复合高斯杂波下雷达目标自适应检测方法  

Adaptive Detectors for Radar Targets in Compound Gaussian Clutter

作　　者：简涛 马颖亮 谢梓铿 魏广芬[2] 何佳 JIAN Tao;MA Yingliang;XIE Zikeng;WEI Guangfen;HE Jia(Research Institute of Information Fusion,Naval Aviation University,Yantai,Shandong 264001,China;School of Information and Electronic Engineering,Shandong Technology and Business University,Yantai,Shandong 264005,China)

机构地区：[1]海军航空大学信息融合研究所,山东烟台264001 [2]山东工商学院信息与电子工程学院,山东烟台264005

出　　处：《信号处理》2025年第4期739-748,共10页Journal of Signal Processing

基　　金：国家自然科学基金(62471483,61971432);泰山学者工程专项经费资助(tsqn201909156);山东省高等学校青创科技支持计划(2019KJN031)。

摘　　要：在高距离分辨率的宽带雷达探测场景下,背景杂波呈现出越来越强的非高斯特性,可用复合高斯分布进行合理建模;不同于传统窄带雷达的点目标,宽带雷达目标在径向距离维上展现距离扩展形式。针对复合高斯杂波下现有距离扩展目标检测方法对杂波纹理分量信息利用不足的问题,在未知协方差矩阵结构的复合高斯杂波背景下,将纹理分量建模为逆Gamma分布,进而研究了雷达距离扩展目标自适应子空间检测方法。根据两步法设计策略和不同统计检验理论,构建了三种自适应子空间检测器。首先,在杂波纹理分量和协方差矩阵结构已知的条件下,基于Rao检验、Wald检验和Gradient检验,推导了三种检测统计量。其次,通过求解未知纹理分量的最大后验估计和未知协方差矩阵结构的迭代估计,构建了距离扩展目标的三种自适应子空间检测器;其中,所提出的Rao检测器和Gradient检测器具有一致性。理论证明了所提三种检测器对未知协方差矩阵结构的渐进恒虚警率特性。仿真分析了目标散射模型、辅助数据量、杂波相关性等因素对所提检测器的性能影响;实验结果表明,针对均匀高斯环境设计的现有检测器在复合高斯环境下检测性能退化较为严重,所提检测器的检测性能均优于现有复合高斯环境下的对比检测器;其中所提出的Wald检测器略优于所提出的Rao检测器。In scenarios involving wideband radar detection with high range resolution,background clutter exhibits increasingly strong non-Gaussian characteristics.As a typical non-Gaussian distribution model,the compound Gaussian model is reasonably used to describe the non-Gaussian statistical properties of clutter in practical environments.Unlike the point-like targets of traditional narrowband radars,wideband radar targets demonstrate extension among the radar radial range.Currently,many detectors designed for range-spread targets are mainly tailored to homogeneous Gaussian clutter environments,which makes them less applicable to practical detection scenarios.To address the issue of the insufficient utilization of information from clutter textures in the existing detectors of range-spread targets,especially under the background of compound Gaussian clutter with an unknown covariance matrix structure,this study models the texture component as an inverse Gamma distribution and investigates several adaptive subspace detectors for radar range-spread targets.According to the two-step design procedure and different test criteria,three adaptive subspace detectors are established.First,by assuming that the clutter texture component and covariance matrix structure are both known,three detection statistics are derived based on the Rao,Wald,and Gradient tests.Second,by solving the maximum a posteriori estimate of the unknown texture component and iterative estimate of the unknown covariance matrix structure and then substituting these estimates into the detection statistics obtained in the first step,three adaptive subspace detectors can be constructed for range-spread targets.Among them,the detection statistic of the proposed Rao test is interestingly consistent with that of the Gradient test.The theoretical results show that the three proposed detectors possess an asymptotic constant false alarm rate property with respect to the unknown covariance matrix structure,which serves as a pivotal criterion delineating the proficiency of

关 键 词：复合高斯 距离扩展目标检测 Gradient检验 Rao检验 WALD检验 

分 类 号：TN95[电子电信—信号与信息处理]