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Sensors 2017, 17(5), 1068; doi:10.3390/s17051068

Joint Smoothed l0-Norm DOA Estimation Algorithm for Multiple Measurement Vectors in MIMO Radar

1
College of Automation, Harbin Engineering University, Harbin 150001, China
2
School of Electrical, Electronic and Computer Engineering, The University of Western Australia, Perth 6009, Australia
3
Department of Electrical Engineering, Universitas Indonesia, Depok 16424, Indonesia
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 13 March 2017 / Revised: 28 April 2017 / Accepted: 2 May 2017 / Published: 8 May 2017
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [948 KB, uploaded 8 May 2017]   |  

Abstract

Direction-of-arrival (DOA) estimation is usually confronted with a multiple measurement vector (MMV) case. In this paper, a novel fast sparse DOA estimation algorithm, named the joint smoothed l 0 -norm algorithm, is proposed for multiple measurement vectors in multiple-input multiple-output (MIMO) radar. To eliminate the white or colored Gaussian noises, the new method first obtains a low-complexity high-order cumulants based data matrix. Then, the proposed algorithm designs a joint smoothed function tailored for the MMV case, based on which joint smoothed l 0 -norm sparse representation framework is constructed. Finally, for the MMV-based joint smoothed function, the corresponding gradient-based sparse signal reconstruction is designed, thus the DOA estimation can be achieved. The proposed method is a fast sparse representation algorithm, which can solve the MMV problem and perform well for both white and colored Gaussian noises. The proposed joint algorithm is about two orders of magnitude faster than the l 1 -norm minimization based methods, such as l 1 -SVD (singular value decomposition), RV (real-valued) l 1 -SVD and RV l 1 -SRACV (sparse representation array covariance vectors), and achieves better DOA estimation performance. View Full-Text
Keywords: direction-of-arrival estimation; joint smoothed l0-norm; multiple measurement vectors; sparse signal reconstruction; MIMO radar direction-of-arrival estimation; joint smoothed l0-norm; multiple measurement vectors; sparse signal reconstruction; MIMO radar
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Liu, J.; Zhou, W.; Juwono, F.H. Joint Smoothed l0-Norm DOA Estimation Algorithm for Multiple Measurement Vectors in MIMO Radar. Sensors 2017, 17, 1068.

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