一种新的未知杂波环境下的PHD滤波器

doi:10.3969/j.issn.1001-2400.2014.05.004

Abstract

Abstract:

Aiming at improving the poor performance of the Probability Hypothesis Density(PHD) filter when the clutter model and the prior knowledge are mismatched, a novel PHD filter into which we introduce the augmented state space and which is used under the unknown clutter circumstance is proposed in this paper. The proposed filter can distinguish the target state space and the clutter state space by the augmented state space. Using the estimate of the unknown clutter model from the measurement, the filter can avoid the tracking performance reduction caused by the improper model selection of the unknown clutter. Simulation results show that the proposed algorithm can achieve a stable tracking performance under the unknown clutter circumstance and a tracking accuracy equal to that of the conventional PHD filter used in the unknown clutter circumstance in the real-time context.

Key words: multitarget tracking, probability hypothesis density, unknown clutter, augmented state space

CLC Number:

TN953

LI Cuiyun;JIANG Zhou;JI Hongbing. Novel PHD filter in unknown clutter environment[J].J4, 2014, 41(5): 18-23.

References

［1］王宝树, 李芳社. 基于数据融合技术的多目标跟踪算法研究［J］. 西安电子科技大学学报, 1998, 25(3): 269-272.
Wang Baoshu, Li Fangshe. The Research on Multiple Targets Tracking Based on the Data Fusion Technique［J］. Journal of Xidian University, 1998, 25(3): 269-272.
［2］ Mahler R. Statistical Multisource-multitarget Information Fusion［M］. Boston: Artech House Publishers, 2007.
［3］杨柏胜, 姬红兵, 高小东. 随机集粒子滤波的快速被动数据关联算法［J］. 西安电子科技大学学报, 2010, 37(4): 655-659.
Yang Baisheng, Ji Hongbing, Gao Xiaodong. Fast Passive Data Association Algorithm Base on the Random Set Particle Filter［J］. Journal of Xidian University, 2010, 37(4): 655-659.
［4］ Lian Feng, Han Chongzhao, Liu Weifeng. Estimating Unknown Clutter Intensity for PHD Filter［J］. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(4): 2066-2078.
［5］ Zhou Xin, Jiang Ju, Wang Congqing. Maximum Likelihood Parameter Estimation for G0 Distribution via EM Algorithm［C］//Proceedings of the 10th International Bhurban Conference on Applied Sciences and Technology. Washington: IEEE, 2013: 89-93.
［6］ Watanabe R, Okamura H, Dohi T. An Efficient MCMC Algorithm for Continuous PH Distributions［C］//Proceedings of the Winter Simulation Conference. New York: IEEE, 2012: 1-12.
［7］ Mahler R, El-Fallah A. CPHD and PHD Filters for Unknown Backgrounds, Part Ⅲ: Tractable Multitarget Filtering in Dynamic Clutter［C］//Proceedings of SPIE: 7698. Bellingham: SPIE, 2010: 76980F.
［8］ Mahler R, Vo B T, Vo B N. CPHD Filtering with Unknown Clutter Rate and Detection Profile［J］. IEEE Transactions on Signal Processing, 2011, 59(8): 3497-3513.
［9］ Zheng Xuetao, Song Liping. Improved CPHD Filtering with Unknown Clutter Rate［C］//Proceedings of the 10th World Congress on Intelligent Control and Automation. Piscataway: IEEE, 2012: 4326-4331.
［10］ Streit R, Stone L. Bayes Derivation of Multitarget Intensity Filters［C］//Proceedings of the International Conference on Information Fusion. Piscataway: IEEE, 2008: 4632414.
［11］ Streit R. Poisson Point Processes: Imaging, Tracking, and Sensing［M］. Heidelberg: Springer, 2010.
［12］ Schikora M, Gning A, Mihaylova L, et al. Box-particle Intensity Filter［C］//9th IET Data Fusion and Target Tracking Conference. Stevenage: IET, 2012: 1-6.
［13］ Schikora M, Koch W, Streit R, et al. Advances in Intelligent Signal Processing and Data Mining［M］. Heidelberg: Springer, 2013: 55-87.
［14］ Vo B N, Singh S, Doucet A. Sequential Monte Carlo Methods for Multitarget Filtering with Random Finite Sets［J］. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1224-1245.
［15］ He Xiaofan, Tharmarasa R, Kirubarajan T, et al. A Track Quality Based Metric for Evaluating Performance of Multitarget Filters［J］. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(1): 610-616.

[1]	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.
[2]	LI Cuiyun;WANG Jingyi;JI Hongbing. Extended target tracking based on CPHD with Gaussian process regression [J]. Journal of Xidian University, 2017, 44(3): 8-12+30.
[3]	LI Cuiyun;WANG Jingyi;JI Hongbing;WANG Rong. CPHD multi-target tracking algorithm with unknown model parameters [J]. Journal of Xidian University, 2017, 44(2): 37-41+184.
[4]	LI Cuiyun;JIANG Zhou;LI Bin;ZHOU Xuan. Gaussian mixture PHD smoothing filter in unknown clutter [J]. J4, 2015, 42(5): 98-104.
[5]	HU Zijun;ZHANG Linrang;ZHAO Shanshan;WANG Jingli. Joint initialization of high-speed targets via multistatic passive-radar measurements and its tracking algorithm [J]. J4, 2014, 41(6): 25-30+110.
[6]	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.
[7]	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.

Novel PHD filter in unknown clutter environment

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 7

Metrics

Comments

Recommended 0