Adaptive Imaging of Sound Source Based on Total Variation Prior and a Subspace Iteration Integrated Variational Bayesian Method

Abstract

Sound source visualization has been widely used in many scenarios such as aerospace, industrial production, and urban management. Sound source localization technology plays an essential role in the realization of sound source visualization. The imaging results and calculation cost are the primary considerations in the problem of sound source localization. This article proposes a subspace iteration integrated variational Bayesian (SVB) method to realize adaptive imaging of different sound sources. First, the proposed variational Bayesian (VB) method is based on total variation (TV) prior to balance the sparsity and smoothness of the imaging results. Second, the subspace optimization method in the probability measure space is integrated into the proposed SVB method to solve the involved ill-posed inverse problem. The proposed SVB method can significantly improve the calculation speed, especially for large-scale inverse problems. Finally, the speed and robustness of the proposed SVB method can be demonstrated according to the extensive results of simulation and experimental validation.