基于JVC的紧凑型地波雷达海上目标点迹-航迹最优关联方法

戴永寿; 马鹏; 孙伟峰; 刘培学; 纪永刚; 庞真真

doi:10.11999/JEIT200604

基于JVC的紧凑型地波雷达海上目标点迹-航迹最优关联方法

doi: 10.11999/JEIT200604

1.
中国石油大学(华东)海洋与空间信息学院青岛 266580
2.
自然资源部第一海洋研究所海洋物理与遥感研究室青岛 266061

基金项目: 国家重点研发计划项目(2017YFC1405202, 2017YFC1405203)，国家自然科学基金(62071493, 61831010, 61501520)，中央高校基本科研业务费项目(19CX02046A)

详细信息

作者简介:
戴永寿：男，1963年生，教授，研究方向为地震信号处理、海洋环境监测等

马鹏：男，1993年生，硕士生，研究方向为紧凑型高频地波雷达目标探测与跟踪

孙伟峰：男，1982年生，副教授，研究方向为紧凑型高频地波雷达目标探测与跟踪、图像处理等

刘培学：男，1983年生，博士生，研究方向为紧凑型高频地波雷达目标探测与跟踪

纪永刚：男，1977年生，研究员，研究方向为紧凑型高频地波雷达目标探测、海上目标多手段融合探测、新体制超视距雷达海态监测等

庞真真：女，1996年生，硕士生，研究方向为紧凑型高频地波雷达目标探测与跟踪

通讯作者:
孙伟峰　sunwf@upc.edu.cn

中图分类号: TN953
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- 被引次数: 3
出版历程
- 收稿日期: 2020-07-21
- 修回日期: 2020-12-16
- 网络出版日期: 2021-01-05
- 刊出日期: 2021-10-18

An Optimal Plot-to-Track Association Method Based on JVC Algorithm for Maritime Target with Compact HFSWR

1.
College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
2.
Laboratory of Marine Physics and Remote Sensing, First Institute of Oceanography, MNR, Qingdao 266061, China

Funds: The National Key R&D Program of China (2017YFC1405202, 2017YFC1405203), The National Natural Science Foundation of China (62071493, 61831010, 61501520), The Fundamental Research Funds for the Central Universities (19CX02046A)

摘要

摘要: 紧凑型地波雷达由于接收天线阵列孔径减小导致对海上目标的定位精度低，在多目标跟踪算法中采用序贯式的点迹-航迹关联方式易发生误关联导致航迹断裂、误跟踪等问题。对此，该文将多目标点迹-航迹关联转化为最优分配问题，提出一种基于JVC算法的多目标点迹-航迹最优关联方法。对于关联波门重叠区域内存在公共候选点迹的多条航迹，首先以雷达获取的目标多普勒速度、距离与方位角作为目标特征参数，利用最小代价函数确定公共候选点迹与所有航迹之间的相似度，得到关联代价矩阵；然后以总关联代价最小化作为优化准则，采用JVC算法求解得到最优的点迹-航迹关联结果。利用仿真与实测目标数据开展了点迹-航迹关联实验，并与序贯最近邻关联方法的关联结果进行了对比。实验结果表明：采用该文所提方法跟踪得到的航迹时长明显优于序贯最近邻关联方法的结果，解决了序贯式关联因关联错误导致的航迹断裂、误跟踪等问题，提高了航迹跟踪的连续性。
- 紧凑型高频地波雷达 /
- 多目标跟踪 /
- 点迹-航迹关联 /
- 最优关联
Abstract: The compact High-Frequency Surface Wave Radar (HFSWR) has low spatial resolution for target detection due to its reduced aperture size of the receiving antenna array. The sequential plot-to-track association method used in multi-target tracking algorithms is prone to erroneous association, which easily leads to track fragmentation and false tracking. In order to solve this problem, regarding the multi-target plot-to-track association as an optimal allocation problem, an optimal multi-target plot-to-track association method based on JVC (Jonker-Volgenant-Castanon) algorithm is proposed. For multiple tracks with common candidate plots in their overlapped association gate, firstly, the similarity between their candidate plots and all tracks is calculated using the minimal cost function with target Doppler velocity, range and azimuth as parameters and an association cost matrix is formed. Then, the optimal association result is achieved by minimizing the total association cost using the JVC algorithm. Both simulation and field target data are used to carry out the plot-to-track association experiment, and the association results are compared with those of the sequential nearest neighbor association method. The experimental results show that the track length obtained by the proposed method is superior to that of the sequential nearest neighbor method, thus the track continuity is improved.
- Compact HFSWR /
- Multitarget tracking /
- Plot-to-track association /
- Optimal association

HTML全文

图 1 关联波门示意图

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图 2 紧凑型HFSWR目标仿真示例

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图 3 基于仿真数据的跟踪结果对比

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图 4 目标参数误差分析

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图 5 采用实测数据跟踪时不同时长的航迹数目对比

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图 6 采用实测数据的跟踪结果对比

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表 1 仿真目标的参数

	初始距离(km)	初始方位角(°)	初始多普勒速 $({\rm{km/h}})$	帧数
目标1	147.2	8.4	20.8	180
目标2	151.6	8.7	19.7	180
目标3	157.4	11.1	19.4	180
目标4	138.1	–19.5	30.6	180
目标5	138.8	–19.0	19.1	180

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表 2 不同跟踪时长的航迹数量对比

方法	跟踪时长>30 min		跟踪时长>40 min		跟踪时长>50 min
方法	航迹总数	平均跟踪时长(min)	航迹总数	平均跟踪时长(min)	航迹总数	平均跟踪时长(min)
序贯最近邻关联方法	181	46.9	123	52.4	92	54.8
本文方法	145	69.1	109	80.7	81	93.7

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表 3 目标个例详细信息

船名	MMSI	船长(m)	船宽(m)	吃水深度(m)	初始距离 ${\rm{(km)}}$	初始方位角 ${(^ \circ })$	多普勒速度 $({\rm{km/h}})$	跟踪时长(m)
JIN YUAN XING 16	413271210	224	32	12.4	91.7	10.4	20.1	124
TONG DA 698	412454070	103	16	3.5	26.6	28.8	–10.2	180
YONG XING ZHOU	413203000	228	32	11.4	27.1	26.4	–12.2	150

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表 4 采用两种方法时的跟踪结果比较

关联方法	正确关联航迹数目	关联正确率(%)	平均运行时间(s)
NNDA	29	53.7	49.61
本文方法	44	81.5	54.7

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