并行高斯粒子滤波被动多目标跟踪新算法

doi:10.3969/j.issn.1001-2400.2011.01.019

Abstract

Abstract:

In multi-target tracking, aiming at the data association problem and the curse of dimensionality that arises due to the increased size of the state-space associated with multiple targets, a novel algorithm based on the parallel Gaussian particle filter (parallel-GPF) is proposed. By assuming that the states of the targets conditioned on the past measurements are mutually independent, the algorithm is amenable for multi-channel parallel implementation. With the Markov chain Monte Carlo (MCMC) approach the marginal association probabilities can be evaluated. Then, the filtering distributions of each target are computed independently with GPF. And, Quasi Monte Carlo (QMC) integration is utilized for approximating the prediction and update distributions. Finally, the proposed method is applied to passive multi-sensor multi-target tracking. Simulation results show that the proposed algorithm can obtain a better tracking performance than JPDAF, MCMCDAF and MC-JPDAF.

Key words: multi-target tracking, data association, Gaussian particle filter, Markov chain Monte Carlo

ZHANG Jungen;JI Hongbing;CAI Shaoxiao. Parallel Gaussian particle filter for passive multi-target tracking[J].J4, 2011, 38(1): 117-122.

References

[1] Bar-Shalom Y, Li X R. Multitarget-Multisensor Tracking: Principles and Techniques[M]. Storrs: YBS Publishing, 1995.
[2] 刘进忙, 姬红兵, 左涛. 纯方位观测的航迹不变量目标跟踪方法[J]. 西安电子科技大学学报, 2008, 35(1):49-53.
Liu Jinmang, Ji Hongbing, Zuo Tao. Trajectory Invariable-information Target Tracking Algorithm with Bearing-only Measurement[J]. Journal of Xidian University, 2008, 35(1):49-53.
[3] Bar-Shalom Y, Fortmann T. Tracking and Data Association[M]. San Diego: Academic Press, 1988.
[4] Musicki D, Suvorova S. Tracking in Clutter Using IMM-IPDA-Based Algorithms[J]. IEEE Trans on Aerospace and Electronic Systems, 2008, 44(1):111-126.
[5] 潘泉, 叶西宁, 张洪才. 广义概率数据关联算法[J]. 电子学报, 2005, 33(3):467-472.
Pan Quan, Ye Xining, Zhang Hongcai. Generalized Probability Data Association Algorithm[J]. Acta Electronica Sinica, 2005, 33(3):467-472.
[6] Li Liangqun, Ji Hongbing, Gao Xinbo. Maximum Entropy Fuzzy Clustering with Application to Real-Time Target Tracking[J]. Signal Processing, 2006, 86(11):3432-3447.
[7] Oh S, Russell S, Sastry S. Markov Chain Monte Carlo Data Association for Multi-Target Tracking[J]. IEEE Trans on Automatic Control, 2009, 54(3):481-497.
[8] Cong S, Hong L, Wicker D. Markov-chain Monte-Carlo Approach for Association Probability Evaluation[J]. IEE Proc-Control Theory Appl, 2004, 151(2):185-193.
[9] Vermaak J, Godsill S J, Perez P. Monte Carlo Filtering for Multi-Target Tracking and Data Association[J]. IEEE Trans on Aerospace and Electronic Systems, 2005, 41(1):309-332.
[10] Kotecha J H, Djuric P M. Gaussian Particle Filtering[J]. IEEE Trans on Signal Processing, 2003, 51(10):2592-2601.
[11] Xavier L, Alain X, Gerard F, et al. A Quasi-Monte Carlo Integration Method Applied to the Computation of the Pollaczek Integral[J]. IEEE Trans on Power Delivery, 2008, 23(3):1527-1534.

[1]	ZHANG Yichen,SHUI Penglang. Improved probabilistic multi-hypothesis tracker via the Poisson point process [J]. Journal of Xidian University, 2021, 48(2): 27-34.
[2]	HAN Zhuoxi,WANG Feng,CHEN Pei,LI Zhuolun. Fuzzy data association algorithm assisted by historical features [J]. Journal of Xidian University, 2021, 48(2): 92-98.
[3]	LI Cuiyun,LI Yang,JI Hongbing,SHI Renzheng. GLMB extended target tracking based on one-step data association [J]. Journal of Xidian University, 2020, 47(5): 137-143.
[4]	WANG Mingjie,JI Hongbing,LIU Long. Student’s t Distribution mixture CPHD filter with noise outliers [J]. Journal of Xidian University, 2019, 46(5): 134-141.
[5]	LI Cuiyun,CHEN Dongwei,SHI Renzheng. Adaptive target birth δ-generalized labeled multi-Bernoulli filtering algorithm [J]. Journal of Xidian University, 2019, 46(2): 12-16.
[6]	WANG Haihuan;WANG Jun. Multi-target tracking with the cubature Kalman multi-bernoulli filter [J]. Journal of Xidian University, 2016, 43(6): 176-182.
[7]	JIN Biao;SU Tao;LI Yanyan;ZHANG Long. Multi-target tracking algorithm aided by a high resolution range profile [J]. J4, 2016, 43(1): 1-6+29.
[8]	WU Bin；LI Peng. Novel track-before-detect algorithm for small infrared target [J]. J4, 2011, 38(3): 107-113.
[9]	GUO Hui;JI Hong-bing;WU Bin. Quasi-Monte-Carlo Gaussian particle filter based target tracking for the multiple passive sensor [J]. J4, 2010, 37(6): 1042-1047.
[10]	YANG Bai-sheng;JI Hong-bing;GAO Xiao-dong. Fast passive data association algorithm base on the random set particle filter [J]. J4, 2010, 37(4): 655-659.
[11]	ZHANG Jun-gen;JI Hong-bing. Passive multi-target tracking based on independent particle filtering and fuzzy clustering [J]. J4, 2010, 37(4): 639-641.
[12]	SHI Yin-shui;JI Hong-bing;YANG Bai-sheng. Multiple passive-radar based time-varying number targets tracking algorithm [J]. J4, 2010, 37(2): 218-223.
[13]	LI Liang-qun;JI Hong-bing. Fast data association algorithm based on maximum entropy fuzzy clustering [J]. J4, 2006, 33(2): 251-256.
[14]	HE You;YI Xiao;GUAN Xin. A serial dynamic multidimensional assignment algorithm [J]. J4, 2005, 32(3): 489-493.

Parallel Gaussian particle filter for passive multi-target tracking

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 14

Metrics

Comments

Recommended 10