2D-DOA and Polarization Estimation Using a Novel Sparse Represent on of Covariance Matrix With COLD Array

作     者：Si, Weijian Wang, Yan Zhang, Chunjie 

作者机构：Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Heilongjiang Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2018年第6卷

页      面：66385-66395页

核心收录：

基　　金：National Natural Science Foundation of China Natural Science Foundation of Heilongjiang Province [QC2016085] Fundamental Research Funds for the Central Universities [HEUCFJ180801, HEUCF180801] 

主　　题：Direction of arrival l(1)-norm penalty polarization sensitive array signal processing spares signal representation two-dimensional multi-resolution grid refinement 

摘      要：In this paper, we propose a novel sparse signal representation (SSR)-based algorithm called the l(1)-PSRCM with cocentered orthogonal loop and dipole (COLD) array to estimate two-dimensional (2D) direction of arrival (DOA) and polarization parameters. Considering the characteristics of polarization sensors, a polarized sparse representation model of covariance matrix is constructed, whose overcomplete dictionary and sparse coefficient matrix only depend on DOA and polarization parameters, respectively. In so doing, the proposed algorithm can make full use of the spatial and polarized information contained in the received data, thereby improving the estimation accuracy. In addition, to reduce the computational complexity and suppress the effect of noise, the modified l(1)-PSRCM algorithm with the real-valued sparse coefficient matrix and noise-free sparse representation model is proposed. Finally, we present the two-dimensional multiresolution grid refinement (2D-MGR) method to reduce the heavy computation burden when the spatial grid is dense. Simulation results validate the superiority of the proposed algorithms.