title: Real-Valued Covariance Vector Sparsity-Inducing DOA Estimation for Monostatic MIMO Radar.
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.
authors.affiliation: College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China. wangxianpeng@hrbeu.edu.cn.
authors.forename: Xianpeng
authors.initials: X
authors.lastname: Wang
authors.affiliation: College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China. wangwei407@hrbeu.edu.cn.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.affiliation: College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China. xinxin_forever@sohu.com.
authors.forename: Xin
authors.initials: X
authors.lastname: Li
authors.affiliation: College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China. angleliujing@126.com.
authors.forename: Jing
authors.initials: J
authors.lastname: Liu
keywords: DOA estimation; Khatri–Rao product; MIMO radar; sparse representation; unitary transformation