A multi-target tracking algorithm based on Gaussian mixture model

doi:10.23919/JSEE.2020.000020

Journal of Systems Engineering and Electronics ›› 2020, Vol. 31 ›› Issue (3): 482-487.doi: 10.23919/JSEE.2020.000020

• Electronics Technology • Previous Articles Next Articles

A multi-target tracking algorithm based on Gaussian mixture model

Lili SUN^1,²(), Yunhe CAO^1,*(), Wenhua WU¹(), Yutao LIU²()

¹ National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China
² Science and Technology on Communication Networks Laboratory, Shijiazhuang 050081, China

Received:2019-04-30 Online:2020-06-30 Published:2020-06-30
Contact: Yunhe CAO E-mail:18729215385@163.com;caoyunhe@mail.xidian.edu.cn;wuwhxidian@163.com;abcqly@gmail.com
About author:SUN Lili was born in 1995. She received her B.S. degree in electronic information science and technology from Qingdao University of Science and Technology, Qingdao, China, in 2017. She is a master degree candidate in the National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China. Her research interest is multi-target tracking. E-mail: 18729215385@163.com|CAO Yunhe was born in 1978. He received his B.S. degree in technique of measuring control and instrument engineering from Xidian University, Xi'an, China, in 2001, and M.S. and Ph.D. degrees in electrical engineering from Xidian University, Xi'an, China, in 2004 and 2006, respectively. He is currently a professor with the National Laboratory of Radar Signal Processing, Xidian University. His research interests include array signal processing, MIMO radar, and wideband radar signal processing. E-mail: caoyunhe@mail.xidian.edu.cn|WU Wenhua was born in 1991. He received his B.S. degree in mathematics from Xidian University, Xi'an, China, in 2014. He is currently working toward his Ph.D. degree in the National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China. His research interests include MIMO radar, radar and communication integration. E-mail: wuwhxidian@163.com|LIU Yutao was born in 1981. He received his B.S. degree from Harbin Institute of Technology, Harbin, China, in 2005. He received his M.S. and Ph.D. degrees from Harbin Institute of Technology, Harbin, China, in 2007 and 2010, respectively. He is currently a senior engineer working at the Science and Technology on Communication Networks Laboratory in Shijiazhuang, China. His research interests include cognitive radios and communication networks. E-mail: abcqly@gmail.com
Supported by:
the National Natural Science Foundation of China(61771367);the Science and Technology on Communication Networks Laboratory(HHS19641X003);This work was supported by the National Natural Science Foundation of China (61771367) and the Science and Technology on Communication Networks Laboratory (HHS19641X003)

Abstract

Abstract:

Since the joint probabilistic data association (JPDA) algorithm results in calculation explosion with the increasing number of targets, a multi-target tracking algorithm based on Gaussian mixture model (GMM) clustering is proposed. The algorithm is used to cluster the measurements, and the association matrix between measurements and tracks is constructed by the posterior probability. Compared with the traditional data association algorithm, this algorithm has better tracking performance and less computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithm.

Key words: multiple-target tracking, Gaussian mixture model (GMM), data association, expectation maximization (EM) algorithm.

Lili SUN, Yunhe CAO, Wenhua WU, Yutao LIU. A multi-target tracking algorithm based on Gaussian mixture model[J]. Journal of Systems Engineering and Electronics, 2020, 31(3): 482-487.

Figures/Tables 7

Fig 1

Fig 2

Table 1

Fig 3

Fig 4

Table 2

Table 3

References 22

1	SHI Z S, XIAO S, XING C F. An improved data association algorithm for multiple-target tracking. Proc. of the International Conference on Computational and Information Sciences, 2010, 553- 556.
2	KIM H, CHUN J, RYU C, et al. JPDAS multi-target tracking algorithm for cluster bombs tracking. The Journal of Korean Institute of Electromagnetic Engineering and Science, 2016, 27 (6): 545- 556. doi: 10.5515/KJKIEES.2016.27.6.545
3	AMROUCHE N, KHENCHAF A, BERKANI D, et al. Multiple target tracking using track before detect algorithm. Proc. of the International Conference on Electromagnetics in Advanced Applications, 2017, 692- 695.
4	ASAD M, KHAN S, ARIF M, et al. Ballistic missile tracking in the presence of clutter using multi-target tracking algorithm. Proc. of the 14th International Bhurban Conference on Applied Sciences and Technology, 2017, 357- 360.
5	SINGER R A, SEA R G, HOUSEWRIGHT K B. New results in optimizing surveillance system tracking and data correlation performance in dense multi-target environments. IEEE Trans. on Automatic Control, 1973, 18 (6): 571- 582. doi: 10.1109/TAC.1973.1100421
6	BAR-SHALOM Y, TSE E. Tracking in a cluttered environment with probabilistic data association. Automatica, 1975, 11 (9): 451- 460.
7	FORTMANN T, BAR-SHALOM Y, SCHEFFE M. Sonar tracking of multiple targets using joint probabilistic data association. IEEE Journal of Oceanic Engineering, 1983, 8 (3): 173- 184. doi: 10.1109/JOE.1983.1145560
8	KOSUGE Y, TACHIBANA Y, TSUJIMICHI S. A track-oriented multiple hypothesis multitarget tracking algorithm. Electronics & Communications in Japan, 1999, 82 (12): 84- 92.
9	QIN W H, HU F, QIN C Y. Improving JPDA (joint probabilistic data association) algorithms. Journal of Northwestern Polytechnical University, 2005, 23 (2): 140- 143.
10	LI W, HAN C Z, YAN X X, et al. Adaptive sequential Monte Carlo implementation of the PHD filter for multi-target tracking. Proc. of the 6th International Conference on Information Fusion, 2003, 792- 799.
11	ASHRAF M A, TUMMALA M, CRISTI R. Fuzzy logic data correlation approach in multisensor-multitarget tracking systems. Journal of Signal Processing, 1999, 76 (2): 195- 209. doi: 10.1016/S0165-1684(99)00008-0
12	HAO L. A possibilistic data association based algorithm for multi-target tracking. Proc. of the International Conference on Intelligent Systems Design and Engineering Applications, 2013, 158- 162.
13	KAYAL S. Face clustering in videos: GMM-based hierarchical clustering using spatio-temporal data. Proc. the 13th UK Workshop on Computational Intelligence, 2013, 272- 278.
14	SONMEZ H H, TURGUT K, HOCAOGLU A K, et al. Improving track initiation performance by track validation algorithms for multi-target tracking in heavy clutter. Proc. of the Signal Processing and Communications Applications Conference, 2017, 1- 4.
15	SHI Z F, XU X H. Near and supersonic target tracking algorithm based on adaptive Kalman filter. Proc. of the International Conference on Intelligent Human-machine Systems and Cybernetics, 2016, 418- 421.
16	PAN S S, BAO Q L, CHEN Z P, et al. An efficient TO-MHT algorithm for multi-target tracking in cluttered environment. Proc. of the IEEE Advanced Information Technology Electronic and Automation Control Conference, 2017, 705- 708.
17	HUANG D Y, WANG D, WU S F, et al. Variable-numbered multi-target tracking algorithm based on universal track initiation model with adaptive threshold. Proc. of the 35th Chinese Control Conference, 2016, 5687- 5692.
18	WANG Y H, WANG J K, WANG B. A modified multi-target tracking algorithm based on joint probability data association and Gaussian particle filter. Proc. of the 11th World Congress on Intelligent Control and Automation, 2015, 2500- 2504.
19	NEAGOE V E, CHIRILA-BERBENTEA V. Improved Gaussian mixture model with expectation-maximization for clustering of remote sensing imagery. Proc. of the IEEE International Geoscience and Remote Sensing Symposium, 2016, 3063- 3065.
20	LI Y, LI L. A novel split and merge EM algorithm for Gaussian mixture model. Proc. of the 5th International Conference on Natural Computation, 2009, 479- 483.
21	WATANABE H, MURAMATSU S, KIKUCHI H. Interval calculation of EM algorithm for GMM parameter estimation. Proc. of the IEEE International Symposium on Circuits and Systems, 2010, 2686- 2689.
22	AHALYA G, PANDEY H M. Data clustering approaches survey and analysis. Proc. of the International Conference on Futuristic Trends on Computational Analysis and Knowledge Management, 2015, 532- 537.

Target	Position/km		Speed/(km/s)
Target	$x$	$y$	$V_x$	$V_y$
1	2.9	2.9	0.72	0.42
2	3.0	7.9	0.72	0.22
3	10.2	2.2	0.45	0.60
4	5.8	10.2	0.62	0.25

Target	GMM	JPDA	SJPDA
1	0.109 1	0.139 2	0.181 3
2	0.111 0	0.117 8	0.148 1
3	0.128 1	0.124 6	0.165 3
4	0.097 3	0.105 4	0.215 7

Algorithm	GMM	JPDA	SJPDA
Average running time/s	12.698 5	27.471 8	14.445 7

[1]	Mahmoudreza HADAEGH, Hamid KHALOOZADEH, Mohammadtaghi BEHESHTI. Augmented input estimation in multiple maneuvering target tracking [J]. Journal of Systems Engineering and Electronics, 2019, 30(5): 841-851.
[2]	Weihua Wu, Jing Jiang, and Yang Wan. Tracking a maneuvering target in clutter with out-of-sequence measurements for airborne radar [J]. Journal of Systems Engineering and Electronics, 2015, 26(4): 746-.
[3]	Xiaoyan Fu, Yingmin Jia, and Xiaohe Liu. Optimal set integer programming algorithm for multiple maneuvering targets tracking in clutter [J]. Journal of Systems Engineering and Electronics, 2013, 24(4): 555-563.
[4]	Li Zhou, Hailin Zou, Yancun Yang, and Qian Gao. Troubleshooting algorithm for solving assignment problem and its applications [J]. Journal of Systems Engineering and Electronics, 2013, 24(1): 165-172.
[5]	Liu Zongxiang, Xie Weixin & Huang Jingxiong. New computing method of weighted coefficients for tracking a maneuvering target using PDAF in the presence of clutter [J]. Journal of Systems Engineering and Electronics, 2009, 20(5): 968-973.
[6]	Kang Li, Xie Weixin & Huang Jingxiong. New data association technique based on ACO with directional information considered [J]. Journal of Systems Engineering and Electronics, 2008, 19(6): 1283-1286.
[7]	Guo Lei, Tang Bin, Liu Gang & Xiao Fei. Posterior Cramer-Rao lower bounds for multitarget bearings-only tracking [J]. Journal of Systems Engineering and Electronics, 2008, 19(6): 1127-1132.
[8]	Ba Hongxin, Cao Lei, He Xinyi & Cheng Qun. Modified joint probabilistic data association with classification-aided for multitarget tracking [J]. Journal of Systems Engineering and Electronics, 2008, 19(3): 434-439.
[9]	Guo Lei, Tang Bin & Liu Gang. Data association based on target signal classification information [J]. Journal of Systems Engineering and Electronics, 2008, 19(2): 246-251.
[10]	Fang Bingyi & Wu Siliang. Multiple targets vector miss distance measurement accuracy based on 2-D assignment algorithms [J]. Journal of Systems Engineering and Electronics, 2008, 19(1): 76-80.
[11]	Wang Jianguo, He Peikun & Cao Wei. Study on the Hungarian algorithm for the maximum likelihood data association problem [J]. Journal of Systems Engineering and Electronics, 2007, 18(1): 27-32.

A multi-target tracking algorithm based on Gaussian mixture model

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

Share this article

Figures/Tables 7

References 22

Related Articles 11

Recommended Articles

Metrics

Comments