title: A Sum-of-Squares and Semidefinite Programming Approach for Maximum Likelihood DOA Estimation.
abstract: Direction of arrival (DOA) estimation using a uniform linear array (ULA) is a classical problem in array signal processing. In this paper, we focus on DOA estimation based on the maximum likelihood (ML) criterion, transform the estimation problem into a novel formulation, named assum-of-squares(SOS), and then solve it using semidefinite programming (SDP). We first derive the SOS and SDP method for DOA estimation in the scenario of a single source and then extend it under the framework of alternating projection for multiple DOA estimation. The simulations demonstrate that the SOS- and SDP-based algorithms can provide stable and accurate DOA estimation when the number of snapshots is small and the signal-to-noise ratio (SNR) is low. Moveover, it has a higher spatial resolution compared to existing methods based on the ML criterion.
authors.affiliation: Jiangsu Key Laboratory of Wireless Communication, Nanjing University of Posts and Telecommunications, Nanjing 210003, China. caishu@njupt.edu.cn.
authors.forename: Shu
authors.initials: S
authors.lastname: Cai
authors.affiliation: Key Laboratory of Ministry of Education for Broad Band Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China. quan.zhou@njupt.edu.cn.|School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China. quan.zhou@njupt.edu.cn.
authors.forename: Quan
authors.initials: Q
authors.lastname: Zhou
authors.affiliation: Jiangsu Key Laboratory of Wireless Communication, Nanjing University of Posts and Telecommunications, Nanjing 210003, China. zhuhb@njupt.edu.cn.
authors.forename: Hongbo
authors.initials: H
authors.lastname: Zhu
keywords: DOA estimation; alternating projection; maximum likelihood; semidefinite programming; sum-of-squares; uniform linear array