Underdetermined DOA Estimation Using MVDR-Weighted LASSO

作     者：Salama, Amgad A. Ahmad, M. Omair Swamy, M. N. S. 

作者机构：Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada 

出 版 物：《SENSORS》 (传感器)

年 卷 期：2016年第16卷第9期

页      面：1549-1549页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

基　　金：Natural Sciences and Engineering Research Council (NSERC) of Canada Regroupment Strategic en Microelectronique de Quebec (ReSMiQ) Egyptian Government 

主　　题：adaptable LASSO sparse array direction of arrival estimation compressive sensing sensor array processing 

摘      要：The direction of arrival (DOA) estimation problem is formulated in a compressive sensing (CS) framework, and an extended array aperture is presented to increase the number of degrees of freedom of the array. The ordinary least square adaptable least absolute shrinkage and selection operator (OLS A-LASSO) is applied for the first time for DOA estimation. Furthermore, a new LASSO algorithm, the minimum variance distortionless response (MVDR) A-LASSO, which solves the DOA problem in the CS framework, is presented. The proposed algorithm does not depend on the singular value decomposition nor on the orthogonality of the signal and the noise subspaces. Hence, the DOA estimation can be done without a priori knowledge of the number of sources. The proposed algorithm can estimate up to ((M2 2) /2 + M 1) /2 sources using M sensors without any constraints or assumptions about the nature of the signal sources. Furthermore, the proposed algorithm exhibits performance that is superior compared to that of the classical DOA estimation methods, especially for low signal to noise ratios (SNR), spatially-closed sources and coherent scenarios.