Using More Information to Determine Trajectory Association: An Improved Method Based on Weighted Cascade Hausdorff Distance

Y. Guo, Han Jiao
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Abstract

The problem of ship trajectory association is well recognized in the overlapping area cross multiple sensors. In this paper, an improved method based on weighted cascade Hausdorff distance, with a variable time sliding window, is proposed. This method can effectively solve the problem of ship target fusion in the field of border and coastal defense, thereby providing a basis for the next step of ship monitoring and management. The similarities of tracks from different radars are determined by the proposed method, which combines the information of both ship position and motion characteristics. Several experiments are designed, with both real and simulated track data as input, to evaluate the effectiveness of the proposed method. The results showed that the method, due to the configurable parameters of time sliding window, has good universality to meet the needs of different radar frequency. The best performance among different configuration settings has the better advantage of 10%-20% improvement compared with the traditional Hausdorff distance model with fixed time window.
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利用更多信息确定轨迹关联:一种基于加权级联Hausdorff距离的改进方法
在多传感器交叉重叠区域,船舶轨迹关联问题得到了很好的认识。本文提出了一种基于加权级联豪斯多夫距离的变时滑动窗口改进方法。该方法可有效解决边海防领域舰船目标融合问题,为下一步舰船监控管理提供依据。该方法结合了舰船位置信息和运动特征信息,确定了不同雷达航迹的相似度。设计了几个实验,以真实和模拟轨迹数据为输入,来评估该方法的有效性。结果表明,由于时间滑动窗口参数可配置,该方法具有较好的通用性,可以满足不同雷达频率的需求。不同配置设置下的最佳性能比传统固定时间窗的Hausdorff距离模型有10%-20%的提升。
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