Labeled box-particle CPHD filter for multiple extended targets tracking

doi:10.21629/JSEE.2019.01.06

Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (1): 57-67.doi: 10.21629/JSEE.2019.01.06

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Labeled box-particle CPHD filter for multiple extended targets tracking

Zhibin ZOU(), Liping SONG*(), Xuan CHENG()

School of Electronic Engineering, Xidian University, Xi'an 710071, China

Received:2018-01-24 Online:2019-02-27 Published:2019-02-27
Contact: Liping SONG E-mail:zbzou@stu.xidian.edu.cn;lpsong@xidian.edu.cn;chengxuanxd@163.com
About author:ZOU Zhibin was born in 1994. He is a master degree candidate at the School of Electronic Engineering at Xidian University. His research interests include nonlinear filtering and target tracking. E-mail:zbzou@stu.xidian.edu.cn|SONG Liping was born in 1975. He received his M.Sc. degree in signal processing from Xidian University in 2003. He received his Ph.D. degree in pattern recognition and intelligent systems from Xidian University in 2008. He is currently an associate professor of Xidian University. His research interests include signal processing, target tracking and nonlinear filtering. E-mail:lpsong@xidian.edu.cn|CHENG Xuan was born in 1991. He is a master degree candidate at the School of Electronic Engineering at Xidian University. His research interests include box-particle filter and group target tracking. E-mail:chengxuanxd@163.com

Abstract

Abstract:

In multiple extended targets tracking, replacing traditional multiple measurements with a rectangular region of the nonzero volume in the state space inspired by the box-particle idea is exactly suitable to deal with extended targets, without distinguishing the measurements originating from the true targets or clutter. Based on our recent work on extended box-particle probability hypothesis density (ET-BP-PHD) filter, we propose the extended labeled box-particle cardinalized probability hypothesis density (ET-LBP-CPHD) filter, which relaxes the Poisson assumptions of the extended target probability hypothesis density (PHD) filter in target numbers, and propagates not only the intensity function but also cardinality distribution. Moreover, it provides the identity of individual target by adding labels to box-particles. The proposed filter can improve the precision of estimating target number meanwhile achieve targetso tracks. The effectiveness and reliability of the proposed algorithm are verified by the simulation results.

Key words: extended target, multiple targets tracking, labled boxparticle, cardinalized probability hypothesis density (CPHD)

Zhibin ZOU, Liping SONG, Xuan CHENG. Labeled box-particle CPHD filter for multiple extended targets tracking[J]. Journal of Systems Engineering and Electronics, 2019, 30(1): 57-67.

Figures/Tables 8

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References 22

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Target label	Initial state/(m, m/s, m, m/s)	Start time/s	End time /s
1	(50, 0,400, –15)	1	40
2	(-50, 13, 80, -10)	6	34
3	(-100, 12, -300, 20)	10	30
4	(-200, 14, -40, 26)	13	27

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Labeled box-particle CPHD filter for multiple extended targets tracking

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