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Sensors 2015, 15(11), 28271-28286; doi:10.3390/s151128271

Real-Valued Covariance Vector Sparsity-Inducing DOA Estimation for Monostatic MIMO Radar

College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China
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Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 21 July 2015 / Revised: 22 September 2015 / Accepted: 3 November 2015 / Published: 10 November 2015
(This article belongs to the Section Physical Sensors)
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Abstract

In this paper, a real-valued covariance vector sparsity-inducing method for direction of arrival (DOA) estimation is proposed in monostatic multiple-input multiple-output (MIMO) radar. Exploiting the special configuration of monostatic MIMO radar, low-dimensional real-valued received data can be obtained by using the reduced-dimensional transformation and unitary transformation technique. Then, based on the Khatri–Rao product, a real-valued sparse representation framework of the covariance vector is formulated to estimate DOA. Compared to the existing sparsity-inducing DOA estimation methods, the proposed method provides better angle estimation performance and lower computational complexity. Simulation results verify the effectiveness and advantage of the proposed method. View Full-Text
Keywords: MIMO radar; DOA estimation; sparse representation; unitary transformation; Khatri–Rao product MIMO radar; DOA estimation; sparse representation; unitary transformation; Khatri–Rao product
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Wang, X.; Wang, W.; Li, X.; Liu, J. Real-Valued Covariance Vector Sparsity-Inducing DOA Estimation for Monostatic MIMO Radar. Sensors 2015, 15, 28271-28286.

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