title: Underdetermined DOA Estimation Using MVDR-Weighted LASSO.
abstract: 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 ( ( M 2 - 2 ) / 2 + M - 1 ) / 2 sources usingMsensors 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.
authors.affiliation: Department of Electrical and Computer Engineering, Concordia University, Montreal, PQ H3G 1M8, Canada. am_adeli@encs.concordia.ca.
authors.forename: Amgad A
authors.initials: AA
authors.lastname: Salama
authors.affiliation: Department of Electrical and Computer Engineering, Concordia University, Montreal, PQ H3G 1M8, Canada. omair@ece.concordia.ca.
authors.forename: M Omair
authors.initials: MO
authors.lastname: Ahmad
authors.affiliation: Department of Electrical and Computer Engineering, Concordia University, Montreal, PQ H3G 1M8, Canada. swamy@ece.concordia.ca.
authors.forename: M N S
authors.initials: MN
authors.lastname: Swamy
keywords: adaptable LASSO; compressive sensing; direction of arrival estimation; sensor array processing; sparse array