Improved pruning algorithm for Gaussian mixture probability hypothesis density filter

doi:10.21629/JSEE.2018.02.02

Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (2): 229-235.doi: 10.21629/JSEE.2018.02.02

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Improved pruning algorithm for Gaussian mixture probability hypothesis density filter

Yongfang NIE^1,²(), Tao ZHANG^1,*()

¹ Department of Automation, Tsinghua University, Beijing 100084, China
² Department of Strategic Missile and Underwater Weapon, Naval Submarine Academy, Qingdao 266071, China

Received:2017-03-07 Online:2018-04-26 Published:2018-04-27
Contact: Tao ZHANG E-mail:nyf14@mails.tsinghua.edu.cn;taozhang@mail.tsinghua.edu.cn
About author:NIE Yongfang was born in 1976. She received her M.S. degree in weapon system engineering from Naval Aeronautical Engineering Institute in 2002. She was a lecturer with Naval Submarine Academy from 2002 to 2014. Now, she is pursuing her Ph.D. degree at Tsinghua University. Her current research activity focuses on nonlinear filtering and adaptive control. E-mail: nyf14@mails.tsinghua.edu.cn|ZHANG Tao was born in 1969. He received his B.S. degree, M.S. degree and Ph.D. degree from Tsinghua University, Beijing, China, in 1993, 1995 and 1999 respectively. He received his second Ph.D. degree from Saga University, Saga, Japan, in 2002. He is currently a professor and the deputy head of the Department of Automation, School of Information Science and Technology, Tsinghua University, Beijing, China. He is the author or coauthor of more than 200 papers and three books. His current research includes robotics, control theory, artificial intelligent, navigation and control of spacecraft, fault diagnosis and reliability analysis, body signal extraction and recognition. E-mail: taozhang@mail.tsinghua.edu.cn
Supported by:
the National Natural Science Foundation of China(61703228);This work was supported by the National Natural Science Foundation of China (61703228)

Abstract

Abstract:

With the increment of the number of Gaussian components, the computation cost increases in the Gaussian mixture probability hypothesis density (GM-PHD) filter. Based on the theory of Chen et al, we propose an improved pruning algorithm for the GM-PHD filter, which utilizes not only the Gaussian components' means and covariance, but their weights as a new criterion to improve the estimate accuracy of the conventional pruning algorithm for tracking very closely proximity targets. Moreover, it solves the end-less while-loop problem without the need of a second merging step. Simulation results show that this improved algorithm is easier to implement and more robust than the formal ones.

Key words: Gaussian mixture probability hypothesis density (GMPHD) filter, pruning algorithm, proximity targets, clutter rate

Yongfang NIE, Tao ZHANG. Improved pruning algorithm for Gaussian mixture probability hypothesis density filter[J]. Journal of Systems Engineering and Electronics, 2018, 29(2): 229-235.

Figures/Tables 5

References 15

1	MAHLER R. Multi-target filtering via first-order multi-target moments. IEEE Trans. on Aerospace and Electronic Systems, 2003, 39(4): 1152-1178.
2	MAHLER R. Advances in statistical multisource-multitarget information fusion. Boston: Artech House, 2014.
3	MAHLER R. PHD filters of higher order in target number. IEEE Trans. on Aerospace and Electronic Systems, 2007, 43(4): 1523-1543.
4	VO B N, SINGH S, DOUCET A. Sequential Monte Carlo implementation of the PHD filter for multi-target tracking. Proc. of the 6th Conference on Information Fusion, 2003: 792-799.
5	VO B N, SINGH S, DOUCET A. Sequential Monte Carlo methods for multi-target filtering with random finite sets. IEEE Trans. on Aerospace and Electronic Systems, 2005, 41(4): 1224-1245.
6	RISTIC B. Particle filters for random set models. New York: Springer, 2013.
7	VO B N, MA W. The Gaussian mixture probability hypothesis density filter. IEEE Trans. on Signal Processing, 2006, 54(11): 4091-4104.
8	WU W H, LIU W, JIANG J, et al. GM-PHD filter-based multitarget tracking in the presence of Doppler blind zone. Digital Signal Processing, 2016, 52 (C): 1- 12.
9	CLARK D. GM-PHD filter multitarget tracking in sonar images. Proc. of Defense and Security Symposium: International Society for Optics and Photonics, 2006, 6235: 62350R-62350R-8.
10	EDMAN V, ANDERSSON M, GRANSTROM K, et al. Pedestrian group tracking using the GM-PHD filter. Proc. of IEEE Signal Processing Conference, 2013: 1-5.
11	ULMKE M, ERDINC O, WILLETT P. GMTI tracking via the Gaussian mixture cardinalized probability hypothesis density filter. IEEE Trans. on Aerospace and Electronic Systems, 2010, 46(4): 1821-1833.
12	CHE L, CHEN Z, YIN F. A novel merging algorithm in Gaussian mixture probability hypothesis density filter for close proximity targets tracking. Journal of Information & Computational Science, 2011, 8 (12): 2283- 2299.
13	SCHUHMACHER D, VO B T, VO B N. A consistent metric for performance evaluation of multi-object filters. IEEE Trans. on Signal Processing, 2008, 56(8): 3447-3457.
14	RISTIC B, VO B N, CLARK D. A metric for performance evaluation of multi-target tracking algorithms. IEEE Trans. on Signal Processing, 2011, 59(7): 3452-3457.
15	MULLANE J, VO B N, ADAMS M, et al. Random finite sets for robot mapping and SLAM. Berlin: Springer, 2011.

Improved pruning algorithm for Gaussian mixture probability hypothesis density filter

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