基于大地距离计算相似度的海上目标轨迹预测

Trajectory prediction of sea targets based on geodetic distance similarity calculation

目前基于相似度的移动目标轨迹预测算法一般根据数据的时空特性进行分类,无法体现算法自身的特点,为此提出一种基于算法特征的分类方法。轨迹相似度算法通常需要先计算两点之间的距离,再开展后续计算,而常用的欧氏距离(ED)只适用于目标在小区域范围内移动的问题。针对现有基于相似度的轨迹预测算法无法适用于移动范围比较大的海上目标轨迹预测的问题,提出使用大地距离代替ED进行相似度计算。首先,对轨迹数据进行预处理和分段;其次采用离散弗雷歇距离(FD)作为相似性度量;最后,利用模拟数据和实际数据进行测试。实验结果表明,当海上目标移动范围较大时,采用ED算法可能会得到不正确的预测结果,而所提算法可输出正确的目标轨迹预测结果。

The existing similarity-based moving target trajectory prediction algorithms are generally classified according to the spatial-temporal characteristics of the data,and the characteristics of the algorithms themselves cannot be reflected.Therefore,a classification method based on algorithm characteristics was proposed.The calculation of the distances between two points is required for the trajectory similarity algorithms to carry out the subsequent calculations,however,the commonly used Euclidean Distance(ED)is only applicable to the problem of moving targets in a small region.A method of similarity calculation using geodetic distance instead of ED was proposed for the trajectory prediction of sea targets moving in a large region.Firstly,the trajectory data were preprocessed and segmented.Then,the discrete Fréchet Distance(FD)was adopted as similarity measure.Finally,synthetic and real data were used to test.Experimental results indicate that when sea targets move in a large region,the ED-based algorithm may gain incorrect prediction results,while the geodetic distancebased algorithm can output correct trajectory prediction.