov chain. Unlike many conventional methods, our model can accommodate non-Gaussian observation. In the next step, a particle filtering method is applied to determine this model under highly noisy GCP's. Owing to the multiple hypotheses tracking represented by diverse particles, the method can balance the strength of geometric and radiometric constraints, i.e., drifted motion models and noisy GCP's, and guarantee an approximately optimal trajectory. Carried out tests are with thousands of mobile panoramic images and aerial ortho-images. Comparing with the conventional extended Kalman filtering and a global registration method, the proposed approach can succeed even under more than 80% gross errors in GCP's and reach a good accuracy equivalent to the traditional bundle adjustment with dense and precise control.
ASME AG-1 Section FC Qualified HEPA Filters; a Particle Loading Comparison - 13435
Stillo, Andrew [Camfil Farr, 1 North Corporate Drive, Riverdale, NJ 07457 (United States)] [Camfil Farr, 1 North Corporate Drive, Riverdale, NJ 07457 (United States); Ricketts, Craig I. [New Mexico State University, Department of Engineering Technology and Surveying Engineering, P.O. Box 30001 MSC 3566, Las Cruces, NM 88003-8001 (United States)] [New Mexico State University, Department of Engineering Technology and Surveying Engineering, P.O. Box 30001 MSC 3566, Las Cruces, NM 88003-8001 (United States)
2013-07-01
High Efficiency Particulate Air (HEPA) Filters used to protect personnel, the public and the environment from airborne radioactive materials are designed, manufactured and qualified in accordance with ASME AG-1 Code section FC (HEPA Filters) [1]. The qualification process requires that filters manufactured in accordance with this ASME AG-1 code section must meet several performance requirements. These requirements include performance specifications for resistance to airflow, aerosol penetration, resistance to rough handling, resistance to pressure (includes high humidity and water droplet exposure), resistance to heated air, spot flame resistance and a visual/dimensional inspection. None of these requirements evaluate the particle loading capacity of a HEPA filter design. Concerns, over the particle loading capacity, of the different designs included within the ASME AG-1 section FC code[1], have been voiced in the recent past. Additionally, the ability of a filter to maintain its integrity, if subjected to severe operating conditions such as elevated relative humidity, fog conditions or elevated temperature, after loading in use over long service intervals is also a major concern. Although currently qualified HEPA filter media are likely to have similar loading characteristics when evaluated independently, filter pleat geometry can have a significant impact on the in-situ particle loading capacity of filter packs. Aerosol particle characteristics, such as size and composition, may also have a significant impact on filter loading capacity. Test results comparing filter loading capacities for three different aerosol particles and three different filter pack configurations are reviewed. The information presented represents an empirical performance comparison among the filter designs tested. The results may serve as a basis for further discussion toward the possible development of a particle loading test to be included in the qualification requirements of ASME AG-1 Code sections FC and FK[1]. (authors)
A parallel histogram-based particle filter for object tracking on SIMD-based smart cameras
Henry Medeiros; Germán Holguín; Paul J. Shin; Johnny Park
2010-01-01
We present a parallel implementation of a histogram-based particle filter for object tracking on smart cameras based on SIMD processors. We specifically focus on parallel computation of the particle weights and parallel construction of the feature histograms since these are the major bottlenecks in standard implementations of histogram-based particle filters. The proposed algorithm can be applied with any histogram-based feature
Hua Han; Yong-Sheng Ding; Kuang-Rong Hao; Xiao Liang
2011-01-01
Particle filter algorithm is widely used for target tracking using video sequences, which is of great importance for intelligent surveillance applications. However, there is still much room for improvement, e.g. the so-called “sample impoverishment”. It is brought by re-sampling which aims to avoid particle degradation, and thus becomes the inherent shortcoming of the particle filter. In order to solve the problem
Memory-based Particle Filter for face pose tracking robust under complex dynamics
Dan Mikami; Kazuhiro Otsuka; Junji Yamato
2009-01-01
Abstract A novel particle filter, the Memory-based Particle Fil- ter (M-PF), is proposed that can visually track moving ob- jects that have complex dynamics. We aim to realize robust- ness against abrupt object movements,and quick recovery from tracking failure caused by factors such as occlusions. To that end, we eliminate the Markov assumption from the previous particle filtering framework,and predict
Particle filtering with path sampling and an application to a bimodal ocean current model
Weare, Jonathan [Courant Institute, New York University, 251 Mercer Street, New York, NY 10012 (United States)], E-mail: weare@cims.nyu.edu
2009-07-01
This paper introduces a recursive particle filtering algorithm designed to filter high dimensional systems with complicated non-linear and non-Gaussian effects. The method incorporates a parallel marginalization (PMMC) step in conjunction with the hybrid Monte Carlo (HMC) scheme to improve samples generated by standard particle filters. Parallel marginalization is an efficient Markov chain Monte Carlo (MCMC) strategy that uses lower dimensional approximate marginal distributions of the target distribution to accelerate equilibration. As a validation the algorithm is tested on a 2516 dimensional, bimodal, stochastic model motivated by the Kuroshio current that runs along the Japanese coast. The results of this test indicate that the method is an attractive alternative for problems that require the generality of a particle filter but have been inaccessible due to the limitations of standard particle filtering strategies.
Estimation of Tumor Size Evolution Using Particle Filters.
Costa, Jose M J; Orlande, Helcio R B; Velho, Haroldo F Campos; de Pinho, Suani T R; Dulikravich, George S; Cotta, Renato M; da Cunha Neto, Silvio H
2015-07-01
Cancer is characterized by the uncontrolled growth of cells with the ability of invading local organs and/or tissues and of spreading to other sites. Several kinds of mathematical models have been proposed in the literature, involving different levels of refinement, for the evolution of tumors and their interactions with chemotherapy drugs. In this article, we present the solution of a state estimation problem for tumor size evolution. A system of nonlinear ordinary differential equations is used as the state evolution model, which involves as state variables the numbers of tumor, normal and angiogenic cells, as well as the masses of the chemotherapy and anti-angiogenic drugs in the body. Measurements of the numbers of tumor and normal cells are considered available for the inverse analysis. Parameters appearing in the formulation of the state evolution model are treated as Gaussian random variables and their uncertainties are taken into account in the estimation of the state variables, by using an algorithm based on the auxiliary sampling importance resampling particle filter. Test cases are examined in the article dealing with a chemotherapy protocol for pancreatic cancer. PMID:25973723
Mihaylova, Lyudmila
Box Particle Filtering for Extended Object Tracking Nikolay Petrov1, Amadou Gning , Lyudmila to this challenging problem is presented within the recently proposed Box Particle Filtering framework. The Box Particle Filter replaces the point samples with regions, which we call boxes. The performance of the Box
Paris-Sud XI, Université de
an additional step to help make basic particle filters more robust with regard to outlying observations. FinallyOn particle filters applied to electricity load forecasting Tristan Launay1,2 Anne Philippe1 Sophie methods, and provide the calculations needed for the derivation of so-called particles filters. We also
NASA Astrophysics Data System (ADS)
Mattern, Jann Paul; Dowd, Michael; Fennel, Katja
2013-05-01
We assimilate satellite observations of surface chlorophyll into a three-dimensional biological ocean model in order to improve its state estimates using a particle filter referred to as sequential importance resampling (SIR). Particle Filters represent an alternative to other, more commonly used ensemble-based state estimation techniques like the ensemble Kalman filter (EnKF). Unlike the EnKF, Particle Filters do not require normality assumptions about the model error structure and are thus suitable for highly nonlinear applications. However, their application in oceanographic contexts is typically hampered by the high dimensionality of the model's state space. We apply SIR to a high-dimensional model with a small ensemble size (20) and modify the standard SIR procedure to avoid complications posed by the high dimensionality of the model state. Two extensions to the SIR include a simple smoother to deal with outliers in the observations, and state-augmentation which provides the SIR with parameter memory. Our goal is to test the feasibility of biological state estimation with SIR for realistic models. For this purpose we compare the SIR results to a model simulation with optimal parameters with respect to the same set of observations. By running replicates of our main experiments, we assess the robustness of our SIR implementation. We show that SIR is suitable for satellite data assimilation into biological models and that both extensions, the smoother and state-augmentation, are required for robust results and improved fit to the observations.
Goodarz Ahmadi
2002-07-01
In this project, a computational modeling approach for analyzing flow and ash transport and deposition in filter vessels was developed. An Eulerian-Lagrangian formulation for studying hot-gas filtration process was established. The approach uses an Eulerian analysis of gas flows in the filter vessel, and makes use of the Lagrangian trajectory analysis for the particle transport and deposition. Particular attention was given to the Siemens-Westinghouse filter vessel at Power System Development Facility in Wilsonville in Alabama. Details of hot-gas flow in this tangential flow filter vessel are evaluated. The simulation results show that the rapidly rotation flow in the spacing between the shroud and the vessel refractory acts as cyclone that leads to the removal of a large fraction of the larger particles from the gas stream. Several alternate designs for the filter vessel are considered. These include a vessel with a short shroud, a filter vessel with no shroud and a vessel with a deflector plate. The hot-gas flow and particle transport and deposition in various vessels are evaluated. The deposition patterns in various vessels are compared. It is shown that certain filter vessel designs allow for the large particles to remain suspended in the gas stream and to deposit on the filters. The presence of the larger particles in the filter cake leads to lower mechanical strength thus allowing for the back-pulse process to more easily remove the filter cake. A laboratory-scale filter vessel for testing the cold flow condition was designed and fabricated. A laser-based flow visualization technique is used and the gas flow condition in the laboratory-scale vessel was experimental studied. A computer model for the experimental vessel was also developed and the gas flow and particle transport patterns are evaluated.
Optimizing VLHC single-particle performance
NASA Astrophysics Data System (ADS)
Talman, Richard
2000-08-01
Since the sine qua non of building the Very Large Hadron Collider (VLHC) is the ability to bend 50 TeV particles in a circle stably and cheaply, we concentrate on the "arcs" of the VLHC, excluding all other problems. Considering only single-particle stability, the arcs are analyzsed using scaling considerations to choose the guide field B that minimizes cost. The extent to which the arcs can act as "achromatic optical fibers" for the beams is studied, in hopes of reducing the need for correction elements and spool pieces. Prescriptions are given to ameliorate the effects of resonance by choosing the best tunes and other parameters. For the (admittedly uncertain) parameters adopted in the paper the optimal value for B seems to be about 3 T, if NbTi superconductor is used. Possible cost savings through the use of iron at low field or superconductor other than NbTi at high field are not analyzsed.
Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm
Yann Cooren; Maurice Clerc; Patrick Siarry
2009-01-01
This paper presents a study of the performance of TRIBES, an adaptive particle swarm optimization algorithm. Particle Swarm\\u000a Optimization (PSO) is a biologically-inspired optimization method. Recently, researchers have used it effectively in solving\\u000a various optimization problems. However, like most optimization heuristics, PSO suffers from the drawback of being greatly\\u000a influenced by the selection of its parameter values. Thus, the common
Memory-Based Particle Filter for Tracking Objects with Large Variation in Pose and Appearance
Dan Mikami; Kazuhiro Otsuka; Junji Yamato
2010-01-01
\\u000a A novel memory-based particle filter is proposed to achieve robust visual tracking of a target’s pose even with large variations\\u000a in target’s position and rotation, i.e. large appearance changes. The memory-based particle filter (M-PF) is a recent extension\\u000a of the particle filter, and incorporates a memory-based mechanism to predict prior distribution using past memory of target\\u000a state sequence; it offers
A particle filtering approach for spatial arrival time tracking in ocean acoustics.
Jain, Rashi; Michalopoulou, Zoi-Heleni
2011-06-01
The focus of this work is on arrival time and amplitude estimation from acoustic signals recorded at spatially separated hydrophones in the ocean. A particle filtering approach is developed that treats arrival times as "targets" and tracks their "location" across receivers, also modeling arrival time gradient. The method is evaluated via Monte Carlo simulations and is compared to a maximum likelihood estimator, which does not relate arrivals at neighboring receivers. The comparison demonstrates a significant advantage in using the particle filter. It is also shown that posterior probability density functions of times and amplitudes become readily available with particle filtering. PMID:21682358
Sparsity Optimization in Design of Multidimensional Filter Networks
2014-11-22
general structure. Its efficiency is demonstrated by designing certain 2D and 3D filter networks. ... Least-Squares Problem; Filter networks; Medical imaging. ...... Dudgeon DE, Mersereau RM (1990) Multidimensional digital signal processing.
Initializing the Particle Swarm Optimizer Using the Nonlinear Simplex Method
Parsopoulos, Konstantinos
of the rest of the swarm. PSO has been proved to be very efficient algorithm in solving hard optimizationInitializing the Particle Swarm Optimizer Using the Nonlinear Simplex Method K.E. PARSOPOULOS, M-Words: - Particle Swarm, Nonlinear Simplex Method, Optimization 1 Introduction Evolutionary Computation (EC
Particle Swarm Optimization A tutorial prepared for SEAL'06
Li, Xiaodong
Particle Swarm Optimization A tutorial prepared for SEAL'06 Xiaodong Li, School of Computer Science n PSO for multiobjective optimization #12;10/11/2006 3 Swarm Intelligence #12;10/11/2006 4 Swarm;10/11/2006 9 Particle Swarm Optimization Russell EberhartJames Kennedy The inventors: #12;10/11/2006 10
A Binary Particle Swarm Optimization Algorithm for Lot Sizing Problem
M. Fatih; Yun-Chia Liang
This paper presents a binary particle swarm optimization algorithm for the lot sizing problem. The problem is to find order quantities which will minimize the total ordering and holding costs of ordering decisions. Test problems are constructed randomly, and solved optimally by Wagner and Whitin Algorithm. Then a binary particle swarm optimization algorithm and a traditional genetic algorithm are coded
Chaotic Particle Swarm Optimization Algorithm for Traveling Salesman Problem
Zhenglei Yuan; Liliang Yang; Yaohua Wu; Li Liao; Guoqiang Li
2007-01-01
In this paper, a novel algorithm based on particle optimization algorithm (PSO) and chaos optimization algorithm (COA) is presented to solve traveling salesman problem. Some new operators are proposed to overcome the difficulties of implementing PSO into solving the discreet problems. Meanwhile embedded with chaos optimization algorithm (COA) it can enhance particle's global searching ability so as not to converge
A Parallel Particle Swarm Optimization Algorithm with Communication Strategies
Jui-fang Chang; Shu-chuan Chu; John F. Roddick; Jeng-shyang Pan
2005-01-01
Particle swarm optimization (PSO) is an alternative population-based evolutionary computation technique. It has been shown to be capable of optimizing hard mathematical problems in continuous or binary space. We present here a parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data. The first strategy
Robust proposal distribution for adaptive visual tracking in a particle filtering frame work
Majid Komeili; Narges Armanfard; Morteza Valizadeh; Ehsanollah Kabir
2009-01-01
Different techniques are available in the literature for target tracking in video sequences. We focus our attention mainly on particle filter because of its power and versatility. We propose a metric named resampling force which measures the effectiveness of resampling stage. Resampling force represents how much the best old particles are regenerated after resampling. Furthermore, we extend the basic particle
A Particle Filter without Dynamics for Robust 3D Face Tracking
Le Lu; Xiang-Tian Dai; Gregory Hager
2004-01-01
Particle filtering is a very popular technique for sequential state estimation problem. However its convergence greatly depends on the balance between the number of particles\\/hypotheses and the fitness of the dynamic model. In particular, in cases where the dynamics are complex or poorly modeled, thousands of particles are usually required for real applications. This paper presents a hybrid sampling solution
Resource Allocation for Tracking Multiple Targets Using Particle Filters Aniruddha Kembhavi
Paris-Sud XI, UniversitÃ© de
Resource Allocation for Tracking Multiple Targets Using Particle Filters Aniruddha Kembhavi William from an expo- nential rise in the number of particles needed to jointly track multiple targets scheme allow- ing us to track a large number of targets using a small set of particles. First, targets
Memory-Efficient Gridmaps in Rao-Blackwellized Particle Filters for SLAM using Sonar Range Sensors
Christof Schröter; Hans-Joachim Böhme; Horst-Michael Gross
2007-01-01
Simultaneous Localization And Mapping (SLAM) has been an important field of research in the robotics community in recent years. A successful class of SLAM algorithms are Rao-Blackwellized Particle Filters (RBPF), where the particles approximate the pose belief distribution, while each particle contains a separate map. So far, RBPF with landmark based environment representations as well as gridmaps have been shown
Human behavior-based particle swarm optimization.
Liu, Hao; Xu, Gang; Ding, Gui-Yan; Sun, Yu-Bo
2014-01-01
Particle swarm optimization (PSO) has attracted many researchers interested in dealing with various optimization problems, owing to its easy implementation, few tuned parameters, and acceptable performance. However, the algorithm is easy to trap in the local optima because of rapid losing of the population diversity. Therefore, improving the performance of PSO and decreasing the dependence on parameters are two important research hot points. In this paper, we present a human behavior-based PSO, which is called HPSO. There are two remarkable differences between PSO and HPSO. First, the global worst particle was introduced into the velocity equation of PSO, which is endowed with random weight which obeys the standard normal distribution; this strategy is conducive to trade off exploration and exploitation ability of PSO. Second, we eliminate the two acceleration coefficients c 1 and c 2 in the standard PSO (SPSO) to reduce the parameters sensitivity of solved problems. Experimental results on 28 benchmark functions, which consist of unimodal, multimodal, rotated, and shifted high-dimensional functions, demonstrate the high performance of the proposed algorithm in terms of convergence accuracy and speed with lower computation cost. PMID:24883357
An improved particle swarm optimization algorithm for unit commitment
B. Zhao; C. X. Guo; B. R. Bai; Y. J. Cao
2006-01-01
This paper presents an improved particle swarm optimization algorithm (IPSO) for power system unit commitment. IPSO is an extension of the standard particle swarm optimization algorithm (PSO) which uses more particles’ information to control the mutation operation, and is similar to the social society in that a group of leaders could make better decisions. The convergence property of the proposed
Multi Swarm and Multi Best particle swarm optimization algorithm
Junliang Li; Xinping Xiao
2008-01-01
This paper proposes a novel particle swarm optimization algorithm: Multi-Swarm and Multi-Best particle swarm optimization algorithm. The novel algorithm divides initialized particles into several populations randomly. After calculating certain generations respectively, every population is combined into one population and continues to calculate until the stop condition is satisfied. At the same time, the novel algorithm updates particlespsila velocities and positions
Gaussian swarm: a novel particle swarm optimization algorithm
Renato A. Krohling; Lehrstuhl Elektrische
2004-01-01
In this paper, a novel particle swarm optimization algorithm based on the Gaussian probability distribution is proposed. The standard particle swarm optimization (PSO) algorithm has some parameters that need to be specified before using the algorithm, e.g., the accelerating constants c1 and c2, the inertia weight w, the maximum velocity Vmax, and the number of particles of the swarm. The
Multi-phase generalization of the particle swarm optimization algorithm
Buthainah Al-kazemi; Chilukuri K. Mohan
2002-01-01
Multi-phase particle swarm optimization is a new algorithm to be used for discrete and continuous problems. In this algorithm, different groups of particles have trajectories that proceed with differing goals in different phases of the algorithm. On several benchmark problems, the algorithm outperforms standard particle swarm optimization, genetic algorithm, and evolution programming
NASA Astrophysics Data System (ADS)
Banu, U. Sabura; Uma, G.
The present research work presents a novel control scheme for tuning PID controllers using Internal Model control with the filter time constant optimized using Bee colony Optimization technique. PID controllers are used widely in Industrial Processes. Tuning of PID controllers is accomplished using Internal Model control scheme. IMC includes tuning of filter constant ?. Compromise is made in selecting the filter constant ? since an increased value of ? results in a sluggish response whereas decreased value of filter constant leads in an aggressive action. In the present work, an attempt has been made to optimize the value of the ? by Bee colony optimization technique. Simulation results show the validity of the proposed scheme for the PID controller tuning.
: Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successfulBiometrics 000, 000Â000 DOI: 000 000 0000 Particle Swarm Optimization Techniques for Finding. This paper has been submitted for consideration for publication in Biometrics #12;Particle Swarm Optimization
An adaptive non-local means filter for denoising live-cell images and improving particle detection
Yang, Lei; Parton, Richard; Ball, Graeme; Qiu, Zhen; Greenaway, Alan H.; Davis, Ilan; Lu, Weiping
2010-01-01
Fluorescence imaging of dynamical processes in live cells often results in a low signal-to-noise ratio. We present a novel feature-preserving non-local means approach to denoise such images to improve feature recovery and particle detection. The commonly used non-local means filter is not optimal for noisy biological images containing small features of interest because image noise prevents accurate determination of the correct coefficients for averaging, leading to over-smoothing and other artifacts. Our adaptive method addresses this problem by constructing a particle feature probability image, which is based on Haar-like feature extraction. The particle probability image is then used to improve the estimation of the correct coefficients for averaging. We show that this filter achieves higher peak signal-to-noise ratio in denoised images and has a greater capability in identifying weak particles when applied to synthetic data. We have applied this approach to live-cell images resulting in enhanced detection of end-binding-protein 1 foci on dynamically extending microtubules in photo-sensitive Drosophila tissues. We show that our feature-preserving non-local means filter can reduce the threshold of imaging conditions required to obtain meaningful data. PMID:20599512
NASA Astrophysics Data System (ADS)
Fu, Jack; Khoury, Jehad; Cronin-Golomb, Mark; Woods, Charles L.
1995-01-01
Computer simulations of photorefractive thresholding filters for the reduction of artifact or dust noise demonstrate an increase in signal-to-noise ratio (SNR) of 70% to 95%, respectively, of that provided by the Wiener filter for inputs with a SNR of approximately 3. These simple, nearly optimal filters use a spectral thresholding profile that is proportional to the envelope of the noise spectrum. Alternative nonlinear filters with either 1/ nu or constant thresholding profiles increase the SNR almost as much as the noise-envelope thresholding filter.
Analysis and Comparison of the Generic and Auxiliary Particle Filtering Frameworks
Laurence Smith; Victor C. Aitken
2006-01-01
State estimation is of paramount importance in many fields of engineering. Filtering is the method of estimating the state of a system by incorporating noisy observations as they become available online with prior knowledge of the system model. Particle filters are sequential Monte Carlo methods that use a point mass representation of probability densities in order to propagate the required
M. M. Olama; S. M. Djouadi; C. D. Charalambous
2006-01-01
This paper presents two methods for tracking a user based on Aulin's wave scattering channel model. The first method is based on the extended Kalman filter approach, while the second method is based on the particle filter approach. Aulin's model takes into account non line of sight and multipath propagation environments, which are usually encountered in wireless fading channels. The
INS/GPS Tightly-coupled Integration using Adaptive Unscented Particle Filter
NASA Astrophysics Data System (ADS)
Zhou, Junchuan; Knedlik, Stefan; Loffeld, Otmar
With the rapid developments in computer technology, the particle filter (PF) is becoming more attractive in navigation applications. However, its large computational burden still limits its widespread use. One approach for reducing the computational burden without degrading the system estimation accuracy is to combine the PF with other filters, i.e., the extended Kalman filter (EKF) or the unscented Kalman filter (UKF). In this paper, the a posteriori estimates from an adaptive unscented Kalman filter (AUKF) are used to specify the PF importance density function for generating particles. Unlike the sequential importance sampling re-sampling (SISR) PF, the re-sampling step is not required in the algorithm, because the filter does not reuse the particles. Hence, the filter computational complexity can be reduced. Besides, the latest measurements are used to improve the proposal distribution for generating particles more intelligently. Simulations are conducted on the basis of a field-collected 3D UAV trajectory. GPS and IMU data are simulated under the assumption that a NovAtel DL-4plus GPS receiver and a Landmark™ 20 MEMS-based IMU are used. Navigation under benign and highly reflective signal environments are considered. Monte Carlo experiments are made. Numerical results show that the AUPF with 100 particles can present improved system estimation accuracy with an affordable computational burden when compared with the AEKF and AUKF algorithms.
Finite-element modeling for the design optimization of microwave filters
S. Bila; D. Baillargeat; M. Aubourg; S. Verdeyme; F. Seyfert; L. Baratchart; C. Boichon; F. Thevenon; J. Puech; C. Zanchi; L. Lapierre; J. Sombrin
2004-01-01
This paper outlines an electromagnetic optimization technique dedicated to the design of microwave bandpass filters. The purpose is to determine the optimal dimensions of the electromagnetic model. Applying this technique, the geometrical dimensions of the distributed structure are accurately determined, and no experimental readjustment is necessary. The optimization procedure combines an electromagnetic analysis and a parameter extraction. The analysis method
Improved Particle Filter in Sensor Fusion for Tracking Randomly Moving Object
Prahlad Vadakkepat; Liu Jing
2006-01-01
An improved particle-filter algorithm is proposed to track a randomly moving object. The algorithm is implemented on a mobile robot equipped with a pan-tilt camera and 16 sonar sen- sors covering 360?. Initially, the moving object is detected through a sequence of images taken by the stationary pan-tilt camera us- ing the motion-detection algorithm. Then, the particle-filter-based tracking algorithm, which
Dynamic Energy Management with Improved Particle Filter Prediction in Wireless Sensor Networks
Xue Wang; Junjie Ma; Sheng Wang; Daowei Bi
2007-01-01
Energy efficiency is a primary problem in wireless sensor networks which employ a large number of intelligent sensor nodes\\u000a to accomplish complicated tasks. Focused on the energy consumption problem in target tracking applications, this paper proposes\\u000a a dynamic energy management mechanism with an improved particle filter prediction in wireless sensor networks. The standard\\u000a particle filter is improved by combining the
A Robust Particle Filter-Based Face Tracker Using Combination of Color and Geometric Information
Bogdan Raducanu; Jordi Vitrià
2006-01-01
Particle filtering is one of the most successful approaches for visual tracking. However, so far, most particle-filter trackers\\u000a are limited to a single cue. This can be a serious limitation, since it can reduce the tracker’s robustness. In the current\\u000a work, we present a multiple cue integration approach applied for face tracking, based on color and geometric properties. We\\u000a tested
A Study on the Gesture Recognition Based on the Particle Filter
Hyung Kwan Kim; Yang Weon Lee; Chil-woo Lee
2007-01-01
The recognition of human gestures in image sequences is an important and challenging problem that enables a host of human-computer\\u000a interaction applications. This paper describes a gesture recognition algorithm based on the particle filters, namely CONDENSATION.\\u000a The particle filter is more efficient than any other tracking algorithm because the tracking mechanism follows Bayesian estimation\\u000a rule of conditional probability propagation. We
GPU-Accelerated Particle Filtering for 3D Model-Based Visual Tracking
J. Anthony Brown
2010-01-01
Model-based approaches to 3D object tracking and pose estimation that employ a particle filter are effective and robust , but computational complexity limits their efficacy in real-time scenarios. This thesis describes a novel framework for acceleration of particle filtering approaches to 3D model-based, markerless visual tracking in monocular video using a graphics processing unit (GPU). Specifically, NVIDIA compute unified device
Robust camera pose tracking for augmented reality using particle filtering framework
Fakhreddine Ababsa; Malik Mallem
2011-01-01
In this paper, we present new solutions for the problem of estimating the camera pose using particle filtering framework.\\u000a The proposed approach is suitable for real-time augmented reality (AR) applications in which the camera is held by the user.\\u000a This work demonstrates that particle filtering improve the robustness of the tracking comparing to existing approaches, such\\u000a as those based on
Target Tracking In a Sensor Network Based on Particle Filtering and Power-Aware Design
Y. Zhai; M. Yeary; J.-C. Noyer
2006-01-01
In this paper, we present a novel target tracking method applied to a distributed acoustic sensor network. The underlying tracking methodology is described as a multiple sensor tracking\\/fusion technique based on particle filtering (PF). As discussed in the most recent literature, particle filtering is defined as an emerging Monte-Carlo non-linear state estimation method. More specifically, in our proposed method each
Multi-agent Motion Tracking Using the Particle Filter in ISpace with DINDs
Taeseok Jin; Changhoon Park
2006-01-01
We present a method for representing, tracking and human following by fusing distributed multiple vision systems in ISpace,\\u000a with application to pedestrian tracking in a crowd. And the article presents the integration of color distributions into particle\\u000a filtering. Particle filters provide a robust tracking framework under ambiguity conditions. We propose to track the moving\\u000a objects by generating hypotheses not in
Robust online tracking using orientation and color incorporated adaptive models in particle filter
Chengjiao Guo; Ying Lu; Takeshi IKENAGA
2010-01-01
Moving object tracking has received much interest in the field of computer vision due to the increasing need for automated video analysis. Particles Filter is a very promising object tracking method since it is suitable for non-linear and\\/or non-Gaussian applications. Most particle filter applies color information in target model which might fail in the presence of similar colored objects in
Multi-agent Motion Tracking Using the Particle Filter in ISpace with DINDs
TaeSeok Jin; ChangHoon Park
We present a method for representing, tracking and human following by fusing distributed multiple vision systems in ISpace,\\u000a with application to pedestrian tracking in a crowd. And the article presents the integration of color distributions into particle\\u000a filtering. Particle filters provide a robust tracking framework under ambiguity conditions. We propose to track the moving\\u000a objects by generating hypotheses not in
Human-following robot using the particle filter in ISpace with distributed vision sensors
TaeSeok Jin; Kazuyuki Morioka; Hideki Hashimoto
2006-01-01
We present a method for representing tracking and human-following by fusing distributed multiple vision systems in intelligent\\u000a space, with applications to pedestrian tracking in a crowd. In this context, particle filters provide a robust tracking framework\\u000a under ambiguous conditions. The particle filter technique is used in this work, but in order to reduce its computational complexity\\u000a and increase its robustness,
A Marginalized Particle Filtering Framework for Simultaneous Localization and Mapping
Gustafsson, Fredrik
filtering (PF). The first trend is the major impact in simultaneous localization and mapping (SLAM) ap SLAM development environment, fusing measurements from inertial sensors (accelerometer and gyroscopes estimation, simultaneous localization and mapping, inertial sensors, vision. I. INTRODUCTION The main task
as , and the importance weights of the particles are obtained from (1) where the superscript denotes the -th trajectory for Particle Filtering Yufei Huang, Member, IEEE, and Petar M. Djuric´, Senior Member, IEEE Abstract--Particle-Gaussian dy- namic problems. One crucial issue in particle filtering is the selec- tion of the importance
Micro-machined tunable optical filters with optimized band-pass spectrum
D. Hohlfeld; H. Zappe
2003-01-01
A novel MEMS-based tunable optical filter structure is presented which for the first time combines the advantages of an optimized filter shape function with tunability. Such a filter is essential for monitoring and reconfiguration of optical communication networks. The device is based on a Fabry-Perot interferometer employing multiple solid-state silicon cavities and dielectric Bragg mirrors. It is fabricated as a
Removal of virus to protozoan sized particles in point-of-use ceramic water filters
Angela R. Bielefeldt; Kate Kowalski; Cherylynn Schilling; Simon Schreier; Amanda Kohler; R. Scott Summers
2010-01-01
The particle removal performance of point-of-use ceramic water filters (CWFs) was characterized in the size range of 0.02–100 ?m using carboxylate-coated polystyrene fluorescent microspheres, natural particles and clay. Particles were spiked into dechlorinated tap water, and three successive water batches treated in each of six different CWFs. Particle removal generally increased with increasing size. The removal of virus-sized 0.02 and 0.1 ?m
Jae-Hong Park; Ki-Young Yoon; Yang-Seon Kim; Jeong Hoon Byeon; Jungho Hwang
2009-01-01
This paper reports the installation of a carbon fiber ionizer in front of a fibrous medium filter to enhance the removal of\\u000a submicron aerosol particles and bioaerosols. Test particles (KCl) were classified with a size range of 50–600 nm using a differential\\u000a mobility analyzer (DMA). The number concentration of the test particles was measured using a condensation particle counter\\u000a (CPC).
Cosmological parameter estimation using Particle Swarm Optimization
NASA Astrophysics Data System (ADS)
Prasad, J.; Souradeep, T.
2014-03-01
Constraining parameters of a theoretical model from observational data is an important exercise in cosmology. There are many theoretically motivated models, which demand greater number of cosmological parameters than the standard model of cosmology uses, and make the problem of parameter estimation challenging. It is a common practice to employ Bayesian formalism for parameter estimation for which, in general, likelihood surface is probed. For the standard cosmological model with six parameters, likelihood surface is quite smooth and does not have local maxima, and sampling based methods like Markov Chain Monte Carlo (MCMC) method are quite successful. However, when there are a large number of parameters or the likelihood surface is not smooth, other methods may be more effective. In this paper, we have demonstrated application of another method inspired from artificial intelligence, called Particle Swarm Optimization (PSO) for estimating cosmological parameters from Cosmic Microwave Background (CMB) data taken from the WMAP satellite.
A novel particle swarm optimizer without velocity: Simplex-PSO
Hong-feng Xiao; Guan-zheng Tan
2010-01-01
A simplex particle swarm optimization (simplex-PSO) derived from the Nelder-Mead simplex method was proposed to optimize the\\u000a high dimensionality functions. In simplex-PSO, the velocity term was abandoned and its reference objectives were the best\\u000a particle and the centroid of all particles except the best particle. The convergence theorems of linear time-varying discrete\\u000a system proved that simplex-PSO is of consistent asymptotic
OPTIMAL SPATIAL FILTERING FOR AUDITORY STEADY-STATE RESPONSE DETECTION USING HIGH-DENSITY EEG
OPTIMAL SPATIAL FILTERING FOR AUDITORY STEADY-STATE RESPONSE DETECTION USING HIGH-DENSITY EEG, spatial filter- ing, multi-channel EEG. 1. INTRODUCTION Auditory steady-state responses (ASSRs implants' The scientific responsibility is assumed by its authors. with the same frequency. Modulation
An Evaluation of a Pilot Model Based on Kalman Filtering and Optimal Control
RODNEY D. WIERENGA
1969-01-01
A pilot model based on Kalman filtering and optimal control is given which, because of its structure, provides for estimation of the plant state variables, the forcing functions, the time delay, and the neuromuscular lag. The inverse filter and control problem is considered where the noise and cost function parameters yield a frequency response which is in close agreement with
PWM Inverter Output Filter Cost to Losses Trade Off and Optimal Design
Paris-Sud XI, Université de
PWM Inverter Output Filter Cost to Losses Trade Off and Optimal Design Robert J. Pasterczyk Jean--This paper describes how to design the output filter of a PWM inverter used in a Uninterruptible Power SupplyVA 3-ph. PWM inverter is taken as example. B. Design Constraints Uninterruptible Power Supply (UPS
A `gammachirp' function as an optimal auditory filter with the Mellin transform
IRINO Toshio; Morinosato Wakamiya
1996-01-01
A `gammachirp' function has been derived as an optimal auditory filter function in terms of minimal uncertainty in a joint time and modified-scale representation if the scale transform defined by Cohen (1989) is used in the auditory system. The gammatone function, which is widely used as the impulse response of a linear auditory filter, is a first-order approximation of the
Optimally designed narrowband guided-mode resonance reflectance filters for mid-infrared
Cunningham, Brian
Optimally designed narrowband guided-mode resonance reflectance filters for mid-infrared mid-infrared reflectance filters based on guided-mode resonance (GMR) in waveguide gratings@illinois.edu Abstract: An alternative to the well-established Fourier transform infrared (FT-IR) spectrometry, termed
A particle swarm pattern search method for bound constrained global optimization
Vicente, LuÃs Nunes
stationarity mentioned before. The particle swarm optimization algorithm was firstly proposed in [11, 24A particle swarm pattern search method for bound constrained global optimization A. Ismael F. Vaz, pattern search, particle swarm, derivative free optimization, global optimization, bound constrained
Inertial measurement unit calibration using Full Information Maximum Likelihood Optimal Filtering
Thompson, Gordon A. (Gordon Alexander)
2005-01-01
The robustness of Full Information Maximum Likelihood Optimal Filtering (FIMLOF) for inertial measurement unit (IMU) calibration in high-g centrifuge environments is considered. FIMLOF uses an approximate Newton's Method ...
A Rao-Blackwellized Particle Filter for EigenTracking Zia Khan, Tucker Balch, and Frank Dellaert
Dellaert, Frank
. Hence, particle filters have recently emerged as a simple and robust method for tracking in the presenceA Rao-Blackwellized Particle Filter for EigenTracking Zia Khan, Tucker Balch, and Frank Dellaert losing track. Par- ticle filters have recently emerged as a robust method for tracking in the presence
Zoran Gajic; Myo-Taeg Lim
In this paper we present a unified approach for optimal control and filtering of linear continuous-time sin- gularly perturbed linear systems that facilitates complete and exact decomposition of optimal control and filtering tasks into pure-slow and pure-fast time scales. The presented methodol- ogy eliminates numerical ill-conditioning of the original singu- larly perturbed problems, introduces parallelism into the design procedures, allows
Optimized cut of LiTaO3 for resonator filters with improved performance
N. Naumenko; B. Abbot
2002-01-01
In resonator filters, it is often desirable to minimize propagation loss simultaneously at resonant and anti-resonant frequencies. Using this criterion, we found an optimal dependence of rotation angle on electrode thickness in wavelengths, in rotated YX cuts of LiTaO3 with At grating. In particular, 48°YX cut was found to be optimal for resonator filters with thick Al electrodes, about 10%
EFFICIENT PARTICLE-PAIR FILTERING FOR ACCELERATION OF MOLECULAR DYNAMICS SIMULATION
Herbordt, Martin
EFFICIENT PARTICLE-PAIR FILTERING FOR ACCELERATION OF MOLECULAR DYNAMICS SIMULATION Matt Chiu ABSTRACT The acceleration of molecular dynamics (MD) simulations using high performance reconfigurable: determining the short-range force between particle pairs. In particular, we present the first FPGA study
AIR FILTER PARTICLE-SIZE EFFICIENCY TESTING FOR DIAMETERS GREATER THAN 1UM
The paper discusses tests of air filter particle-size efficiency for diameters greater than 1 micrometer. valuation of air cleaner efficiencies in this size range can be quite demanding, depending on the required accuracy. uch particles have sufficient mass to require considerati...
Paris-Sud XI, Université de
Factored Interval Particle Filtering for Gait Analysis Jamal Saboune, C´edric Rose, and Franc¸ois Charpillet Abstract-- Commercial gait analysis systems rely on wearable sensors. The goal of this study for efficiently weighting and resampling the particles. I. HUMAN MOTION CAPTURE Gait analysis is becoming a very
Variance Reduction for Particle Filters of Systems with Time Scale Separation
Del Moral , Pierre
1 Variance Reduction for Particle Filters of Systems with Time Scale Separation Dror Givon, dimensional reduction, variance reduction, Rao-Blackwellization, stochastic differential equations, jump) the use of the averaging principle for the dimensional reduction of the dynamics for each particle during
Bearings-Only Tracking Based on Multiple Sensor Measurements and Generalized Particle Filtering
P. M. Djuric; Ting Lu; M. F. Bugallo
2006-01-01
In this paper we address the problem of tracking by using bearings-only data obtained by more than one sensor. We apply the generalized particle filtering methodology which does not require any probabilistic assumptions, including prior probabilities and noise distributions in the state and observation equations. As a result, the proposed approach is much more robust in performance than standard particle
Visual tracking and recognition using appearance-adaptive models in particle filters
Shaohua Kevin Zhou; Rama Chellappa; Baback Moghaddam
2004-01-01
We propose an approach that incorporates appearance-based models in a particle filter to real- ize robust visual tracking and recognition algorithms. In conventional tracking algorithms, the appearance model is either fixed or rapidly changing, and the motion model is simply a ran- dom walk with fixed noise variance. Also, the number of particles is typically fixed. All these factors make
Mobile Robot Navigation Using Particle Swarm Optimization and Adaptive NN
Li, Yangmin
mini car-like robot with nonholonomic con- straints. The position of robot is p = [ x y ]T . DefiningMobile Robot Navigation Using Particle Swarm Optimization and Adaptive NN Yangmin Li and Xin Chen presents a novel design for mobile robot using particle swarm optimization (PSO) and adaptive NN control
An Estimation of Distribution Particle Swarm Optimization Algorithm
Mudassar Iqbal; Marco Antonio Montes De Oca
2006-01-01
In this paper we present an estimation of distribution par- ticle swarm optimization algorithm that borrows ideas from recent de- velopments in ant colony optimization which can be considered an es- timation of distribution algorithm. In the classical particle swarm opti- mization algorithm, particles exploit their individual memory to explore the search space. However, the swarm as a whole has
A particle swarm optimization algorithm based on orthogonal design
Jie Yang; Abdesselam Bouzerdoum; Son Lam Phung
2010-01-01
The last decade has witnessed a great interest in using evolutionary algorithms, such as genetic algorithms, evolutionary strategies and particle swarm optimization (PSO), for multivariate optimization. This paper presents a hybrid algorithm for searching a complex domain space, by combining the PSO and orthogonal design. In the standard PSO, each particle focuses only on the error propagated back from the
Chaotically encoded particle swarm optimization algorithm and its applications
Bilal Alatas; Erhan Akin
2009-01-01
This paper proposes a novel particle swarm optimization (PSO) algorithm, chaotically encoded particle swarm optimization algorithm (CENPSOA), based on the notion of chaos numbers that have been recently proposed for a novel meaning to numbers. In this paper, various chaos arithmetic and evaluation measures that can be used in CENPSOA have been described. Furthermore, CENPSOA has been designed to be
A dynamic inertia weight particle swarm optimization algorithm
Bin Jiao; Zhigang Lian; Xingsheng Gu
2008-01-01
Particle swarm optimization (PSO) algorithm has been developing rapidly and has been applied widely since it was introduced, as it is easily understood and realized. This paper presents an improved particle swarm optimization algorithm (IPSO) to improve the performance of standard PSO, which uses the dynamic inertia weight that decreases according to iterative generation increasing. It is tested with a
M. F. AlHajri; M. R. AlRashidi; M. E. El-Hawary
2007-01-01
This paper presents a novel particle swarm optimization based approach to optimally incorporate a distribution generator into a distribution system. The proposed algorithm combines particle swarm optimization with load flow algorithm to solve the problem in a single step, i.e. finding the best combination of location and size simultaneously. In the developed algorithm, the objective function to be minimized is
Distributed Particle Filters for Sensor Networks Mark Coates
-varying density estimation, and in the task of robot navigation [3, 4]. In these situations, the class of signal-space model that involves potentially nonlinear dynamics, nonlinear observations, and non-Gaussian inno between #12;2 sensor nodes, becomes inapplicable. Extended Kalman filters, grid-based meth- ods
SVD based Kalman particle filter for robust visual tracking
Xiaoqin Zhang; Weiming Hu; Zixiang Zhao; Yan-guo Wang; Xi Li; Qingdi Wei
2008-01-01
Object tracking is one of the most important tasks in computer vision. The unscented particlefilter algorithm has been extensively used to tackle this problem and achieved a great success, because it uses the UKF (un- scented Kalman filter) to generate a sophisticated pro- posal distributions which incorporates the newest ob- servations into the state transition distribution and thus overcomes the
A t-distribution based particle filter for target tracking
Shaojun Li; Hong Wang; Tianyou Chai
2006-01-01
From a Bayesian perspective, target tracking is the problem of generating an inference engine on the state of a target using a sequence of observations in time, which is to recursively estimate the probability density function (PDF) of the target state. Previous approaches to density estimation have mostly focused on Gaussian filters in practice, but these are well known sensitive
Particle Filter-Based Predictive Tracking for Robust Fish Counting
Erikson F. Morais; Mario Fernando Montenegro Campos; Flávio L. C. Pádua; Rodrigo L. Carceroni
2005-01-01
In this paper we study the use of computer vision tech- niques for for underwater visual tracking and counting of fishes in vivo. The methodology is based on the applica- tion of a Bayesian filtering technique that enables track- ing of objects whose number may vary over time. Unlike existing fish-counting methods, this approach provides ad- equate means for the
NASA Technical Reports Server (NTRS)
Juday, Richard D.
1993-01-01
Minimizing a Euclidean distance in the complex plane optimizes a wide class of correlation metrics for filters implemented on realistic devices. The algorithm searches over no more than two real scalars (gain and phase). It unifies a variety of previous solutions for special cases (e.g., a maximum signal-to-noise ratio with colored noise and a real filter and a maximum correlation intensity with no noise and a coupled filter). It extends optimal partial information filter theory to arbitrary spatial light modulators (fully complex, coupled, discrete, finite contrast ratio, and so forth), additive input noise (white or colored), spatially nonuniform filter modulators, and additive correlation detection noise (including signal dependent noise).
hyjang@bi.snu.ac.kr, btzhang@bi.snu.ac.kr Bayesian Filtering Method using n-gram Particle
n-gram O hyjang@bi.snu.ac.kr, btzhang@bi.snu.ac.kr Bayesian Filtering Method using n-gram Particle University (Bayesian Filtering) (Markov Process) (Kalman Filter) (Particle Filter) . (Random Walk) , . n-gram n-gram , n-gram . n-gram . 1. , n-gram . [4] (Word Segmentation)[3] . . n-gram . n-gram [5] n-gram
Parallel Hybrid Particle Swarm Optimization and Applications in Geotechnical Engineering
Youliang Zhang; Domenico Gallipoli; Charles Augarde
2009-01-01
A novel parallel hybrid particle swarm optimization algorithm named hmPSO is presented. The new algorithm combines particle\\u000a swarm optimization (PSO) with a local search method which aims to accelerate the rate of convergence. The PSO provides initial\\u000a guesses to the local search method and the local search accelerates PSO with its solutions. The hybrid global optimization\\u000a algorithm adjusts its searching
Wireless sensor network path optimization based on particle swarm algorithm
Xia Zhu; Yulin Zhang
2011-01-01
This paper proposes a particle swarm optimization algorithm for Wireless Sensor Network (WSN) path optimization. It designs and increases the mutation operator. This algorithm can find effective optimization of WSN routing, not only the solution quality is superior to genetic algorithm, but also increases in the success rate. In experimental results verified that proposed PSO-WSN intelligent method can escape from
Particle swarm optimization - mass-spring system analogon
Bernhard Brandstätter; Ulrike Baumgartner
2002-01-01
A concept for the optimization of nonlinear cost functionals, occurring in electrical engineering applications, using particle swarm optimization (PSO) is proposed. PSO is a stochastic optimization technique, whose stochastic behavior can be controlled very easily by one single factor. Additionally, this factor can be chosen to end up with a deterministic strategy, that does not need gradient information. The PSO
Image Thresholding Using Mean-Shift Based Particle Swarm Optimization
Chien-cheng Lee; Yu-chun Chiang; Cheng-yuan Shih; Wen-sheng Hu
2008-01-01
In this paper, we propose a mean shift based particle swarm optimization (MS-PSO) algorithm to solve the image thresholding problem. PSO is an emerging evolutionary algorithm. However, the traditional PSO uses random number to move to the optimal position. The best position is based on random trials. Therefore, it often just detects the sub-optimal solutions due to its intrinsic stochastic
Distribution Systems Reconfiguration using a modified particle swarm optimization algorithm
A. Y. Abdelaziz; F. M. Mohammed; S. F. Mekhamer; M. A. L. Badr
2009-01-01
This paper presents the particle swarm optimization (PSO) algorithm for solving the optimal distribution system reconfiguration problem for power loss minimization. The PSO is a relatively new and powerful intelligence evolution algorithm for solving optimization problems. It is a population-based approach. The PSO is originally inspired from the social behavior of bird flocks and fish schools. The proposed PSO algorithm
Vaswani, Namrata
PARTICLE FILTER WITH EFFICIENT IMPORTANCE SAMPLING AND MODE TRACKING (PF-EIS-MT) AND ITS a practically implementable particle filtering (PF) method called "PF-EIS-MT" for tracking on large dimensional dimensions and (b) direct application of PF requires an impractically large number of particles. PF-EIS
E. Bichot; L. Mascarilla; P. Courtellemont
In this paper, we propose a cooperation between motion segmentation and tracking by particle filtering to track non rigid objects in cluttered environment. Motion regions map is used in order to improve tracking in two steps: on the one hand, it drives propagation of particles from particle filtering towards regions of similar motion than target. Af- ter identifying moving target
Sequential Optimal Attitude Recursion Filter John A. Christian
Lightsey, Glenn
of Texas at Austin, Austin, Texas 78758 DOI: 10.2514/1.49561 A new nonlinear attitude filter called profile matrix E = expected value operator ei = ith unit vector observation F = Fisher information matrix
Auto-Clustering Using Particle Swarm Optimization and Bacterial Foraging
Jakob R. Olesen; Jorge Cordero Hernandez; Yifeng Zeng
2009-01-01
This paper presents a hybrid approach for clustering based on particle swarm optimization (PSO) and bacteria foraging algorithms\\u000a (BFA). The new method AutoCPB (Auto-Clustering based on particle bacterial foraging) makes use of autonomous agents whose primary objective is to cluster\\u000a chunks of data by using simplistic collaboration. Inspired by the advances in clustering using particle swarm optimization,\\u000a we suggest further
Design and performance optimization of fiber optic adaptive filters
Palacharla Paparao; Anjan Ghosh; Susan D. Allen
1991-01-01
The design of the least-mean-square algorithm-based fiber optic adaptive filters for processing guided lightwave signals in real time is described. Fiber optic adaptive filters can learn to change their parameters or to process a set of characteristics of the input signal. The realization employs as few electronic devices as possible and uses optical computation to utilize the advantages of optics
Optimal mismatched filter design for radar ranging, detection, and resolution
ROBERT J. McAULAY; J. Johnson
1971-01-01
In a multiple-target environment a radar signal processor often uses weighting filters that are not matched to the transmitted waveform. In this paper the mean-square range-estimation error, the detection Signal-to-noise ratio (SNR), and the effects of sidelobes are expressed in terms of the impulse response of an arbitrary mismatched filter. It is desired to find that impulse response that results
Design of oversampled DFT modulated filter banks optimized for acoustic echo cancellation
Qin Li; Wei-Ge Chen; Chao He; Henrique S. Malvar
2009-01-01
This paper describes a method for designing oversampled DFT filter banks (FB) optimized for subband acoustic echo cancellation (AEC). For this application, the design require- ments typically are good echo cancellation quality, low delay, small reconstruction error, and low computation complexity. Our method explicitly includes a model for echo return loss enhancement (ERLE) as part of the optimization criteria. Convergence
Global Stochastic Optimization for Robust and
Gall, Juergen
technique exhibits both the robustness of filtering strategies and a remarkable accuracy. We apply. It comprises a quantitative error analysis comparing the approach with local optimization, particle filtering, and a heuristic based on particle filtering. Keywords Human Motion Capture, Stochastic Optimization, Particle
Optimal design of a generalized compound eye particle detector array
Nehorai, Arye
Optimal design of a generalized compound eye particle detector array Arye Nehoraia, Zhi Liua ABSTRACT We analyze the performance of a novel detector array1 for detecting and localizing particle shape with a lens on top and a particle detectors subarray inside. The array's configuration is inspired
Using the Particle Swarm Optimization Algorithm for Robotic Search Applications
J. M. Hereford; M. Siebold; S. Nichols
2007-01-01
This paper describes the experimental results of using the particle swarm optimization (PSO) algorithm to control a suite of robots. In our approach, each bot is one particle in the PSO; each particle\\/bot makes measurements, updates its own position and velocity, updates its own personal best measurement (pbest) and personal best location (if necessary), and broadcasts to the other bots
Rao-Blackwellized particle filter for multiple target tracking
Simo Särkkä; Aki Vehtari; Jouko Lampinen
2007-01-01
In this article we propose a new Rao-Blackwellized particle ltering based algorithm for tracking an unknown number of targets. The algorithm is based on formulating probabilistic stochastic process models for target states, data associations, and birth and death processes. The tracking of these stochastic processes is implemented using sequential Monte Carlo sampling or particle ltering, and the eciency of the
A note on optimal filtering in the presence of unknown biases
NASA Technical Reports Server (NTRS)
Joshi, S. M.
1975-01-01
This note considers some aspects of the optimal filtering problem for linear processes in the presence of unknown biases in the input and the observations. It is proved via duality that the optimal filtering problem in the presence of an input bias is equivalent to a certain optimal regulator problem incorporating integral feedback. The question of observability of the augmented system used in the state and bias estimation is answered by deriving necessary and sufficient conditions when bias is present (1) in the input, (2) in the observations and (3) both in the input and the observations.
Human tracking in the complicated background by Particle Filter using color-histogram and HOG
Lujun Jin; Jian Cheng; Hu Huang
2010-01-01
Human tracking based on computer vision, is a challenging and crucial problem in intelligent video surveillance system. As is known to all, human motion is usually non-linear and non-Gaussian, many prevalent frameworks are not appropriate, such as Kalman Filter, etc. Nevertheless, the Particle Filter could still have good performance even when the system is nonlinear and non-Gaussian. This paper is
Particle filtering for signal enhancement in a noisy shallow ocean environment
J. V. Candy
2010-01-01
The development of model-based processing techniques in ocean acoustics is well-known evolving from the pure statistical approach of maximum likelihood parameter estimation, matched-field processing and sequential model-based processing for Gaussian uncertainties. More recent model-based techniques such as unscented Kalman filtering (UKF) and sequential Markov chain Monte Carlo (MCMC) methods using particle filters (PF) have been developed to improve both unimodal
Robust Auxiliary Particle Filter with an Adaptive Appearance Model for Visual Tracking
Du Yong Kim; Ehwa Yang; Moongu Jeon; Vladimir Shin
2010-01-01
\\u000a The algorithm proposed in this paper is designed to solve two challenging issues in visual tracking: uncertainty in a dynamic\\u000a motion model and severe object appearance change. To avoid filter drift due to inaccuracies in a dynamic motion model, a sliding\\u000a window approach is applied to particle filtering by considering a recent set of observations with which internal auxiliary\\u000a estimates
A Study on Smoothing for Particle-Filtered 3D Human Body Tracking
Patrick Peursum; Svetha Venkatesh; Geoff West
2010-01-01
Stochastic models have become the dominant means of approaching the problem of articulated 3D human body tracking, where approximate\\u000a inference is employed to tractably estimate the high-dimensional (?30D) posture space. Of these approximate inference techniques,\\u000a particle filtering is the most commonly used approach. However filtering only takes into account past observations—almost\\u000a no body tracking research employs smoothing to improve the
Sun, W Y [Lawrence Berkeley Lab., CA (United States)
1993-04-01
This thesis solves the problem of finding the optimal linear noise-reduction filter for linear tomographic image reconstruction. The optimization is data dependent and results in minimizing the mean-square error of the reconstructed image. The error is defined as the difference between the result and the best possible reconstruction. Applications for the optimal filter include reconstructions of positron emission tomographic (PET), X-ray computed tomographic, single-photon emission tomographic, and nuclear magnetic resonance imaging. Using high resolution PET as an example, the optimal filter is derived and presented for the convolution backprojection, Moore-Penrose pseudoinverse, and the natural-pixel basis set reconstruction methods. Simulations and experimental results are presented for the convolution backprojection method.
Distributed Adaptive Particle Swarm Optimizer in Dynamic Environment
Cui, Xiaohui [ORNL; Potok, Thomas E [ORNL
2007-01-01
In the real world, we have to frequently deal with searching and tracking an optimal solution in a dynamical and noisy environment. This demands that the algorithm not only find the optimal solution but also track the trajectory of the changing solution. Particle Swarm Optimization (PSO) is a population-based stochastic optimization technique, which can find an optimal, or near optimal, solution to a numerical and qualitative problem. In PSO algorithm, the problem solution emerges from the interactions between many simple individual agents called particles, which make PSO an inherently distributed algorithm. However, the traditional PSO algorithm lacks the ability to track the optimal solution in a dynamic and noisy environment. In this paper, we present a distributed adaptive PSO (DAPSO) algorithm that can be used for tracking a non-stationary optimal solution in a dynamically changing and noisy environment.
Particle filtering for obstacle tracking in UAS sense and avoid applications.
Tirri, Anna Elena; Fasano, Giancarmine; Accardo, Domenico; 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
Mapping loops onto Coarse-Grained Reconfigurable Architectures using Particle Swarm Optimization
Norvell, Theodore S.
and efficient Modulo-Constrained Hybrid Particle Swarm Optimization (MCHPSO) scheduling algorithm to exploitMapping loops onto Coarse-Grained Reconfigurable Architectures using Particle Swarm Optimization Architectures; Particle Swarm Optimization; Modulo Scheduling; Loop level parallelism; Mapping. I. INTRODUCTION
NASA Astrophysics Data System (ADS)
Shishkovsky, I.; Sherbakov, V.; Pitrov, A.
2007-06-01
The main goal of the work was optimization of the phase and porous fine structures of filter elements and subsequent laser synthesis by the method layer-by-layer Selective Laser Sintering (SLS) of functional devices, exploration of their properties and requirements of synthesis. Common methodical approaches are developed by the searching optimal requirements of layer-by-layer synthesis usable to different powder compositions and concrete guidelines (conditions of sintering, powder composition, etc.) for SLS of filter elements (including anisotropic) from metal-polymer powder mixture - brass + polycarbonate{PC} = 6:1. As a result of numerical simulations it designed an original graph - numerical procedure and represented a computer program for definition of flow filter performances, as homogeneous (isotropic) as heterogeneous (anisotropic), having the cylindrical shape. Calculation of flow behavior for anisotropic filter elements allows predicting their future applications and managing its.
On the application of optimal wavelet filter banks for ECG signal classification
NASA Astrophysics Data System (ADS)
Hadjiloucas, S.; Jannah, N.; Hwang, F.; Galvão, R. K. H.
2014-03-01
This paper discusses ECG signal classification after parametrizing the ECG waveforms in the wavelet domain. Signal decomposition using perfect reconstruction quadrature mirror filter banks can provide a very parsimonious representation of ECG signals. In the current work, the filter parameters are adjusted by a numerical optimization algorithm in order to minimize a cost function associated to the filter cut-off sharpness. The goal consists of achieving a better compromise between frequency selectivity and time resolution at each decomposition level than standard orthogonal filter banks such as those of the Daubechies and Coiflet families. Our aim is to optimally decompose the signals in the wavelet domain so that they can be subsequently used as inputs for training to a neural network classifier.
Optimized Loading for Particle-in-cell Gyrokinetic Simulations
J.L.V. Lewandowski
2004-05-13
The problem of particle loading in particle-in-cell gyrokinetic simulations is addressed using a quadratic optimization algorithm. Optimized loading in configuration space dramatically reduces the short wavelength modes in the electrostatic potential that are partly responsible for the non-conservation of total energy; further, the long wavelength modes are resolved with good accuracy. As a result, the conservation of energy for the optimized loading is much better that the conservation of energy for the random loading. The method is valid for any geometry and can be coupled to optimization algorithms in velocity space.
Particle Count Statistics Applied to the Penetration of a Filter Challenged with Nanoparticles.
O'Shaughnessy, Patrick T; Schmoll, Linda H
2013-01-01
Statistical confidence in a single measure of filter penetration (P) is dependent on the low number of particle counts made downstream of the filter. This paper discusses methods for determining an upper confidence limit (UCL) for a single measure of penetration. The magnitude of the UCL was then compared to the P value, UCL ? 2P, as a penetration acceptance criterion (PAC). This statistical method was applied to penetration trials involving an N95 filtering facepiece respirator challenged with sodium chloride and four engineered nanoparticles: titanium dioxide, iron oxide, silicon dioxide and single-walled carbon nanotubes. Ten trials were performed for each particle type with the aim of determining the most penetrating particle size (MPPS) and the maximum penetration, Pmax. The PAC was applied to the size channel containing the MPPS. With those P values that met the PAC for a given set of trials, an average Pmax and MPPS was computed together with corresponding standard deviations. Because the size distribution of the silicon dioxide aerosol was shifted towards larger particles relative to the MPPS, none of the ten trials satisfied the PAC for that aerosol. The remaining four particle types resulted in at least 4 trials meeting the criterion. MPPS values ranged from 35 - 53 nm with average Pmax values varying from 4.0% for titanium dioxide to 7.0% for iron oxide. The use of the penetration acceptance criterion is suggested for determining the reliability of penetration measurements obtained to determine filter Pmax and MPPS. PMID:24678138
Particle Clogging in Filter Media of Embankment Dams: A Numerical and Experimental Study
NASA Astrophysics Data System (ADS)
Antoun, T.; Kanarska, Y.; Ezzedine, S. M.; Lomov, I.; Glascoe, L. G.; Smith, J.; Hall, R. L.; Woodson, S. C.
2013-12-01
The safety of dam structures requires the characterization of the granular filter ability to capture fine-soil particles and prevent erosion failure in the event of an interfacial dislocation. Granular filters are one of the most important protective design elements of large embankment dams. In case of cracking and erosion, if the filter is capable of retaining the eroded fine particles, then the crack will seal and the dam safety will be ensured. Here we develop and apply a numerical tool to thoroughly investigate the migration of fines in granular filters at the grain scale. The numerical code solves the incompressible Navier-Stokes equations and uses a Lagrange multiplier technique which enforces the correct in-domain computational boundary conditions inside and on the boundary of the particles. The numerical code is validated to experiments conducted at the US Army Corps of Engineering and Research Development Center (ERDC). These laboratory experiments on soil transport and trapping in granular media are performed in constant-head flow chamber filled with the filter media. Numerical solutions are compared to experimentally measured flow rates, pressure changes and base particle distributions in the filter layer and show good qualitative and quantitative agreement. To further the understanding of the soil transport in granular filters, we investigated the sensitivity of the particle clogging mechanism to various parameters such as particle size ratio, the magnitude of hydraulic gradient, particle concentration, and grain-to-grain contact properties. We found that for intermediate particle size ratios, the high flow rates and low friction lead to deeper intrusion (or erosion) depths. We also found that the damage tends to be shallower and less severe with decreasing flow rate, increasing friction and concentration of suspended particles. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and was sponsored by the Department of Homeland Security (DHS), Science and Technology Directorate, Homeland Security Advanced Research Projects Agency (HSARPA).
Wavelet-based Image Denoising with Optimal Filter
Yong-hwan Lee; Sang-burm Rhee
2005-01-01
Image denoising is basic work for image processing, analysis and computer vision. This paper proposes a novel algorithm based on wavelet threshold for image denoising, which is combined with the linear CLS (Constrained Least Squares) filtering and thresholding methods in the transform domain. We demonstrated through simulations with images contaminated by white Gaussian noise that our scheme exhibits better performance
TESTING OF OPTIMAL FILTERS FOR GRAVITATIONALWAVE SIGNALS: AN EXPERIMENTAL IMPLEMENTATION
is the main tool of signal extraction for gravitational wave (GW) detectors. In gaussian noise, WK filtering implemented within the data analysis of the AURIGA ultra-cryogenic detector2 . We performed a preliminary model for the detector transfer function and noise spectrum. The experimental setup both for the room
Optimal spatial filtering of real data from submarine sonar arrays
Brian G. Ferguson; Dragana Carevic
2010-01-01
Submarine hydrophone arrays sample the underwater acoustic pressure field in space and time to sense the presence of sources of radiated sound and to extract tactical information from the received sounds. The outputs of the hydrophones are combined by a spatial filter (or beamformer) so that signals from a chosen direction are coherently added while the effects of noise and
On optimal filtering of GPS dual frequency observations without using orbit information
Hans-Juergen Eueler; Clyde C. Goad
1991-01-01
The concept of optimal filtering of observations collected with a dual frequency GPS P-code receiver is investigated in comparison\\u000a to an approach for C\\/A-code units. The filter presented here uses only data gathered between one receiver and one satellite.\\u000a The estimated state vector consists of a one-way pseudorange, ionospheric influence, and ambiguity biases. Neither orbit information\\u000a nor station information is
Chaotic particle swarm optimization with mutation for classification.
Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza
2015-01-01
In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms. PMID:25709937
Chaotic Particle Swarm Optimization with Mutation for Classification
Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza
2015-01-01
In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms. PMID:25709937
J. H. Ji; G. N. Bae; S. H. Kang; J. Hwang
2003-01-01
Electret filters are composed of permanently charged electret fibers and are widely used in applications requiring high collection efficiency and low-pressure drop. We tested electret filter media used in manufacturing cabin air filters by applying two different charging states to the test particles. These charging states were achieved by spray electrification through the atomization process and by bipolar ionization with
Siegel, Jeffrey
Proceedings of Healthy Buildings 2009 Paper 535 HVAC filters as "passive" samplers: fate analysis: fedenoris@mail.utexas.edu SUMMARY To assess the potential use of HVAC filters as passive indoor samplers, exfiltration, and capture in the HVAC filter. The results suggest that large particles are likely to deposit
Ares-I Bending Filter Design using a Constrained Optimization Approach
NASA Technical Reports Server (NTRS)
Hall, Charles; Jang, Jiann-Woei; Hall, Robert; Bedrossian, Nazareth
2008-01-01
The Ares-I launch vehicle represents a challenging flex-body structural environment for control system design. Software filtering of the inertial sensor output is required to ensure adequate stable response to guidance commands while minimizing trajectory deviations. This paper presents a design methodology employing numerical optimization to develop the Ares-I bending filters. The design objectives include attitude tracking accuracy and robust stability with respect to rigid body dynamics, propellant slosh, and flex. Under the assumption that the Ares-I time-varying dynamics and control system can be frozen over a short period of time, the bending filters are designed to stabilize all the selected frozen-time launch control systems in the presence of parameter uncertainty. To ensure adequate response to guidance command, step response specifications are introduced as constraints in the optimization problem. Imposing these constrains minimizes performance degradation caused by the addition of the bending filters. The first stage bending filter design achieves stability by adding lag to the first structural frequency to phase stabilize the first flex mode while gain stabilizing the higher modes. The upper stage bending filter design gain stabilizes all the flex bending modes. The bending filter designs provided here have been demonstrated to provide stable first and second stage control systems in both Draper Ares Stability Analysis Tool (ASAT) and the MSFC MAVERIC 6DOF nonlinear time domain simulation.
Quantum-behaved particle swarm optimization algorithm for economic load dispatch of power system
Zhisheng Zhang
2010-01-01
Quantum-behaved particle swarm optimization algorithm is firstly used in economic load dispatch of power system in this paper. Quantum-behaved particle swarm optimization algorithm is the integration of particle swarm optimization algorithm and quantum computing theory. The superposition characteristic and probability representation of quantum methodology are combined into particle swarm optimization algorithm. This can make a single particle be expressed by
A Distributed Particle Swarm Optimization Algorithm for Swarm Robotic Applications
James M. Hereford
2006-01-01
We have derived a version of the particle swarm optimization algorithm that is suitable for a swarm consisting of a large number of small, mobile robots. The algorithm, called the distributed PSO (dPSO), is for \\
NASA Astrophysics Data System (ADS)
Glascoe, L. G.; Ezzedine, S. M.; Kanarska, Y.; Lomov, I. N.; Antoun, T.; Smith, J.; Hall, R.; Woodson, S.
2014-12-01
Understanding the flow of fines, particulate sorting in porous media and fractured media during sediment transport is significant for industrial, environmental, geotechnical and petroleum technologies to name a few. For example, the safety of dam structures requires the characterization of the granular filter ability to capture fine-soil particles and prevent erosion failure in the event of an interfacial dislocation. Granular filters are one of the most important protective design elements of large embankment dams. In case of cracking and erosion, if the filter is capable of retaining the eroded fine particles, then the crack will seal and the dam safety will be ensured. Here we develop and apply a numerical tool to thoroughly investigate the migration of fines in granular filters at the grain scale. The numerical code solves the incompressible Navier-Stokes equations and uses a Lagrange multiplier technique. The numerical code is validated to experiments conducted at the USACE and ERDC. These laboratory experiments on soil transport and trapping in granular media are performed in constant-head flow chamber filled with the filter media. Numerical solutions are compared to experimentally measured flow rates, pressure changes and base particle distributions in the filter layer and show good qualitative and quantitative agreement. To further the understanding of the soil transport in granular filters, we investigated the sensitivity of the particle clogging mechanism to various parameters such as particle size ratio, the magnitude of hydraulic gradient, particle concentration, and grain-to-grain contact properties. We found that for intermediate particle size ratios, the high flow rates and low friction lead to deeper intrusion (or erosion) depths. We also found that the damage tends to be shallower and less severe with decreasing flow rate, increasing friction and concentration of suspended particles. We have extended these results to more realistic heterogeneous population particulates for sediment transport. This work performed under the auspices of the US DOE by LLNL under Contract DE-AC52-07NA27344 and was sponsored by the Department of Homeland Security, Science and Technology Directorate, Homeland Security Advanced Research Projects Agency.
A multiagent-based particle swarm optimization approach for optimal reactive power dispatch
B. Zhao; C. X. Guo; Y. J. Cao
2005-01-01
Reactive power dispatch in power systems is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. In this paper, a solution to the reactive power dispatch problem with a novel particle swarm optimization approach based on multiagent systems (MAPSO) is presented. This method integrates the multiagent system (MAS) and the particle swarm
Multiple Faces Tracking Using Motion Prediction and IPCA in Particle Filters
Sukwon Choi; Daijin Kim
2007-01-01
We propose an efficient real-time face tracking system that can track fast moving face and cope with the illumination changes.\\u000a To achieve these goals, we use the active appearance model(AAM) to represent the face image due to its simplicity and flexibility\\u000a and take the particle filter framework to track the face image due to its robustness. We modify the particle
An Improved Particle Swarm Optimization Algorithm with Disturbance Term
Qingyuan He; Chuanjiu Han
2006-01-01
\\u000a The standard particle swarm optimization (PSO) algorithm, existing improvements and their influence to the performance of\\u000a standard PSO are introduced. The framework of PSO basic formula is analyzed. Implied by its three-term structure, the inherent\\u000a shortcoming that trends to local optima is indicated. Then a modified velocity updating formula of particle swarm optimization\\u000a algorithm is declared. The addition of the
Quantum-Behaved Particle Swarm Optimization Algorithm with Controlled Diversity
Jun Sun; Wenbo Xu; Wei Fang
2006-01-01
\\u000a Premature convergence, the major problem that confronts evolutionary algorithms, is also encountered with the Particle Swarm\\u000a Optimization (PSO) algorithm. In the previous work [11], [12], [13], the Quantum-behaved Particle Swarm (QPSO) is proposed.\\u000a This novel algorithm is a global-convergence-guaranteed and has a better search ability than the original PSO. But like other\\u000a evolutionary optimization technique, premature in the QPSO is
Lewpiriyawong, Nuttawut; Yang, Chun; Lam, Yee Cheong
2008-01-01
The conventional microfluidic H filter is modified with multi-insulating blocks to achieve a flow-through manipulation and separation of microparticles. The device transports particles by exploiting electro-osmosis and electrophoresis, and manipulates particles by utilizing dielectrophoresis (DEP). Polydimethylsiloxane (PDMS) blocks fabricated in the main channel of the PDMS H filter induce a nonuniform electric field, which exerts a negative DEP force on the particles. The use of multi-insulating blocks not only enhances the DEP force generated, but it also increases the controllability of the motion of the particles, facilitating their manipulation and separation. Experiments were conducted to demonstrate the controlled flow direction of particles by adjusting the applied voltages and the separation of particles by size under two different input conditions, namely (i) a dc electric field mode and (ii) a combined ac and dc field mode. Numerical simulations elucidate the electrokinetic and hydrodynamic forces acting on a particle, with theoretically predicted particle trajectories in good agreement with those observed experimentally. In addition, the flow field was obtained experimentally with fluorescent tracer particles using the microparticle image velocimetry (mu-PIV) technique. PMID:19693372