Robust DOA estimation for burst impulsive noise

Abstract This paper describes a robust sparse Bayesian learning approach for DOA estimation in the presence of impulsive noise. In practical situations, impulses in the noise often appear in bursts, resulting in structured rather than independent sparsity, which can be exploited to enhance DOA estimation performance. However, there is no method available for capturing the realistic burst structure of impulsive noise in the literature. In this paper, we model the structured impulsive noise with a novel two-dimensional burst prior and develop a robust variational Bayesian inference (VBI) framework for DOA estimation under burst impulsive noise, and an improved grid refinement method is further proposed to adjust grid points of the dictionary matrix, which can combat the off-grid gap more efficiently. The superior impulse-resistant DOA estimation performance and computational efficiency of our proposed method are verified by simulation results.