基于自适应GMM杂波估计的改进MHT算法  

An improved MHT method with clutter estimation based on adaptive Gaussian Mixture Model

作　　者：李旭东[1] 王子微 张玉玺[2] 陆小科[1] LI Xudong;WANG Ziwei;ZHANG Yuxi;LU Xiaoke(Nanjing Electronic Technology Research Institute,Nanjing Jiangsu 210039,China;School of Electronic and Information Engineering,Beihang University,Beijing 100191,China)

机构地区：[1]南京电子技术研究所,江苏南京210039 [2]北京航空航天大学电子信息工程学院,北京100191

出　　处：《太赫兹科学与电子信息学报》2023年第6期794-800,共7页Journal of Terahertz Science and Electronic Information Technology

基　　金：国家自然科学基金资助项目(62073334)。

摘　　要：在传统多假设跟踪(MHT)算法中通常会假设杂波强度先验已知,当观测场景中杂波未知且空变时,该假设将会导致跟踪算法性能急剧下降。针对这一问题,本文提出一种基于自适应高斯混合模型(GMM)在线估计未知杂波的改进MHT算法。首先利用自适应GMM拟合未知杂波空间分布,并自适应地估计出波门内的杂波强度;然后将其应用于MHT处理中,有效改善航迹得分计算和最优假设航迹估计的准确性,进而实现在杂波未知场景中的稳定跟踪。仿真结果表明,在未知杂波观测场景中,所提算法相比传统MHT算法和MHT-GMM算法获得了更好的数据关联准确性和航迹维持性能。In the traditional Multiple Hypothesis Tracker(MHT)algorithm,it is usually assumed that the clutter intensity is known a priori.When the clutter of observation scene is unknown and spatially variable,the performance of the tracking algorithm drops sharply.To solve this problem,an improved MHT method with clutter estimation based on adaptive Gaussian Mixture Model(GMM)is proposed.Firstly,the adaptive GMM is utilized to fit the spatial distribution of unknown clutter,and the clutter intensity in the gate is estimated adaptively.Then,it is applied to the MHT tracker to effectively improve the accuracy of track score calculation and optimal hypothetical track estimation,so as to realize stable tracking in unknown clutter scene.Simulation results show that the proposed algorithm achieves better data association accuracy and track maintenance performance than the standard MHT algorithm and the MHT-GMM algorithm in unknown clutter observation scene.

关 键 词：多假设跟踪 杂波强度 自适应高斯混合模型 航迹得分 最优假设航迹 

分 类 号：TN953[电子电信—信号与信息处理]