基于标签多贝努利多传感器组网目标跟踪算法

doi:10.12305/j.issn.1001-506X.2021.06.11

摘要/Abstract

摘要：

随着电磁环境的日益复杂, 强干扰和高杂波带来的目标低检测概率问题日益突出, 给探测系统准确估计监测区域内目标个数以及目标状态带来了新的挑战。针对低检测概率问题, 提出随机有限集框架下基于标签多贝努利(labelled multi-Bernoulli, LMB)多传感器组网目标跟踪算法。该算法首次将LMB框架应用到不同探测范围的多传感器组网目标跟踪场景中, 实现了多目标跟踪目标数和相应状态稳定估计。仿真结果表明, 所提方法不仅能在低检测概率条件下获得目标稳定的航迹估计, 及时捕捉目标新生、消亡等事件, 还能有效叠加不同传感器不同探测范围, 充分发挥多传感器优势。

关键词: 低检测概率, 标签多贝努利, 多传感器, 探测范围

Abstract:

With the increasing complexity of electromagnetic environment, strong interferences and high clutter rate lead to the low detection probability problem, which brings a new challenge for the observation system to accurately estimate the number and states of target in surveillance area. Aiming at the problem of low detection probability, a multi-sensor networking target tracking algorithm based on labelled multi-Bernoulli (LMB) in the framework of random finite sets is proposed. The proposed algorithm incorporates the LMB framework into target tracking scene of multi-sensor networking with different detection ranges for the first time and realizes the target number and corresponding sate stability estimation of multi-target tracking. The simulation results show that the proposed algorithm not only provides stable estimation of trajectories under the condition of low detection probability, including targets born and die, but also gives an effective superposition of different detection ranges, which makes full advantage of multiple sensors.

Key words: low detection probability, labelled multi-Bernoulli (LMB), multi-sensor, detection range

中图分类号:

TN95

胡琪, 杨超群. 基于标签多贝努利多传感器组网目标跟踪算法[J]. 系统工程与电子技术, 2021, 43(6): 1541-1546.

Qi HU, Chaoqun YANG. Multi-sensor networking target tracking algorithm based on labelled multi-Bernoulli[J]. Systems Engineering and Electronics, 2021, 43(6): 1541-1546.

图/表 3

参考文献 30

1	ZHENG L, ZHANG Y, WANG X D. Feature-aided NN-JPDAF algorithm for multiple target tracking[C]//Proc. of the IEEE Radar Conference, 2019.
2	DING J , SUN S L , MA J , et al. Fusion estimation for multi-sensor networked systems with packet loss compensation[J]. Information Fusion, 2019, 45, 138- 149. doi: 10.1016/j.inffus.2018.01.008
3	LI G C , YI W , LI S Q , et al. Asynchronous multi-rate multi-sensor fusion based on random finite set[J]. Signal Processing, 2019, 160, 113- 126. doi: 10.1016/j.sigpro.2019.01.028
4	BAR-SHALOM Y , LI X R . Multitarget-multisensor tracking: principles and techniques[M]. London: Yorkshire Building Society, 1995.
5	LEE E H, MUSICKI D, SONG T L. Multi-sensor distributed fusion based on integrated probabilistic data association[C]//Proc. of the Information Fusion Conference, 2014.
6	ZE D N, SHENG L W. A maneuvering target tracking algorithm based on amplitude characteristics in sea clutter[C]//Proc. of the Information Technology, Networking, Electronic and Automation Control Conference, 2020: 1528-1533.
7	LANCASTER J, BLACKMAN S. Joint IMM/MHT tracking and identification for multi-sensor ground target tracking[C]//Proc. of the Information Fusion Conference, 2006.
8	陆耀宾, 孙伟. 基于MHT的多传感器数据融合算法[J]. 中国电子科学研究院学报, 2008, 3 (1): 24- 29.
	LU Y B , SUN W . A multisensor data fusion algorithm based on MHT[J]. Journal of China Academy of Electronics and Information Technology, 2008, 3 (1): 24- 29.
9	MAHLER R P S . Multitarget Bayes filtering via first-order multitarget moments[J]. IEEE Trans.on Aerospace and Electronic Systems, 2003, 39 (4): 1152- 1178. doi: 10.1109/TAES.2003.1261119
10	MAHLER R P S . PHD filters of higher order in target number[J]. IEEE Trans.on Aerospace and Electronic Systems, 2007, 43 (4): 1523- 1543. doi: 10.1109/TAES.2007.4441756
11	卢晓东, 崔涛, 王伟, 等. 考虑配准时空误差的多目标标签多伯努利滤波[J]. 系统工程与电子技术, 2020, 42 (4): 904- 911.
	LU X D , CUI T , WANG W , et al. Multi-target labeled multi-Bernoulli filter with time-delay and registration errors[J]. Systems Engineering and Electronics, 2020, 42 (4): 904- 911.
12	柳超, 孙进平, 陈小龙, 等. 结合幅度信息的扩展目标随机有限集跟踪方法[J]. 雷达学报, 2020, 9 (4): 730- 738.
	LIU C , SUN J P , CHEN X L , et al. Random finite set-based extended target tracking method with amplitude information[J]. Journal of Radars, 2020, 9 (4): 730- 738.
13	周成伟, 郑航, 顾宇杰, 等. 互质阵列信号处理研究进展: 波达方向估计与自适应波束成形[J]. 雷达学报, 2019, 8 (5): 558- 577.
	ZHOU C W , ZHENG H , GU Y J , et al. Research progress on coprime array signal processing: direction-of-arrival estimation and adaptive beamforming[J]. Journal of Radars, 2019, 8 (5): 558- 577.
14	熊伟, 顾祥岐, 徐从安, 等. 基于ET-GMPHD算法的编队目标跟踪方法[J]. 系统工程与电子技术, 2020, 42 (11): 2434- 2440.
	XIONG W , GU X Q , XU C A , et al. Formation target tracking method based on ET-GMPHD algorithm[J]. Systems Engineering and Electronics, 2020, 42 (11): 2434- 2440.
15	VO B N , MA W K . The Gaussian mixture probability hypothe-sis density filter[J]. IEEE Trans.on Signal Processing, 2006, 54 (11): 4091- 4104. doi: 10.1109/TSP.2006.881190
16	孟凡彬, 郝燕玲, 张崇猛, 等. 基于无迹粒子PHD滤波的序贯融合算法[J]. 系统工程与电子技术, 2011, 33 (1): 30- 34.
	MENG F B , HAO Y L , ZHANG C M , et al. Sequential fusion algorithm based on unscented particle probability hypothesis density filter[J]. Systems Engineering and Electronics, 2011, 33 (1): 30- 34.
17	PHAM N T, HUANG W, ONG S H. Multiple sensor multiple object tracking with GMPHD filter[C]//Proc. of the Information Fusion Conference, 2007.
18	ZHANG H J , JING Z L , HU S Q . Localization of multiple emitters based on the sequential PHD filter[J]. Signal Processing, 2010, 90 (1): 34- 43. doi: 10.1016/j.sigpro.2009.05.026
19	LI W L , JIA Y M , DU J P , et al. Gaussian mixture PHD filter for multi-sensor multi-target tracking with registration errors[J]. Signal Processing, 2013, 93 (1): 86- 99. doi: 10.1016/j.sigpro.2012.06.030
20	MAHLER R. Approximate multisensor CPHD and PHD filters[C]//Proc. of the Information Fusion Conference, 2010.
21	MRYER F , BRACA P , WILLETT P , et al. A scalable algorithm for tracking an unknown number of targets using multiple sensors[J]. IEEE Trans.on Signal Processing, 2017, 65 (13): 3478- 3493. doi: 10.1109/TSP.2017.2688966
22	GAEBLER J A , AXELRAD P , SCHUMACHER J R P W . CubeSat cluster deployment track initiation via a radar admissible region birth model[J]. Journal of Guidance, Control, and Dynamics, 2020, 43 (10): 1927- 1934. doi: 10.2514/1.G005139
23	MEYER F, BRACA P, WILLETT P, et al. Tracking an unknown number of targets using multiple sensors: a belief propagation method[C]//Proc. of the Information Fusion Confe-rence, 2016: 719-726.
24	VO B T , VO B N . Labeled random finite sets and multi-object conjugate priors[J]. IEEE Trans.on Signal Processing, 2013, 61 (13): 3460- 3475. doi: 10.1109/TSP.2013.2259822
25	VO B N , VO B T , HOANG H G . An efficient implementation of the generalized labeled multi-Bernoulli filter[J]. IEEE Trans.on Signal Processing, 2017, 65 (8): 1975- 1987. doi: 10.1109/TSP.2016.2641392
26	CAO C H , ZHAO Y B , PANG X J , et al. An efficient implementation of multiple weak targets tracking filter with labeled random finite sets for marine radar[J]. Digital Signal Processing, 2020, 101, 102710. doi: 10.1016/j.dsp.2020.102710
27	VO B N, VO B T. Multi-sensor multi-object tracking with the generalized labeled multi-Bernoulli filter[EB/OL]. [2020-12-10]. https://arxiv.org/abs/1702.08849.
28	LIN Y F, LIU Z P, LIU D K, et al. Spawned target tracking algorithm based on GLMB model in RFS theory [C]//Proc. of the International Conference on Electronics Technology, 2020: 851-856.
29	HOU L, LIAN F, DE A G T F, et al. Robust delta generalized labeled multi-Bernoulli filter for nonlinear systems with heavy-tailed noises[C]//Proc. of the Information Fusion Conference, 2020.
30	QIU H , HUANG G M , GAO J . Centralized multi-sensor multi-target tracking with labeled random finite set[J]. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2017, 231 (4): 669- 676. doi: 10.1177/0954410016641447

[1]	刘康, 何明浩, 韩俊, 王庚. 基于本征向量和Jousselme距离的高冲突证据融合方法[J]. 系统工程与电子技术, 2022, 44(7): 2175-2180.
[2]	谢家豪, 黄树彩, 韦道知, 张曌宇, 王文豪. 基于P_EV准则的不确定混合多传感器联盟求解[J]. 系统工程与电子技术, 2022, 44(3): 819-826.
[3]	董晨, 帅逸仙, 周金鹏, 赖鹏, 程仙垒. 网络化多传感器-多武器协同防空任务规划[J]. 系统工程与电子技术, 2022, 44(12): 3738-3746.
[4]	时晨光, 董璟, 周建江, 汪飞. 飞行器射频隐身技术研究综述[J]. 系统工程与电子技术, 2021, 43(6): 1452-1467.
[5]	张佳琦, 陶海红, 张修社. 量测点迹空间聚类的多传感器多帧检测算法[J]. 系统工程与电子技术, 2021, 43(6): 1533-1540.
[6]	张晓彤, 李书凯, 孙志国, 谢红. 时宽-波形基联合捷变的LPD通信波形[J]. 系统工程与电子技术, 2020, 42(12): 2884-2891.
[7]	张闯, 郭晨, 张大恒. 船舶组合导航自适应迭代粒子滤波方法及应用[J]. 系统工程与电子技术, 2019, 41(4): 883-888.
[8]	欧阳成, 徐敏, 顾杰. 基于探测区域搜索的TOA量测数据关联[J]. 系统工程与电子技术, 2019, 41(12): 2697-2702.
[9]	高晓光, 李飞, 万开方. 数据丢包环境下的多传感器协同跟踪策略研究[J]. 系统工程与电子技术, 2018, 40(11): 2450-.
[10]	庞策, 黄树彩, 刘锦昌, 赵炜. 基于博弈论的多传感器交叉提示算法[J]. 系统工程与电子技术, 2017, 39(8): 1684-1690.
[11]	范成礼, 付强, 邢清华. 基于改进PSO的临空高速飞行器协同跟踪优化[J]. 系统工程与电子技术, 2017, 39(3): 476-481.
[12]	刘俊, 刘瑜, 何友, 孙顺. 基于速度方位约束的多传感器模糊数据互联[J]. 系统工程与电子技术, 2016, 38(9): 2040-2047.
[13]	倪鹏1,2, 刘进忙1, 付强1, 高嘉乐1. 异构MAS下反导作战多传感器任务规划分层决策框架[J]. 系统工程与电子技术, 2016, 38(8): 1816-1825.
[14]	詹锟, 蒋宏, 赵天衢, 于耀中. 基于模型类型匹配PHD滤波器和TBM的多目标联合跟踪分类[J]. 系统工程与电子技术, 2016, 38(10): 2235-2243.
[15]	刘华, 吴文, 王世元. 基于平方根CKF的多传感器序贯式融合跟踪算法[J]. 系统工程与电子技术, 2015, 37(7): 1494-1498.