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American Control Conference

作者： Ziyu Liu Shihong Wei James C. Spall Johns Hopkins University Applied Math Whitehead Hall North Charles Street Baltimore MD 3400 21218 JHU Applied Physics Laboratory Research Professor of Johns Hopkins University Applied Math Whitehead Hall North Charles Street Baltimore MD 3400 21218

ISBN: (纸本)9781538679012;9781538679265

The particle filter is a popular Bayesian filtering algorithm for use in cases where the state-space model is nonlinear and/or the random terms (initial state or noises) are non-Gaussian distributed. We study the behavior of the error in the particle filter algorithm as the number of particles gets large. After a decomposition of the error into two terms, we show that the difference between the estimator and the conditional mean is asymptotically normal when the resampling is done at every step in the filtering process. Two nonlinear/non-Gaussian examples are tested to verify this conclusion.

关键词： particle filters particle filtering algorithm State space models Resampling Non-Gaussian Bayesian filtering State-space methods

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Target Tracking Based on Improved particle Filter algorithm-and Camshift Method 8

Target Tracking Based on Improved Particle Filter Algorithm-...

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8th IEEE Annual International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (IEEE-CYBER)

作者： Zheng, Shuang Yuan, Liang Chen, Heping Shenzhen Acad Robot Shenzhen Peoples R China Xinjiang Univ Coll Mech Engn Urumqi 830047 Peoples R China Xinjiang Univ Coll Mech Engn Xinjiang 830047 Peoples R China

ISBN: (纸本)9781538670576

The applications of visual tracking in real life are becoming more and more extensive, and attention paid to it is getting higher and higher. The problem of traditional particle filtering is that the recommended distribution probability density generally does not include the target current position state information. The Camshift algorithm introduced in the particle filtering prediction particle phase is proposed, and the current position information is added to the new sampling particle. Using the directional feature, the target edge is extracted by Snake, the observation probability of the directional model feature is calculated. The algorithm greatly reduces the problem of particle degradation and improves the tracking accuracy. And the experimental results show that the algorithm can be used to solve the single-target and multi-target tracking problems with good robustness.

关键词： object tracking particle filtering (numerical methods) probability target tracking Target tracking particle filters tracking objects particle filtering algorithm POSITION INFORMATION DIRECTIONAL tracking accuracy Current Position visual tracking Probability

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Target Tracking via particle Filter and Convolutional Network

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JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING 2018年第Pt.1期2018卷 9-9页

作者： Chu, Hongxia Wang, Kejun Xing, Xianglei Harbin Engn Univ Coll Automat Harbin Heilongjiang Peoples R China Heilongjiang Inst Technol Coll Elect & Informat Engn Harbin Heilongjiang Peoples R China

We propose a more effective tracking algorithm which can work robustly in a complex scene such as illumination, appearance change, and partial occlusion. The algorithm is based on an improved particle filter which used the efficient design of observation model. Predefined convolutional filters are used to extract the high-order features. The global representation is generated by combining local features without changing their structures and space arrangements. It not only increases the feature invariance, but also maintains the specificity. The extracted feature from convolution network is introduced into particle filter algorithm. The observation model is constructed by fusing the color feature of the target and a set of features from templates which are extracted by convolutional networks without training in our paper. It is fused with the features extracted from convolutional network for tracking. In the process of tracking, the template is updated in real time, and then the robustness of the algorithm is improved. Experiments show that the algorithm can achieve an ideal tracking effect when the targets are in a complex environment.

关键词： Targets color feature particle filtering algorithm tracking higher-order Target tracking algorithms jigs(templates) Network particle filters

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Realization of Moving Object Detection and Tracking algorithm Based on Frame Difference Method and particle Filter algorithm 29

Realization of Moving Object Detection and Tracking Algorith...

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第29届中国控制与决策会议

作者： ZHUANG Yaoming WU Chengdong ZHANG Yunzhou FENG Sheng College of Information Science & Engineering Northeastern University

ISBN: (纸本)9781509046584

With the multimedia sensor is widely used,moving objects detection and tracking based on video have gradually become a popular research field in multimedia sensor *** the multimedia sensor networks,the existing target detection and tracking algorithm cannot meet the system requirements of real-time and accuracy,which existing the problem of slowness and target ***,this paper proposes moving target detection and tracking algorithm based on frame difference method and particle filter *** the same time,a WMSN node experiment platform based on ARM embedded system is *** the practical experiment,the effective detection and tracking of the moving target are realized on the *** with the general computer simulation experiment,the experimental results running on the experiment platform is closer to the practical ***,the experimental results show that the algorithm proposed in this paper is superior to Kalman filter in speed and stability.

关键词： multimedia sensor networks target detection and tracking frame difference method particle filtering algorithm Embedded Systems

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Multiple Model based Unscented particle Filter algorithm for a SINS/CNS Integrated Navigation System

Multiple Model based Unscented Particle Filter Algorithm for...

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American Control Conference

作者： Fangfang Zhao Shuzhi Sam Ge Wei He School of Computer Science and Engineering Center for Robotics University of Electronic Science and Technology of China Chengdu 611731 China the Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore and Center for Robotics University of Electronic Science and Technology of China Chengdu 611731 China School of Automation and Electrical Engineering University of Science and Technology of Beijing Beijing 100083 China

ISBN: (纸本)9781509045839

The single model filter has poor adaptability under the uncertain or unknown system parameters, multiple model filters can be used to resolve this problem. This paper investigates the multiple model adaptive estimation (MMAE) algorithm and proposes a multiple model unscented particle filter (MMUPF) algorithm. Forward the traditional MMAE method which uses extend Kalman filter (EKF) or unscented Kalman filter (UKF) as each filter to execute the state estimation process. In this method, we combine MMAE with unscented particle filter (UPF), and replace UKF with UPF which shows higher accuracy than UKF for the nonlinear system. At the same time, this algorithm is applied to the integrated navigation system which combines strap-down inertial navigation with celestial navigation by a ballistic missile. Simulations are provided to demonstrate that MMUPF algorithm has the highest navigation precision, but at the cost of heavier computational burden.

关键词： resolve problem paper Resolve hybrid navigation systems Navigation systems particle filtering algorithm State estimation Celestial navigation INERTIAL NAVIGATION paper particle filters Filters multiples Nonlinear systems system parameter

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A particle filtering algorithm for Cooperative Tracking of Nodes in Wireless Networks

A Particle Filtering Algorithm for Cooperative Tracking of N...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications

作者： T. Sathyan M. Hedley ICT Center Commonwealth Scientific and Industrial Research Organization (CSIRO)

ISBN: (纸本)9781457713460

Tracking nodes in a wireless network is beneficial for many applications. This is, however, a challenging task in multihop networks, particularly when combined with the limitations in processing power and communication capability of the nodes. The task is further complicated when rapid deployment of the network is required. In this paper we present a new algorithm for cooperative tracking of nodes, which allows accurate tracking over large indoor areas using only a small number of anchor nodes. The proposed algorithm is based on the multiple model particle filter and addresses several tracking issues not previously addressed in the literature. Further, we present a new indoor ranging error model based on our measurements that is simple, yet widely applicable, and gives much better localization accuracy compared to the standard Gaussian error model. Experimental results using a deployed wireless network, where GPS referenced nodes are used as anchors, demonstrate the effectiveness of the proposed algorithm.

关键词： Wireless networks Ganglia particle filtering algorithm tracking Boat anchors processing power

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Multiple Target Tracking with particle filtering in UWB Radar Sensor Networks

Multiple Target Tracking with Particle Filtering in UWB Rada...

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International Conference on Location and GNSS (ICL-GNSS)

作者： Sobhani, Bita Paolini, Enrico Mazzotti, Matteo Giorgetti, Andrea Chiani, Marco Univ Bologna Dept Elect Elect & Informat Engn DEI I-40126 Bologna Italy

ISBN: (纸本)9781479998586

In this paper, a particle filtering algorithm is proposed for the challenging problem of multiple target tracking in ultra-wideband (UWB) radar sensor networks. The particle weights are derived analytically for the case of one transmitter and several receivers. The performance of the proposed particle algorithm is then evaluated through numerical results with comparison to the well-known Kalman filter. The scenarios account for the spatial configuration of the receivers, propagation effects, presence of clutter and noise.

关键词： multiple target tracking particle filters ultra wideband radar Target tracking Sensor networks clutter particle filtering algorithm Receivers

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particle filter framework for 6D seam tracking under large external forces using 2D laser sensors

Particle filter framework for 6D seam tracking under large e...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Fredrik Bagge Carlson Martin Karlsson Anders Robertsson Rolf Johansson Lund University Dept Automatic Control PO Box 118 SE22100 Sweden

ISBN: (纸本)9781509037636

We provide a framework for 6 DOF pose estimation in seam-tracking applications using particle filtering. The particle filter algorithm developed incorporates measurements from both a 2 DOF laser seam tracker and the robot forward kinematics under an assumed external force. Special attention is paid to modeling of disturbances in the respective measurements, and methods are developed to assist the selection of sensor configurations for optimal estimation performance. The developed estimation algorithm and simulation environment are provided as an open-source, extendable package, written with an intended balance between readability and performance.

关键词： Seam tracking particle filtering algorithm particle filters Laser sensors Simulated Environment intelligibility Frameworks DEPTH OF FIELD Force laser seam

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Tracking Feedback System of Golf Robotic Caddie Based on the Binocular Vision

Tracking Feedback System of Golf Robotic Caddie Based on the...

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World Congress on Intelligent Control and Automation

作者： Yuejuan Tang Jing Xu Ming Fang Changchun University of Science and Technology

ISBN: (纸本)9781467384155

In this paper, tracking feedback system of Golf Robotic Caddie based on the binocular stereo vision is proposed, and we use the improved particle filter algorithm to track specific dynamic golfer in real-time, then we keep the robot autonomous motion under control by the feedback system of control. The experiment results show that the improved algorithm used on 30 continuous images needs 1.1270s. It improves processing efficiency of the algorithm and robustness. Furthermore, using the tracking feedback system, the robot can follow target preliminarily.

关键词： Feedback Stereoviews particle filtering algorithm Golf Binocular vision algorithm efficiency tracking

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Adaptive particle filtering in Data Assimilation of Wildfire Spread Simulation 15

Adaptive Particle Filtering in Data Assimilation of Wildfire...

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Simulation Multiconference

作者： Feng Gu Mohammad Butt Chunyu Ai Xiaoke Shen Jiehao Xiao College of Staten Island CUNY University of South Carolina Upstate The Graduate Center CUNY

ISBN: (纸本)9781510810594

Data assimilation techniques can be used to improve the estimation of the wildfire spread simulation by assimilating the real time data into the simulation models, such as sequential Monte Carlo (SMC) methods. The standard SMC methods employ the same number of particles and don't utilize the runtime state inference particle convergence characterization information during the execution of the algorithm. Therefore, a large number of particles are required to ensure that the particles can converge to the true posterior of the system state. For such large-scale spatial temporal systems as wildfire spread simulation, the system's state is large and its behaviors are heterogeneous in various areas. This heterogeneous and dynamic behavior makes the uncertainty of state inference dynamically change. In this paper, we design the adaptive particle filtering algorithm to dynamically adjust the number of particles based on the quantified uncertainty of state inference, thus to achieve more efficient simulation results.

关键词： Data assimilation Sequential Monte Carlo methods Wildfire spread State inference Quantification Data assimilation particle filters Monte Carlo technique particle filtering algorithm numerical invariant Wildfire simulating particles

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