密集杂波环境下基于KHM的多目标数据关联

Data Association for Multiple Targets Based on KHM in Dense Clutters

针对在杂波密集、多目标交叉的场景中,传统聚类算法在数据关联中出现的用聚类中心点更新航迹产生较大误差。为减少误差,提高航迹精度,提出一种基于KHM(k-Harmonic Means)的多目标数据关联算法。算法首次将KHM用于多目标的数据关联问题中,先对接收到的量测数据通过KHM算法进行聚类处理,然后结合最近邻思想完成量测-航迹的关联,在目标出现密集交叉时,引入KHM的"软聚类"方法,实现了量测-航迹的精确关联。采用蒙特卡罗仿真结果表明,基于KHM的关联算法在精度上要优于PDA算法,并验证了算法的可信性和有效性。

A new data association algorithm based on KHM for multiple targets in dense clutters is proposed.The KHM method is applied firstly to data association for multiple targets.The proposed algorithm firstly classifies the received measurements,then combines the nearest neighbor method to carry out the measurement-track association.By introducing the idea of soft clustering of the KHM,association is exactly realized in closer targets environment.The Monte Carlo simulation results show that the association precision of the algorithm based on KHM is superior to PDA,therefore,it verifies the feasibility and validity of the proposed algorithm.