面向扩展目标跟踪的网格聚类量测划分方法

Grid clustering measurement set partition method for extended target tracking

针对扩展目标跟踪中量测集划分困难及目标数目估计不准的问题,提出了一种面向扩展目标跟踪的网格聚类量测集划分方法。首先,由目标之间的时空关联性,将当前时刻的量测划分为存活目标量测与新生目标量测。然后,针对高斯混合概率假设密度滤波器与扩展目标高斯混合概率假设密度滤波器,分别推导出改进的模糊C均值算法与改进的网格聚类算法用于划分存活目标量测集与新生目标量测集。仿真结果表明本文方法可实现量测集的准确划分,有效完成扩展目标跟踪,避免了漏检与过检。

To address the issues of difficult measurement set partitioning and inaccurate estimation of the number of targets in extended target tracking,we suggest a grid clustering measurement set partitioning approach for extended target tracking.Firstly,the current moment measurement is classified into two categories based on the time-space correlation between the targets:survival-target measurement and newborn-target measurement.Then,an improved fuzzy C-means algorithm and an improved grid clustering algorithm are derived for the Gaussian mixture probability hypothesis density filter and the extended target Gaussian mixture probability hypothesis density filter,respectively,which are employed to separate the viable target set and the new target set.The simulation results show that the proposed techniques can accurately divide the measurement set,effectively complete the extended target tracking,and avoid the missed and over-checked measurements.