Constrained auxiliary particle filtering for bearings-only maneuvering target tracking

doi:10.21629/JSEE.2019.04.06

Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (4): 684-695.doi: 10.21629/JSEE.2019.04.06

收稿日期:2018-11-19 出版日期:2019-08-01 发布日期:2019-08-29

Constrained auxiliary particle filtering for bearings-only maneuvering target tracking

Hongwei ZHANG*(), Weixin XIE()

Automatic Target Recognition Key Laboratory, Shenzhen University, Shenzhen 518060, China

Received:2018-11-19 Online:2019-08-01 Published:2019-08-29
Contact: Hongwei ZHANG E-mail:hongweiz@szu.edu.cn;wxxie@szu.edu.cn
About author:ZHANG Hongwei was born in 1982. She is currently pursuing her Ph.D. degree in the College of Information Engineering at Shenzhen University. She received her B.S. degree in electronic engineering from Zhengzhou University in 2006 and M.S. degree from South China University of Technology in 2013. Her research interests are particle filtering and multiple target tracking. E-mail:hongweiz@szu.edu.cn|XIE Weixin was born in 1941. He received his B.S. degree from Xidian University, Xi'an, and joined the faculty of Xidian University in 1965. From 1981 to 1983, he was a visiting scholar with the University of Pennsylvania, USA. In 1989, he was invited to University of Pennsylvania as a visiting professor. He is currently a professor with Shenzhen University. His research interests include intelligent information processing and pattern recognition. E-mail:wxxie@szu.edu.cn
Supported by:
the National Natural Science Foundation of China(61773267);the Shenzhen Fundamental Research Project(JCYJ20170302145519524);the Shenzhen Fundamental Research Project(20170818102503604);This work was supported by the National Natural Science Foundation of China (61773267) and the Shenzhen Fundamental Research Project (JCYJ20170302145519524; 20170818102503604)

摘要/Abstract

Abstract:

To track the nonlinear, non-Gaussian bearings-only maneuvering target accurately online, the constrained auxiliary particle filtering (CAPF) algorithm is presented. To restrict the samples into the feasible area, the soft measurement constraints are implemented into the update routine via the $\ell$₁ regularization. Meanwhile, to enhance the sampling diversity and efficiency, the target kinetic features and the latest observations are involved into the evolution. To take advantage of the past and the current measurement information simultaneously, the sub-optimal importance distribution is constructed as a Gaussian mixture consisting of the original and modified priors with the fuzzy weighted factors. As a result, the corresponding weights are more evenly distributed, and the posterior distribution of interest is approximated well with a heavier tailor. Simulation results demonstrate the validity and superiority of the CAPF algorithm in terms of efficiency and robustness.

Key words: bearings-only maneuvering target tracking, soft measurement constraints, constrained auxiliary particle filtering (CAPF)

. [J]. Journal of Systems Engineering and Electronics, 2019, 30(4): 684-695.

Hongwei ZHANG, Weixin XIE. Constrained auxiliary particle filtering for bearings-only maneuvering target tracking[J]. Journal of Systems Engineering and Electronics, 2019, 30(4): 684-695.

图/表 10

参考文献 35

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Algorithm	RMSE		RTAMSE/km
Algorithm	Mean/km	Variance/km$^{2}$	RTAMSE/km
EKF	0.159	0.016	0.489
UKF	0.611	0.377	2.200
PF-EKF	0.331	0.138	0.451
PF-UKF	0.187	0.023	0.647
ATPF	0.104	0.006	0.789
CAPF	0.033	0.008	0.168

Algorithm	RMSE		RTAMSE/km
Algorithm	Mean/km	Variance/km$^{2}$	RTAMSE/km
EKF	0.103	0.004	0.365
UKF	0.241	0.073	0.575
PF-EKF	0.254	0.066	0.389
PF-UKF	0.137	0.016	0.437
ATPF	0.079	0.002	0.490
CAPF	0.031	0.001	0.167

Algorithm	RMSE		RTAMSE/km
Algorithm	Mean/km	Variance/km$^{2}$	RTAMSE/km
EKF	0.081	0.004	0.413
UKF	0.081	0.007	0.385
PF-EKF	0.111	0.007	0.449
PF-UKF	0.090	0.001	0.443
ATPF	0.100	0.005	0.707
CAPF	0.048	0.001	0.168

Algorithm	EKF	UKF	EPF	UPF	ATPF	CAPF
Time/s	3.03	17.81	78.24	64.83	17.68	19.49

Algorithm	RMSE		RTAMSE/km
Algorithm	Mean/km	Variance/km$^{2}$	RTAMSE/km
EKF	0.039	0.001	0.143
UKF	0.052	0.002	0.146
PF-EKF	0.068	0.006	0.152
PF-UKF	0.034	0.000 5	0.140
ATPF	0.030	0.000 4	0.202
CAPF	0.032	0.000 8	0.198

Constrained auxiliary particle filtering for bearings-only maneuvering target tracking

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Algorithm	RMSE		RTAMSE/km
Algorithm	Mean/km	Variance/km$^{2}$	RTAMSE/km
EKF	0.189	0.086	0.667
UKF	0.616	0.371	2.021
PF-EKF	0.210	0.011	0.698
PF-UKF	0.331	0.102	1.035
ATPF	0.205	0.022	1.317
CAPF	0.051	0.001	0.281