多源传感器箱粒子LMB滤波算法

doi:10.19665/j.issn1001-2400.20240104

摘要/Abstract

摘要：

随着复杂跟踪场景的大量涌现,常规多源传感器多目标跟踪算法存在计算量大、跟踪精度低、无法估计目标航迹等不足,已无法满足现代战争的需求。笔者以主动传感器和被动传感器组成的多源传感器系统为背景,重点研究多源传感器多目标跟踪问题的实现算法。针对“多主动+多被动”多源传感器系统量测无法充分融合且整体算法计算复杂度较高的问题,提出一种多源传感器箱粒子标签多伯努利(MS-BPF-LMB)滤波算法。首先,对传感器依据不同主动传感器进行分组,即将所有传感器划分为若干“单主动+多被动”传感器组;然后,通过并行运算,对各传感器组运用基于角度关联的多传感器信息融合算法,得到跟踪所需的有效量测;最后,在跟踪滤波阶段,通过引入箱粒子滤波数值计算方法,将获取的量测点划分为若干箱粒子,并对箱粒子滤波下的多传感器量测更新系数进行重新定义,以较低的计算复杂度实现LMB(Labeled multi-Bernoulli,LMB)滤波。仿真结果表明,所提算法在保证跟踪精度的前提下,误差明显降低且算法复杂度下降约40%,能够有效处理异构数据多源信息融合问题。

关键词: 目标跟踪, 传感器数据融合, 信息融合, 箱粒子滤波, 标签多伯努利滤波

Abstract:

With the emergence of a large number of complex tracking scenarios,conventional multi-source sensor multi-target tracking algorithms have shortcomings of high computational complexity,low tracking accuracy,and inability to estimate target trajectories,making them unable to meet the needs of modern warfare.In this paper,we focus on the implementation of the multi-source sensor tracking problem with the background of the multi-source sensor system composed of active and passive sensors.For the problem of a “multi-active+multi-passive” multi-source sensor system that the measurement cannot be fully integrated and the overall algorithm complexity is high,a multi-source sensor box particle labeled multi-Bernoulli(MS-BPF-LMB)filtering algorithm is proposed.First,the sensors are grouped according to different active sensors,i.e.,all sensors are divided into several "single active + multiple passive" sensor groups;and then,through parallel operations,a multi-sensor information fusion method based on angle correlation is applied to each sensor group to obtain the effective measurements required for tracking.Finally,in the tracking filtering stage,the obtained measurement points are divided into several box particles by introducing the box particle filtering numerical calculation method,and the update coefficients of multi-sensor measurements under box particle filtering are redefined to achieve LMB filtering with a low computational complexity.Simulation results show that the proposed method can effectively deal with the problem of multi-source information fusion of heterogeneous data by significantly reducing the error and decreasing the complexity of the algorithm by about 40% on the basis of maintaining the tracking accuracy of the target.

Key words: target tracking, sensor data fusion, information fusion, box particle filtering, labeled multi-Bernoulli filtering

中图分类号:

V219

张永权, 李志彬, 张文博, 苏镇镇. 多源传感器箱粒子LMB滤波算法[J]. 西安电子科技大学学报, 2024, 51(4): 51-66.

ZHANG Yongquan, LI Zhibin, ZHANG Wenbo, SU Zhenzhen. Multi-source sensor box particle LMB filtering algorithm[J]. Journal of Xidian University, 2024, 51(4): 51-66.

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参考文献 29

[1]	张逸宸, 水鹏朗, 廖沫. 目标回波特征辅助的海面多目标跟踪方法[J]. 西安电子科技大学学报, 2023, 50(5):1-10.
	ZHANG Yichen, SHUI Penglang, LIAO Mo. Sea-Surface Multi-Target Tracking Method Aided by Target Returns Feature[J]. Journal of Xidian University, 2023, 50(5):1-10.
[2]	侯建华, 余宁远, 项俊. 基于运动模型和数据关联的多目标跟踪[J]. 中南民族大学学报(自然科学版), 2023, 42(2):260-266.
	HOU Jianhua, YU Ningyuan, XIANG Jun. Multi-Object Tracking Based on Motion Model and Data Association[J]. Journal of South-Central Minzu University (Natural Science Edition), 2023, 42(2):260-266.
[3]	陈振, 李翠芸, 李想. B样条曲面三维扩展目标跟踪算法[J]. 西安电子科技大学学报, 2023, 50(2):101-111.
	CHEN Zhen, LI Cuiyun, LI Xiang. Algorithm for Tracking the 3D Extended Target Based on the B-Spline Surface[J]. Journal of Xidian University, 2023, 50(2):101-111.
[4]	REUTER S, VO B T, VO B N, et al. The Labeled Multi-Bernoulli Filter[J]. IEEE Transactions on Signal Processing, 2014, 62(12):3246-3260.
[5]	ZHANG Y Q, JI H B, HU Q. A Box-Particle Implementation of Standard PHD Filter for Extended Target Tracking[J]. Information Fusion, 2017,34:55-69.
[6]	ZHANG Y Q, JI H B, HU Q. A Fast Ellipse Extended Target PHD Filter Using Box-Particle Implementation[J]. Mechanical Systems and Signal Processing, 2018,99:57-72.
[7]	ZHANG Y Q, JI H B, GAO X B, HU Q. An Ellipse Extended Target CBMeMBer Filter Using Gamma and Box-Particle Implementation[J]. Signal Processing, 2018,149:88-102.
[8]	张翔, 郑玲, 李以农, 等. 融合视觉与雷达数据的改进粒子滤波车辆目标跟踪[J]. 重庆大学学报, 2022, 45(9):28-38.
	ZHANG Xiang, ZHENG Ling, LI Yinong, et al. Improved Particle Filter Vehicle Tracking Based on Vision and Radar Sensor Fusion[J]. Journal of Chongqing University, 2022, 45(9):28-38.
[9]	LI S, YI W, HOSEINNEZHAD R, et al. Robust Distributed Fusion with Labeled Random Finite Sets[J]. IEEE Transactions on Signal Processing, 2018, 66(2):278-293.
[10]	LI S, BATTISTELLI G, CHISCI L, et al. Computationally Efficient Multi-Agent Multi-Object Tracking with Labeled Random Finite Sets[J]. IEEE Transactions on Signal Processing, 2019, 67(1):260-275.
[11]	陈旭志, 杨金龙. 一种状态扩维标签匹配的分布式融合算法[J]. 信号处理, 2022, 38(7):1467-1480.
	CHEN Xuzhi, YANG Jinlong. A Distributed Fusion Algorithm Based on State-Extended Label Matching[J]. Journal of Signal Processing, 2022, 38(7):1467-1480.
[12]	ZHANG Y, SHANG A, ZHANG W, et al. A Measurement Fusion Algorithm of Active and Passive Sensors Based on Angle Association for Multi-Target Tracking[J]. Information Fusion, 2024,106:102267.
[13]	曾雅俊, 王俊, 魏少明, 等. 分布式多传感器多目标跟踪方法综述[J]. 雷达学报, 2023, 12(1):197-213.
	ZENG Yajun, WANG Jun, WEI Shaoming, et al. Review of the Method for Distributed Multi-Sensor Multi-Target Tracking[J]. Journal of Radars, 2023, 12(1):197-213.
[14]	SUN W, HAN Y. A New Multiple Targets Tracking Algorithm Based on Improved MHT Algorithm[C]//2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI).Piscataway:IEEE, 2022: 26-30.
[15]	张立勇, 张利, 李丹. 基于最优分组原则的多传感器分组加权融合算法[J]. 仪器仪表学报, 2008, 29(1):200-205.
	ZHANG Liyong, ZHANG Li, LI Dan. Grouping Weighted Fusion Algorithm for Multi-Sensor and Its Optimal Grouping Principle[J]. Chinese Journal of Scientific Instrument, 2008, 29(1):200-205.
[16]	刘钊, 戴斌, 刘大学. 多传感器信息融合中时间同步方法的研究[J]. 计算机仿真, 2009(6):124-127.
	LIU Zhao, DAI Bin, LIU Daxue. A Time Synchronization Approach for Multi-Sensor Fusion[J]. Computer Simulation, 2009(6):124-127.
[17]	贾润亮. 无线传感器网络成对节点同步方法研究[J]. 无线互联科技, 2022, 19(9):13-15.
	JIA Runliang. Research on Pairwise Node Synchronization in Wireless Sensor Networks[J]. Wireless Internet Technology, 2022, 19(9):13-15.
[18]	李璐, 雷明. 密集杂波下的高斯混合信度传播多目标跟踪[J]. 哈尔滨工业大学学报, 2020, 52(4):38-46.
	LI Lu, LEI Ming. Gaussian Mixture Belief Propagation Multi-Target Tracking under Dense Clutter[J]. Journal of HARBIN Institute of Technology, 2020, 52(4):38-46.
[19]	XU Y, XU K, WAN J, et al. Research on Particle Filter Tracking Method Based on Kalman Filter[C]//2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC).Piscataway:IEEE, 2018:1564-1568.
[20]	陈辉, 邓东明, 韩崇昭. 基于区间箱粒子多伯努利滤波器的传感器控制策略[J]. 自动化学报, 2021, 47(6):1428-1443.
	CHEN Hui, DENG Dongming, HAN Chongzhao. Sensor Control Based on Interval Box-particle Multi-Bernoulli Filter[J]. Acta Automatica Sinica, 2021, 47(6):1428-1443.
[21]	QIN Y. Box-Particle Bernoulli Filter for Multipath Target Tracking in OTHR[C]//2019 2nd International Conference on Information Systems and Computer Aided Education (ICISCAE).Piscataway:IEEE, 2019: 567-571.
[22]	冯新喜, 迟珞珈, 王泉. 基于箱粒子滤波的混合标签多伯努利跟踪算法[J]. 控制与决策, 2020, 35(2):507-512.
	FENG Xinxi, CHI Luojia, WANG Quan. A Hybrid Labeled Multi-Bernoulli Tracking Algorithm Based on Box Particle Filter[J]. Control and Decision, 2020, 35(2):507-512.
[23]	陈楠祺. 基于箱粒子滤波的随机有限集多目标跟踪算法研究[D]. 西安: 西安电子科技大学, 2019.
[24]	MOORE R E, KEARFOTT R B, CLOUD M J. Introduction to Interval Analysis[M]. Philadelphia:SIAM, 2009.
[25]	VO B N, VO B T, HOANG H G. An Efficient Implementation of the Generalized Labeled Multi-Bernoulli Filter[J]. IEEE Transactions on Signal Processing, 2016, 65(8):1975-1987.
[26]	VO B T, VO B N. Labeled Random Finite Sets and Multi-Object Conjugate Priors[J]. IEEE Transactions on Signal Processing, 2013, 61(13):3460-3475.
[27]	VO B N, VO B T, PHUNG D. Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter[J]. IEEE Transactions on Signal Processing, 2014, 62(24):6554-6567.
[28]	JAULIN L, KIEFFER M, DIDRIT O, et al. Applied Interval Analysis[M]. Heidelberg:Springer, 2001.
[29]	SCHUHMACHER D, VO B T, VO B N. AConsistent Metric for Performance Evaluation in Multi-Object Filtering[J]. IEEE Transactions on Signal Processing, 2008, 56(8):3447-3457.

目标编号	初始状态	新生时刻	消亡时刻
1	[-1 000,28,2 000,-20,500,0,3]^T	1	70
2	[1 500,-18,-2 000,28,500,0,3]^T	1	100
3	[1 500,10,-2 000,25,500,0,3]^T	8	80
4	[-1 000,-25,2 000,-30,500,0,3]^T	15	100
5	[-500,50,-1 000,-52,500,0,3]^T	25	100
6	[-2 000,16,-1 000,40,500,0,3]^T	30	100

参数名称	数值	单位
采样间隔T	1	s
方位角量测标准差σ_α	1	rad
俯仰角量测标准差σ_β	1	rad
x方向距离标准差σ_x	10	m
y方向距离标准差σ_y	10	m
z方向距离标准差σ_z	10	m
目标存活概率	0.99
传感器检测概率	0.99

算法	运行时间/s
MS-BPF-LMB	25.67
MS-SMC-LMB	41.39
AS-BPF-LMB	21.34
MS-GCI-LMB	69.13