目标回波特征辅助的海面多目标跟踪方法

doi:10.19665/j.issn1001-2400.20230201

摘要/Abstract

摘要：

由于复杂的海洋环境和近海稠密的海面目标,海用雷达常面临高虚警率、高目标密度的“双高”跟踪场景。来源于海杂波和多个邻近目标的量测点在探测空间中密集出现,传统的跟踪方法仅利用单帧量测的位置信息不能很好区分量测的具体来源,导致跟踪性能严重下降。利用目标回波特征信息可以在不提高算法复杂度的情况下弥补上述缺陷,但目标回波特征的泛化能力较弱,需要针对不同的雷达体制、工作场景及探测需求,因地制宜地选择适合的目标回波特征。以探测大中小型船只为任务的高分辨对海警戒雷达为应用背景,采用检验统计量和目标径向速度量测作为目标回波特征,重构了跟踪方程使得目标回波特征的信息被充分应用于关联、滤波和航迹管理等跟踪的各个环节。此外,采用了一种“两级”跟踪流程,依据航迹质量划分确认航迹和候选航迹,串联跟踪目标并交互信息,有助于跟踪难以形成持续稳定检测的非机动海面小目标。实测和仿真数据实验结果表明,在海面复杂的多目标场景中,所提方法可以实现稳健的目标跟踪。

关键词: 特征辅助跟踪, 目标跟踪, 海杂波, 目标回波特征, 跟踪系统

Abstract:

Due to the complex marine environment and the dense sea-surface targets,radars often face the tough tracking scenarios with a high false alarm rate and high target density.The measurement points originating from clutter and multiple closely-spaced targets appear densely in the detection space.The traditional tracking methods only use the position information,which cannot distinguish the specific source of the measurement well,resulting in serious degradation of the tracking performance.Target returns features can be used to solve the problem without increasing the complexity of the algorithm,but the generalization ability of the features is low.It is necessary to select suitable features according to different radar systems,working scenes and requirements.In this paper,the test statistic and the target radial velocity measurement are used as the target returns features,and the tracking equation is reconstructed so that features can be fully applied in all aspects of tracking.In addition,this paper adopts a "two-level" tracking process,which divides track and candidate track according to track quality.Experimental results show that the proposed method can achieve robust target tracking in the complex multi-target scenarios on the sea surface.

Key words: feature-aided tracking, target tracking, sea clutter, target returns features, tracking systems

中图分类号:

TN820.4

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 features[J]. Journal of Xidian University, 2023, 50(5): 1-10.

图/表 6

图1

图2

图3

图4

表1

图5

参考文献 29

[1]	BRIGGS J N. Target Detection by Marine Radar[M]. London: The Institution of Electrical Engineers, 2004:1-28.
[2]	WARD K, TOUGH R, WATTS S. Sea Clutter:Scattering,the K Distribution and Radar Performance[M]. London: The Institution of Electrical Engineers, 2013:17-104.
[3]	ROSENBERG L, WATTS S, GRECO M S. Modeling the Statistics of Microwave Radar Sea Clutter[J]. IEEE Transactions on Aerospace and Electronic Systems Magazine, 2019, 34(10):44-75.
[4]	柳超, 王月基. 对海探测雷达多目标跟踪技术综述[J]. 雷达学报, 2021, 10(1):100-115.
	LIU Chao, WANG Yueji. Review of Multi-Target Tracking Technology for Marine Radar[J]. Journal of Radars, 2021, 10(1):100-115.
[5]	ZHANG K, SHUI P L, FENG Y. Detection of Sea-Surface Small Targets Masked by Range Sidelobes of Large Objects[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(2):1446-1461. doi: 10.1109/TAES.2021.3116120
[6]	XU S W, WANG Z X, ZHOU H. Optimum and Near-Optimum Coherent CFAR Detection of Radar Targets in Compound-Gaussian Clutter with Generalized Inverse Gaussian Texture[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(3):1692-1706. doi: 10.1109/TAES.2021.3120045
[7]	XUE J, XU S, PAN M Y. Adaptive Detection of Radar Targets in Heavy-Tailed Sea Clutter with Lognormal Texture[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60:1-11.
[8]	ZHENG L, WANG X D. Improved Multiple Hypothesis Tracker for Joint Multiple Target Tracking and Feature Extraction[J]. IEEE Transactions on Aerospace and Electronic Systems, 2019, 55(6):3080-3089. doi: 10.1109/TAES.7
[9]	韩崇昭, 朱洪艳, 段战胜. 多源信息融合[M].第二版. 北京: 清华大学出版社, 2010:139-207.
[10]	张佳琦, 陶海红, 张修社, 等. 一种利用量测空间聚类的多帧检测前跟踪算法[J]. 西安电子科技大学学报, 2021, 48(5):231-238.
	ZHANG Jiaqi, TAO Haihong, ZHANG Xiushe, et al. A Multi-Frame Track before Detect Algorithm Utilizing Measurement Space Clustering[J]. Journal of Xidian University, 2021, 48(5):231-238.
[11]	李翠芸, 李洋, 姬红兵, 等. 一步数据关联GLMB扩展目标跟踪算法[J]. 西安电子科技大学学报, 2020, 47(5):137-143.
	LI Cuiyun, LI Yang, JI Hongbing, et al. GLMB Extended Target Tracking Based on One-Step Data Association[J]. Journal of Xidian University, 2020, 47(5):137-143.
[12]	GROSSI E, LOPS M, VENTURINO L. A Novel Dynamic Programming Algorithm for Track-before-Detect in Radar Systems[J]. IEEE Transactions on Signal Processing, 2013, 61(10):2608-2619. doi: 10.1109/TSP.78
[13]	张逸宸, 水鹏朗. 一种改进的泊松点过程概率多假设跟踪方法[J]. 西安电子科技大学学报, 2021, 48(2):27-34.
	ZHANG Yichen, SHUI Penglang. Improved Probabilistic Multi-Hypothesis Tracker via the Poisson Point Process[J]. Journal of Xidian University, 2021, 48(2):27-34.
[14]	EHRMAN L M, BLAIR W D. Using Target RCS when Tracking Multiple Rayleigh Targets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(2):701-716. doi: 10.1109/TAES.2010.5461650
[15]	MELLEMA G R. Improved Active Sonar Tracking in Clutter Using Integrated Feature Data[J]. IEEE Journal of Oceanic Engineering, 2020, 45(1):304-318. doi: 10.1109/JOE.48
[16]	BAR-SHALOM Y, KIRUBARAJAN T, GOKBERK C. Tracking with Classification-Aided Multiframe Data Association[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(3):868-878. doi: 10.1109/TAES.2005.1541436
[17]	BREKKE E O, HALLINGSTAD O, GLATTETRE J. Tracking Small Targets in Heavy-Tailed Clutter Using Amplitude Information[J]. IEEE Journal of Oceanic Engineering, 2010, 35(2):314-329. doi: 10.1109/JOE.2010.2044670
[18]	RUAN Y, HONG L, WICKER D. AnalyticPerformance Prediction of Feature-aided Global Nearest Neighbour Algorithm in Dense Target Scenarios[J]. IET Radar,Sonar & Navigation, 2007, 1(5):369-376. doi: 10.1049/iet-rsn:20050110
[19]	YOON J H, KIM D Y, BAE S H, et al. Joint Initialization and Tracking of Multiple Moving Objects Using Doppler Information[J]. IEEE Transactions on Signal Processing, 2011, 59(7):3447-3452. doi: 10.1109/TSP.2011.2132720
[20]	WANG X, MUSICKI D, ELLEM R, et al. Efficient and Enhanced Multi-Target Tracking with Doppler Measurements[J]. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(4):1400-1417. doi: 10.1109/TAES.2009.5310307
[21]	WANG J, HE P, LONG T. Use of the Radial Velocity Measurement in Target Tracking[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(2):401-413. doi: 10.1109/TAES.2003.1207253
[22]	MORI S, CHANG K C, CHONG C Y. Performance Prediction of Feature-Aided Track-to-Track Association[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(4):2593-2603. doi: 10.1109/TAES.2014.120687
[23]	XIA M, YI W, LI S, et al. A Polarization Information Aided Probabilistic Data Association for Target Tracking in Polarimetric Radar System[C]//2015 IEEE Radar Conference (RadarCon).Piscataway:IEEE, 2015:1182-1187.
[24]	KIM D, HWANG I. Dynamic Model-Based Feature Aided Data Association Filter in Target Tracking[J]. IET Radar,Sonar & Navigation, 2020, 14(2):279-289. doi: 10.1049/rsn2.v14.2
[25]	SANGSTON K J, GINI F, GRECO M S. Coherent Radar Target Detection in Heavy-Tailed Compound-Gaussian Clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(1):64-77. doi: 10.1109/TAES.2012.6129621
[26]	SANGSTON K J, FARINA A. Coherent Radar Detection in Compound-Gaussian Clutter:Clairvoyant Detectors[J]. IEEE Aerospace and Electronic Systems Magazine, 2016, 31(11):42-63.
[27]	The Defense,Peace,Safety,and Security Unit of the Council for Scientific and Industrial Research. The Fynmeet Radar Database (2012)[DB/OL].[2012-10-11]. http://www.csir.co.ca/small_boat_detection.
[28]	于涵, 水鹏朗, 施赛楠, 等. 广义Pareto分布海杂波模型参数的组合双分位点估计方法[J]. 电子与信息学报, 2019, 41(12):2836-2843.
	YU Han, SHUI Penglang, SHI Sainan, et al. Combined Bipercentile Parameter Estimation of Generalized Pareto Distributed Sea Clutter Model[J]. Journal of Electronics & Information Technology, 2019, 41(12):2836-2843.
[29]	SHI S N, LIANG X, SHUI P L, et al. Low-Velocity Small Target Detection with Doppler-Guided Retrospective Filter in High-Resolution Radar at Fast Scan Mode[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11):8937-8953. doi: 10.1109/TGRS.36

目标序号	目标类型	平均信杂比/dB	检测概率(P_fa=10^-4)	出现时间/s
1	直升机	12.1	0.887	[0,163]
2	挂着角反的四旋翼无人机	3.4	0.573	[25,151] [198,255]
3	锚定小船	16.9	0.930	一直存在
4	快艇	8.5	0.779	[208,297.5]