A Study and Comparison of Different Sparse Bayesian Learning Algorithms in DOA Estimation

Abstract

The direction of arrival (DOA) is a typical sparse parameter estimation problem. Its solution methods include greedy algorithm, norm minimization method and Bayesian estimation, in which the Bayesian methods are superior in estimation accuracy, but huge amount of computation has become the bottle-neck. This paper analyzes and compares the computation complexity of sparse Bayesian learning (SBL), multi-task sparse Bayesian learning (MSBL) and inverse-free sparse Bayesian learning (IFSBL) in DOA estimation. Simulations are also provided and prove that IFSBL is much better than SBL and MSBL in operational efficiency.