Four-Dimensional Parameter Estimation for Mixed Far-Field and Near-Field Target Localization Using Bistatic MIMO Arrays and Higher-Order Singular Value Decomposition

作     者：Zhang, Qi Jiang, Hong Zheng, Huiming 

作者机构：Jilin Univ Coll Commun Engn Changchun 130012 Peoples R China 

出 版 物：《REMOTE SENSING》 (Remote Sens.)

年 卷 期：2024年第16卷第18期

页      面：3366页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1002[医学-临床医学] 070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：Natural Science Foundation of Jilin Province National Natural Science Foundation of China [61371158, 61771217] 20220101100JC 20180101329JC 

主　　题：near-field and far-field target localization multidimensional parameter estimation higher-order singular value decomposition (HOSVD) tensor 

摘      要：In this paper, we present a novel four-dimensional (4D) parameter estimation method to localize the mixed far-field (FF) and near-field (NF) targets using bistatic MIMO arrays and higher-order singular value decomposition (HOSVD). The estimated four parameters include the angle-of-departure (AOD), angle-of-arrival (AOA), range-of-departure (ROD), and range-of-arrival (ROA). In the method, we store array data in a tensor form to preserve the inherent multidimensional properties of the array data. First, the observation data are arranged into a third-order tensor and its covariance tensor is calculated. Then, the HOSVD of the covariance tensor is performed. From the left singular vector matrices of the corresponding module expansion of the covariance tensor, the subspaces with respect to transmit and receive arrays are obtained, respectively. The AOD and AOA of the mixed FF and NF targets are estimated with signal-subspace, and the ROD and ROA of the NF targets are achieved using noise-subspace. Finally, the estimated four parameters are matched via a pairing method. The Cram & eacute;r-Rao lower bound (CRLB) of the mixed target parameters is also derived. The numerical simulations demonstrate the superiority of the tensor-based method.