scholarly journals Bearing‐only underwater uncooperative target tracking for non‐Gaussian environment using fast particle filter

2021 ◽  
Author(s):  
Xianghao Hou ◽  
Long Yang ◽  
Yixin Yang ◽  
Jianbo Zhou ◽  
Gang Qiao
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Fast Particle ◽  
Non Gaussian

Dynamic Target Tracking Based on Particle Filter in Actual Environment

2013 ◽  
Vol 683 ◽  
pp. 824-827
Author(s):  
Tian Ding Chen ◽  
Chao Lu ◽  
Jian Hu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Monitoring System ◽  
Filter Method ◽  
Complex Scene ◽  
Actual Environment ◽  
Dynamic Target ◽  
Gaussian System ◽  
Non Gaussian

With the development of science and technology, target tracking was applied to many aspects of people's life, such as missile navigation, tanks localization, the plot monitoring system, robot field operation. Particle filter method dealing with the nonlinear and non-Gaussian system was widely used due to the complexity of the actual environment. This paper uses the resampling technology to reduce the particle degradation appeared in our test. Meanwhile, it compared particle filter with Kalman filter to observe their accuracy .The experiment results show that particle filter is more suitable for complex scene, so particle filter is more practical and feasible on target tracking.

Particle filter initialization in non-linear non-Gaussian radar target tracking

2007 ◽  
Vol 18 (3) ◽  
pp. 491-496 ◽  
Author(s):  
Wang Lian ◽  
Jin Yonggao ◽  
Dai Dingzhang ◽  
Dong Huachun ◽  
Quan Taifan
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Radar Target ◽  
Non Linear ◽  
Non Gaussian

A Novel Modified Particle Filter Algorithm

2011 ◽  
Vol 204-210 ◽  
pp. 1895-1899
Author(s):  
Qing Hua Gao ◽  
Jie Wang ◽  
Ming Lu Jin
Keyword(s):  
Probability Distribution ◽  
Particle Filter ◽  
State Estimation ◽  
Target Tracking ◽  
Optimization Method ◽  
Effective Solution ◽  
Optimal Probability ◽  
Particle Filter Algorithm ◽  
Non Gaussian ◽  
Filtering Problem

The particle filter (PF) algorithm provides an effective solution to the non-linear and non-Gaussian filtering problem. However, when the motion noises or observation noises are strong, the degenerate phenomena will occur, which leads to poor estimation. In this paper, we propose a modified particle filter (MPF) algorithm for improving the estimated precision through a particle optimization method. After calculating the coarse estimation with the traditional PF, we optimize the particles according to their weights and relative positions, then, move the particles toward the optimal probability distribution. The state estimation and target tracking experiments demonstrate the outstanding performance of the proposed algorithm.

The Particle Filter Algorithm Research for Target Tracking Based on Information Fusion

2012 ◽  
Vol 628 ◽  
pp. 440-444 ◽  
Author(s):  
Juan Li ◽  
Hui Juan Hao ◽  
Mao Li Wang
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Information Fusion ◽  
Particle Filtering ◽  
Tracking System ◽  
Distributed Sensor ◽  
Multi Target Tracking ◽  
Particle Filter Algorithm ◽  
Gaussian System ◽  
Non Gaussian

This paper researches the particle filters Algorithms for target tracking based on Information Fusion, it combines the traditional Kalman filter with the particle filter. For multi-sensor and multi-target tracking system with complex application background, which is nonlinear and non-gaussian system, the paper proposes an effective particle filtering algorithm based on information fusion for distributed sensor, this algorithm contributes to the solution of particle degradation problems and the phenomenon of particle lack, and achieve high precision for target tracking.

ALGORITHM OF IMM COMBINING KALMAN AND PARTICLE FILTER FOR MANEUVERING TARGET TRACKING

2006 ◽  
Vol 03 (04) ◽  
pp. 321-328
Author(s):  
GUIXI LIU ◽  
ENKE GAO ◽  
CHUNYU FAN
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Nonlinear Problems ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Maneuvering Target Tracking ◽  
Maneuvering Target ◽  
Computation Efficiency ◽  
Non Gaussian

The particle filter can deal with nonlinear/non-Gaussian problems and it has been introduced to the algorithm of the interacting multiple model (IMM) for higher precision. The general IMM based on Kalman filter or extended Kalman filter (IMMEKF) cannot deal with non-Gaussian problems and also does not work as well as the IMM based on the particle filter (IMMPF) for the nonlinear problems. However the problem of the particle filter is its expensive computation, because a particle filter usually has a lot of particles, which will increase the computation load greatly. Here an algorithm of IMM combining the Kalman filter and the particle filter (IMMK-PF) for maneuvering target tracking is proposed to improve the computation efficiency as compared to the IMMPF. For nonlinear/Gaussian problems the new algorithm is expected to have a good performance as the IMMPF, while for linear problems it will perform similarly to the IMMEKF and will work better than the IMMPF.

scholarly journals Probabilistic Data Association-Feedback Particle Filter for Multiple Target Tracking Applications

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Tao Yang ◽  
Prashant G. Mehta
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Data Association ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Particle Filter Algorithm ◽  
Gaussian Case ◽  
Non Gaussian

This paper is concerned with the problem of tracking single or multiple targets with multiple nontarget-specific observations (measurements). For such filtering problems with data association uncertainty, a novel feedback control-based particle filter algorithm is introduced. The algorithm is referred to as the probabilistic data association-feedback particle filter (PDA-FPF). The proposed filter is shown to represent a generalization—to the nonlinear non-Gaussian case—of the classical Kalman filter-based probabilistic data association filter (PDAF). One remarkable conclusion is that the proposed PDA-FPF algorithm retains the error-based feedback structure of the classical PDAF algorithm, even in the nonlinear non-Gaussian case. The theoretical results are illustrated with the aid of numerical examples motivated by multiple target tracking (MTT) applications.

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