基于ICP的稳态部分可辨编队目标精细跟踪算法

针对稳态编队在部分可辨条件下精细跟踪的难题,提出了一种基于迭代就近点(ICP)的稳态部分可辨编队精细跟踪算法。首先将ICP算法思想应用于编队成员拓扑的点航关联中,将k时刻的位置状态估计通过最近点循环迭代逼近k+1时刻的量测,在关联判决时采用双门限原则应对部分可辨所带来的漏观测问题,以提高关联时的容错性能;进而采用概率最近邻对漏观测航迹进行填补,以进一步保证跟踪的可靠性;最后,采用多模型法实现编队成员航迹滤波更新,以保证航迹的跟踪滤波精度。仿真结果表明,与现有的基于模版匹配的编队目标跟踪算法以及经典的多假设多目标跟踪算法相比,该算法具有较高的跟踪可靠性与精度,且在编队拓扑发生缓慢变化时具有更高的正确跟踪率。

Refined tracking algorithm for steady partly resolvable group targets based on ICP

To deal with the problem of the refined tracking of steady groups in partly resolvable condition,a refined tracking algorithm based on iterative closest point (ICP) is proposed in this paper.First,the ICP algorithm is used in tracking association,and by using closest point cyclic iteration,the measurements at time k + 1 can be matched with the position estimation at time k.In order to deal with the problem of leakage tracks brought by partly resolvable group and to increase the fault tolerant performance in tracking association,double threshold principle is used in decision making.Then,to further ensure the reliability of tracking,probabilistic nearest neighbor method has been used to fill the leakage tracks.Finally,to ensure the precision of tracking,multi-model algorithm is used to realize filter update of group member tracking.The simulation results show that,compared with group target tracking algorithm based on template matching and classical multiple hypothesis tracking algorithm,the algorithm has better performance in tacking reliability and precision,and can be more accurate when slow change of group topology happens.