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

作　　者：唐孟麒 李波[1] 郝丽君 TANG Mengqi;LI Bo;HAO Lijun(School of Electronics and Information Engineering,Liaoning University of Technology,Jinzhou 121001,China)

机构地区：[1]辽宁工业大学电子与信息工程学院,辽宁锦州121001

出　　处：《智能系统学报》2022年第4期806-813,共8页CAAI Transactions on Intelligent Systems

基　　金：辽宁省自然科学基金面上项目(2020-MS-292);国家自然科学基金面上项目(51679116).

摘　　要：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.

关 键 词：扩展目标 量测集 网格聚类 时空关联 模糊C均值 存活目标 新生目标 概率密度假设 

分 类 号：TN713[电子电信—电路与系统] TP39[自动化与计算机技术—计算机应用技术]