Sparse Bayesian Learning Approach for Outlier-resistant Direction-of-arrival Estimation

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

64 Scopus Citations
View graph of relations


Related Research Unit(s)


Original languageEnglish
Pages (from-to)744-756
Journal / PublicationIEEE Transactions on Signal Processing
Issue number3
Online published13 Nov 2017
Publication statusPublished - 1 Feb 2018


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.

Research Area(s)

  • Direction-of-arrival (DOA) estimation, Impulsive noise, Robust estimation, Sparse Bayesian learning (SBL), Sparse representation