超视距雷达多目标跟踪的随机样本一致性算法

Hua Lan, Zhishan Zhang, ZengfuWang, Q. Pan
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引用次数: 1

摘要

超视距雷达(OTHR)中的多径传播现象给多目标跟踪带来了两大挑战。第一种是多路径检测,自动检测出现和消失的目标,一个目标可能为5个传播路径产生5个轨迹。第二种是多路径跟踪,通过计算目标-测量-路径分配矩阵来估计目标状态,这是由于组合爆炸导致的计算难题。提出了一种基于随机样本一致性(RANSAC)的联合多径目标检测与跟踪方法。利用RANSAC的迭代假设-检验框架,建立了目标-测量-路径辨识与目标状态估计之间的闭环,有利于利用多路径测量提高跟踪性能。数值仿真验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Random sample consensus algorithm for multiple target tracking in over-the-horizon radar
Multiple target tracking in over-the-horizon radar (OTHR) suffers from two major challenges due to the multipath propagation phenomenon. The first is multipath detection that detects appearing and disappearing targets automatically, while one target may produce s tracks for s propagation paths. The second is multipath tracking that calculates the target-to-measurement-to-path assignment matrices to estimate target states, which is computationally intractable due to the combinatorial explosions. A joint multipath target detection and tracking method is proposed based on random sample consensus (RANSAC). Using the iterative hypothesize-and-test framework of RANSAC, the close loop between identification of target-to-measurement-to-path and estimation of target states is established, which is conducive to improving the tracking performance by utilizing multipath measurements. Numerical simulations demonstrate the effectiveness of the proposed method.
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