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

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 obt...

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Bibliographic Details
Main Authors: Xianpeng Wang, Wei Wang, Xin Li, Jing Liu
Format: Article
Language:English
Published: MDPI AG 2015-11-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/15/11/28271
Description
Summary: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.
ISSN:1424-8220