An Optimal Plot-to-Track Association Method Based on JVC Algorithm for Maritime Target with Compact HFSWR
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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。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.
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Key words:
- Compact HFSWR /
- Multitarget tracking /
- Plot-to-track association /
- Optimal association
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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 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 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 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 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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