Sparse Bayesian Learning Approach for Outlier-Resistant Direction-of-Arrival Estimation

Abstract

Conventional direction-of-arrival (DOA) estimation methods are sensitive to outlier measurements. Therefore, their performance may degrade substantially in the presence of impulsive noise. In this paper, we address the problem of DOA estimation in additive outliers from the perspective of sparse Bayesian learning (SBL). A Bayes-optimal algorithm is devised for robust DOA estimation, which can achieve excellent performance in terms of resolution and accuracy. To reduce the computational complexity of the SBL scheme, a fast alternating algorithm is also developed. New grid-refining procedures are further introduced into these two proposed algorithms to efficiently fix the off-grid gap. As our solutions do not require the prior knowledge of the number of sources and can resolve highly correlated or coherent sources, it is expected that they have higher applicability. Simulation results verify the outlier-robust performance of the SBL approach.