title: The Real-Valued Sparse Direction of Arrival (DOA) Estimation Based on the Khatri-Rao Product.
abstract: There is a problem that complex operation which leads to a heavy calculation burden is required when the direction of arrival (DOA) of a sparse signal is estimated by using the array covariance matrix. The solution of the multiple measurement vectors (MMV) model is difficult. In this paper, a real-valued sparse DOA estimation algorithm based on the Khatri-Rao (KR) product called the L₁-RVSKR is proposed. The proposed algorithm is based on the sparse representation of the array covariance matrix. The array covariance matrix is transformed to a real-valued matrix via a unitary transformation so that a real-valued sparse model is achieved. The real-valued sparse model is vectorized for transforming to a single measurement vector (SMV) model, and a new virtual overcomplete dictionary is constructed according to the KR product's property. Finally, the sparse DOA estimation is solved by utilizing the idea of a sparse representation of array covariance vectors (SRACV). The simulation results demonstrate the superior performance and the low computational complexity of the proposed algorithm.
authors.affiliation: College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China. chentao@hrbeu.edu.cn.
authors.forename: Tao
authors.initials: T
authors.lastname: Chen
authors.affiliation: College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China. wuhuanxin1990@163.com.
authors.forename: Huanxin
authors.initials: H
authors.lastname: Wu
authors.affiliation: College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China. zhaozhongkai@hrbeu.edu.cn.
authors.forename: Zhongkai
authors.initials: Z
authors.lastname: Zhao
keywords: Khatri-Rao (KR) product; array covariance vectors; multiple measurement vectors (MMV); sparse direction of arrival (DOA) estimation; unitary transformation