地波雷达与自动识别系统目标点迹最优关联算法

为了提高海洋探测精度和范围，针对高频地波雷达(HFSWR)和自动识别系统(AIS)目标点迹的融合利用问题，该文提出一种基于JVC(Jonker-Volgenant-Castanon)的点迹分状态全局最优关联算法。首先，通过判断高频地波雷达和AIS点迹的径向速度，将点迹分为准静态目标和动态目标。接着，选取径向速度和点迹间的球面距离为特征参数，对不同状态下目标点迹分别进行径向速度和位置间球面距离粗关联。最后，使用相对距离比的平均值进行关联效果的评价，通过选择合适的关联门限参数，使用JVC算法实现高频地波雷达和AIS的点迹最优关联。实验结果表明:该算法在关联相同点迹对数的情况下，关联精度高于最近邻(NN)算法和Munkres关联法，关联用时少于最近邻算法和Munkres关联法。通过近3年内3组不同时刻实测目标点迹的验证，该算法可以满足关联的实用性和实时性要求。

Target Point Tracks Optimal Association Algorithm with Surface Wave Radar and Automatic Identification System

In order to solve the problem that of High Frequency Surface Wave Radar (HFSWR) and Automatic Identification System (AIS) target point tracks fusion, a point tracks association algorithm using Jonker- Volgenant-Castanon (JVC) global optimal matching for different status is proposed. Firstly, the HFSWR and AIS target point tracks are divided into the quasi-static and dynamic data by the radial velocity. Then the radial velocity and spherical distance are selected as the feature parameters, and the different status data are respectively pre-associated by the radial velocity and spherical distance. Finally, the average of relative distance ratio is used to evaluate the effect of association. According to the selection of threshold parameter, the HFSWR and AIS point tracks are optimal associated with the JVC algorithm. The experimental results indicate that the proposed algorithm, in the condition of equal number point tracks associated, is superior to the Nearest Neighbor (NN) algorithm and Munkres association algorithm in the association accuracy, and the associate time is less than the NN algorithm and Munkres association. Moreover, three different time data gained from the target traits measured in nearly three years demonstrate that the feasibility and real-time of the proposed method.