Super-Resolution Sound Source Localization by Iterative Bayesian Focusing Algorithm with Off-Grid Model

The sparse Bayesian method is a sound source localization method with high spatial resolution, which is of great significance for noise control in engineering. Due to the discretization of the region of interest and the assumption that the source is on grid, the sparse Bayesian method suffers from the base mismatch problem. In this paper, the iterative Bayesian focusing (IBF) algorithm incorporating the off-grid model (IBF-OG) is proposed for solving the basis mismatch problem. The first-order Taylor expansion of the off-grid model is first constructed, and then the expectation-maximization algorithm is used to derive the maximum a posteriori estimate of the IBF algorithm under the off-grid model. The proposed IBF-OG algorithm can effectively alleviate the base mismatch problem and improve spatial resolution. The performance of the proposed IBF-OG algorithm is verified by numerical simulations.