鲁棒自适应的机载外辐射源雷达多目标跟踪算法

doi:10.12305/j.issn.1001-506X.2024.09.02

摘要/Abstract

摘要：

针对未知杂波环境下机载外辐射源雷达的多目标跟踪问题, 提出一种鲁棒自适应的标签多伯努利滤波器。首先基于标签多伯努利滤波器算法框架对多目标跟踪问题进行建模, 然后针对目标新生参数、杂波参数以及目标检测概率未知的问题, 提出采用量测驱动的目标新生模型和基于势均衡多目标多伯努利估计器的在线参数估计方法, 最后考虑到机载外辐射源雷达量测的非线性, 采用序贯蒙特卡罗方法对所提算法进行实现。实验结果表明, 所提滤波器能够利用外辐射源量测准确估计多目标航迹, 且在未知杂波环境下的性能可以逼近杂波参数已知的广义标签多伯努利滤波器, 鲁棒性更好。

关键词: 外辐射源雷达, 多目标跟踪, 鲁棒跟踪, 标签多伯努利滤波器, 随机有限集

Abstract:

In order to address the multi-target tracking problem of airborne passive bisstatic radar (APBR) in an unknown clutter environment, a robust adaptive labelled multi-Bernoulli (RA-LMB) filter is proposed. Firstly, a model for multi-target tracking problem is established on the basis of the LMB filter algorithm framework. Then, for the problems of unknown target newborn parameters, clutter parameters and target detection probability, the measurement-driven target newborn model and the online parameter estimation method based on the cardinality-balanced multi-target multi-Bernoulli estimator are proposed. Finally, considering the non-linearity of APBR measurements, the sequential Monte Carlo method is used to implement the proposed algorithm. The experimental results show that the proposed filter is able to estimate the multi-target trajectory using the APBR measurements, and the performance in the unknown clutter environment can be approximated to the generalized LMB filter with known clutter parameters, with better robustness.

Key words: passive bistatic radar, multi-target tracking, robust tracking, labeled multi-Bernoulli (LMB) filter, random finite set (RFS)

中图分类号:

TN957.51

单靖原, 卢雨, 凌寒羽. 鲁棒自适应的机载外辐射源雷达多目标跟踪算法[J]. 系统工程与电子技术, 2024, 46(9): 2902-2915.

Jingyuan SHAN, Yu LU, Hanyu LING. Robust adaptive multi-target tracking algorithm for airborne passive bistatic radar[J]. Systems Engineering and Electronics, 2024, 46(9): 2902-2915.

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仿真对象	初始状态	存活时段/s
目标1	(-14 950 m, 150 m/s, 12 550 m, -65 m/s)	1~90
目标2	(-15 050 m, 120 m/s, 12 450 m, -15 m/s)	5~100
目标3	(10 550 m, -25 m/s, 16 550 m, 100 m/s)	10~90
目标4	(10 450 m, -130 m/s, 16 550 m, -85 m/s)	10~75
目标5	(5 950 m, -100 m/s, 4 450 m, 120 m/s)	15~80
目标6	(6 050 m, -140 m/s, 4 450 m, 80 m/s)	20~75
目标7	(-12 450 m, 150 m/s, 5 950 m, 80 m/s)	30~90
目标8	(-12 550 m, 120 m/s, 6 050 m, 20 m/s)	50~100
T1	(0 m, 0 m/s, 18 000 m, 0 m/s)	-
T2	(15 000 m, 0 m/s, 10 000 m, 0 m/s)	-
T3	(-15 000 m, 0 m/s, 1 000 m, 0 m/s)	-
R	(-10 000 m, 200 m/s, 1 000 m, 10 m/s)	-

算法	杂波率估计平均值	检测概率估计平均值
RA-LMB	37.67	0.89
DP-GLMB	36.75	0.86

仿真对象	初始状态	转弯率(rad/s)	存活时段/s
目标1	(-14 950 m, 150 m/s, 12 550 m, -65 m/s)	π/1 080	1~90
目标2	(-15 050 m, 120 m/s, 12 450 m, -15 m/s)	π/360	5~100
目标3	(10 550 m, -25 m/s, 16 550 m, -100 m/s)	π/1 080	10~90
目标4	(10 450 m, -130 m/s, 16 550 m, -85 m/s)	-π/360	10~75
目标5	(5 950 m, -100 m/s, 4 450 m, 120 m/s)	-π/1 080	15~80
目标6	(6 050 m, -140 m/s, 4 450 m, 80 m/s)	π/270	20~75
目标7	(-12 450 m, 150 m/s, 5 950 m, 80 m/s)	-π/540	30~90
目标8	(-12 550 m, 120 m/s, 6 050 m, 120 m/s)	π/180	50~100
T1	(0 m, 0 m/s, 18 000 m, 0 m/s)	—	-
T2	(15 000 m, 0 m/s, 10 000 m, 0 m/s)	—	-
T3	(-15 000 m, 0 m/s, 1 000 m, 0 m/s)	—	-
R	(-10 000 m, 200 m/s, 1 000 m, 10 m/s)	—	-

算法	杂波率估计平均值	检测概率估计平均值
RA-LMB	36.81	0.89
DP-GLMB	36.61	0.88

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