Robust Variational Bayesian Inference for Direction-of-Arrival Estimation With Sparse Array

Conventional direction-of-arrival (DOA) estimation algorithms are sensitive to array imperfections and outliers, making it challenging to realize accurate estimates in real applications. Facing the challenge, we propose a robust variational Bayesian inference based DOA estimation algorithm using the linear sparse array in this paper, where accurate DOA estimation with increased number of degrees of freedom (DOFs) is realized. With the mixture of von Mises model, a prior-grid scheme is further proposed to alleviate the computational burden introduced by the Bayesian variational inference framework. Since there is no restriction on the prior knowledge of the number of sources, the proposed algorithm is friendly to actual scenarios. Simulation results demonstrate the effectiveness of the proposed algorithm.