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Sensors 2016, 16(9), 1549; doi:10.3390/s16091549

Underdetermined DOA Estimation Using MVDR-Weighted LASSO

Department of Electrical and Computer Engineering, Concordia University, Montreal, PQ H3G 1M8, Canada
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Author to whom correspondence should be addressed.
Academic Editor: Wilmar Hernandez
Received: 10 June 2016 / Revised: 2 September 2016 / Accepted: 13 September 2016 / Published: 21 September 2016
(This article belongs to the Section Physical Sensors)
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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 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. View Full-Text
Keywords: adaptable LASSO; sparse array; direction of arrival estimation; compressive sensing; sensor array processing adaptable LASSO; sparse array; direction of arrival estimation; compressive sensing; sensor array processing
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MDPI and ACS Style

Salama, A.A.; Ahmad, M.O.; Swamy, M.N.S. Underdetermined DOA Estimation Using MVDR-Weighted LASSO. Sensors 2016, 16, 1549.

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