雷达信息二次处理过程中被跟踪轨迹与标记的吸引关联

U. A. Aparovich
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引用次数: 0

摘要

本文提出了一种新的算法,用于在雷达信息的二次处理(边扫描边跟踪)过程中,通过坐标将跟踪轨迹与新接收的标记相关联。众所周知,当在密集组中进行关联时,即当轨迹之间的距离与测量其坐标的误差相当时,会出现最大的困难。通常,使用众所周知的方法来解决分配任务,例如,匈牙利算法等。这些方法的一个常见缺点是解决任务的时间迅速增加(与轨迹数量的三阶或四阶成比例)。文章提出采用“吸引”法来提高计算速度。所提出的算法模拟了所有轨迹对所有标记的“吸引力”以及所有轨迹彼此之间的相互“排斥”(轨迹的位置外推到标记的位置时间。)轨迹的条件“移动”是一步一步模拟的,直到对任何标记出现设定的方法。在轨迹和标记数量相等的情况下,吸引算法和匈牙利算法的比较建模表明,两种算法的定性特征大致相同,但吸引算法的执行时间比匈牙利算法增长得慢(与轨迹数量的平方成比例)。因此,有了大量的吸引算法(超过100-300),吸引算法的执行速度会快得多。显然,通过对参数的值和维度进行相应的调整,新算法可以用于解决其他分配任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Association of the Tracked Trajectories and Marks by the Attraction Method During Secondary Processing of Radar Information
The article proposes a new algorithm for associating the tracked trajectories and newly received marks by coordinates during the secondary processing (track-while-scan) of radar information. It is known that the biggest difficulties arise when associating in dense groups, that is, when the distance between the trajectories is commensurate with the errors in measuring their coordinates. Usually, well-known methods for solving the assignment task are used, for example, the Hungarian algorithm and similar. A common disadvantage of these methods is a rapid increase (in proportion to the third or fourth degree of the number of trajectories) in the time for solving the task. The article proposes to use the “attraction” method to increase the speed of calculations. The proposed algorithm simulates the “attraction” of all trajectories to all marks and the mutual “repulsion” of all trajectories from each other (the position of the trajectories is extrapolated to the time of location of the marks.) The conditional “movement” of the trajectories is simulated step by step until a set approach to any marks happens. Comparative modeling of the attraction algorithm and the Hungarian algorithm in the case of equal number of trajectories and marks showed that the qualitative characteristics of the algorithms are approximately the same, but the execution time for the attraction algorithm grows more slowly than for the Hungarian algorithm (in proportion to the square of the number of trajectories). Therefore, with a large number of them (more than 100–300), the attraction algorithm is executed much faster. Obviously, with the corresponding adjustment of the value and dimensions of the parameters, the new algorithm can be used to solve other assignment tasks.
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