一种基于杂波稀疏度改进的杂波密度估计方法  

An Extended Clutter Density Estimation Method Based on Clutter Sparsity

作　　者：周福珍 唐新丰 石义芳 彭冬亮[1] 郭云飞[1] ZHOU Fuzhen;TANG Xinfeng;SHI Yifang;PENG Dongliang;GUO Yunfei(School of Automation,Hangzhou Dianzi University,Hangzhou 310018,China;Beijing Institute of Astronautical System Engineering,Beijing 100076,China)

机构地区：[1]杭州电子科技大学自动化学院,浙江杭州310018 [2]北京宇航系统工程研究所,北京100076

出　　处：《无线电工程》2022年第3期376-383,共8页Radio Engineering

基　　金：浙江省自然科学基金青年项目(LQ19F010009)。

摘　　要：在实际跟踪环境中,杂波测量空间分布特性往往是未知时变的,杂波密度通常被用来描述杂波测量的空间分布特性,是决定多目标自动跟踪性能的核心环境要素。现有的空间稀疏度的杂波密度估计方法(Spatial Clutter Measurement Density Estimator,SCMDE)在多目标自动跟踪场景下的杂波密度估计偏差急剧增大。针对以上问题,提出了一种基于SCMDE改进的杂波自适应估计方法,通过计算以待估点为中心的超球体内测量来源于杂波的概率估计超球体内真实的杂波个数,消除超球体内目标测量带来杂波密度估计偏差,从而提升复杂环境下多目标自动跟踪的航迹管理性能。In realistic target tracking application,the spatial distribution characteristics of clutter measurement are often unknown and time-varying.Clutter density is usually used to describe the spatial distribution characteristics of clutter measurement,and is an important environmental parameter for multi-target automatic tracking in clutter.The estimation error of the existing Spatial Clutter Measurement Density Estimation(SCMDE)method increases dramatically in the multi-target automatic tracking scenario.To solve the problem caused by SCMDE,an extended adaptive cluster estimation method is proposed,which adaptively evaluates the probability that the measurement inside the hypersphere is originated from the clutter,so as to eliminate the deviation caused by target measurements,resulting in a significant improvement on the track management performance for multi-target automatic tracking in clutter.

关 键 词：杂波密度估计 多目标跟踪 航迹管理 杂波概率 杂波稀疏度 数据关联 

分 类 号：TN911.7[电子电信—通信与信息系统]