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 as sum-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.

Application of Bee Evolutionary Genetic Algorithm to Maximum Likelihood Direction‐of‐Arrival Estimation

“…In past decades, to reduce the computational complexity, many alternative multidimensional searching methods have been proposed [9][10][11], such as the Newton-Gauss method, alternating projection method, space-alternating generalized expectation-maximization method, and method of direction estimation (MODE). Unfortunately, the application of these methods remains restricted due to their drawbacks.…”