基于核密度杂波估计的改进MHT算法  

An Improved MHT Method Based on Kernel Density Clutter Estimation

作　　者：王子微 孙进平[1] 赵楚楚 WANG Ziwei;SUN Jinping;ZHAO Chuchu(School of Electronic and Information Engineering,Beihang University,Beijing 100191,China)

机构地区：[1]北京航空航天大学电子信息工程学院,北京100191

出　　处：《信号处理》2021年第6期991-999,共9页Journal of Signal Processing

基　　金：国家自然科学基金(62073334)。

摘　　要：传统多假设跟踪(Multiple Hypothesis Tracker,MHT)算法假定杂波强度先验已知,在未知杂波的观测场景中,杂波强度误差将导致数据关联的准确性急剧下降。针对这一问题,本文提出一种基于核密度估计(Kernel Density Estimation,KDE)的在线杂波估计MHT算法。首先利用核密度函数拟合未知的杂波密度函数,并自适应地估计出该时刻波门内的杂波强度;然后利用杂波强度估计值计算假设航迹的得分函数,提高了数据关联的准确性和目标跟踪的稳定性。仿真结果表明,在未知杂波观测场景中,MHT-KDE算法有效改善了航迹的连续性,减少了虚假航迹数。The traditional multiple hypothesis tracking(MHT)algorithm assumes that the clutter intensity is known a priori.In the observation scene of unknown clutter,the clutter intensity error will lead to a sharp decline in the accuracy of data association.To solve this problem,this paper proposes an improved MHT method with clutter estimation based on kernel density estimation(KDE).Firstly,the kernel density function is used to fit the unknown clutter spatial distribution,and the clutter intensity in the gate at that time is estimated adaptively;then the score function of the track hypothesis is calculated by using the obtained clutter intensity,which improves the accuracy of data association and the stability of target tracking.Simulation results show that MHT-KDE algorithm can effectively improve the track continuity and reduce the number of false tracks in unknown clutter observation scene.

关 键 词：多假设跟踪 核密度估计 杂波强度 杂波空间分布 航迹得分 

分 类 号：TN953[电子电信—信号与信息处理]