Persymmetric adaptive detection of range-spread targets in subspace interference plus Gaussian clutter  

作　　者：Tao JIAN Jia HE Yu LIU You HE Congan XU Zikeng XIE 

机构地区：[1]Research Institute of Information Fusion,Naval Aviation University,Yantai 264001,China

出　　处：《Science China(Information Sciences)》2023年第5期267-278,共12页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant Nos.61971432,61790551);Taishan Scholar Project of Shandong Province(Grant No.tsqn201909156);Outstanding Youth Innovation Team Program of University in Shandong Province(Grant No.2019KJN031);Technical Areas Foundation for Fundamental Strengthening Program(Grant No.2019-JCJQ-JJ-060)。

摘　　要：In this paper,we consider the adaptive detection problem of range-spread targets embedded in subspace interference plus structured Gaussian clutter.The target signal and interference are assumed to lie in two linearly independent subspaces with unknown coordinates.The clutter component is modeled as a complex Gaussian vector with an unknown persymmetric covariance matrix.We leverage the persymmetric structure to design a two-step detector according to the Rao test criterion.The theoretical results show that the proposed detector possesses the constant false alarm rate property with respect to the clutter covariance matrix.Furthermore,the numerical results show that the proposed detector exhibits better detection performance than the existing unstructured subspace detectors,particularly under a limited training data size.In addition,the proposed detector outperforms the existing persymmetric subspace detectors.

关 键 词：adaptive detection persymmetry structured interference constant false alarm rate Rao test 

分 类 号：TN957.51[电子电信—信号与信息处理]