文献类型：期刊文献

英文题名：Underdetermined DOA Estimation via Covariance Matrix Completion for Nested Sparse Circular Array in Nonuniform Noise

作者：Jiang, Guojun[1,2];Mao, Xing-Peng[1,3];Liu, Yong-Tan[1,3]

机构：[1]Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150001, Peoples R China;[2]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[3]Minist Ind & Informat Technol, Key Lab Marine Environm Monitoring & Informat Pro, Harbin 150001, Peoples R China

年份：2020

卷号：27

起止页码：1824

外文期刊名：IEEE SIGNAL PROCESSING LETTERS

收录：;EI(收录号：20212010348936);WOS:【SCI-EXPANDED(收录号:WOS:000583745300001)】；

基金：This work was supported by the Key Program of National Natural Science Foundation of China under Grant 61831009. The associate editor coordinating the reviewof this manuscript and approving it for publication was Prof. Xun Cao.

语种：英文

外文关键词：Covariance matrices; Direction-of-arrival estimation; Estimation; Sensor arrays; Signal processing algorithms; Array signal processing; Direction of arrival estimation; nested sparse circular array; nonuniform noise; covariance matrix completion

摘要：This paper proposes a covariance matrix completion based algorithm for underdetermined direction of arrival (DOA) estimation in the presence of unknown nonuniform noise using nested sparse circular array (NSCA) with only N sensors. The proposed algorithm provides a systematic procedure to complete a covariance matrix for a virtual uniform circular array (UCA) with Msensors (M > N). Comparedwith the covariance matrix of the NSCA, the completed covariance matrix is capable of increasing degrees of freedom (DOFs), and is noise-free to mitigate the effect of nonuniform noise. The elements of the completed covariance matrix are from three steps: (1) elements from covariance matrix of the NSCA; (2) elements generated from the properties of the UCA; (3) elements produced from output of oblique projection operator based on initial DOAs. Then compressive sensing (CS) method is used to estimate DOAs based on the completed covariance matrix for better performance. The computational complexity of the proposed algorithm, and CRB are also given. Simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art methods in estimation accuracy.