A Hierarchical Variational Bayesian Approximation Approach in Acoustic Imaging

Acoustic imaging is a powerful technique for acoustic source localization and power reconstruction from limited noisy measurements at microphone sensors. But it inevitably confronts a very ill-posed inverse problem which causes unexpected solution uncertainty. Recently, the Bayesian inference methods using sparse priors have been effectively investigated. In this paper, we propose to use a hierarchical variational Bayesian approximation for robust acoustic imaging. And we explore the Student-t priors with heavy tails to enforce source sparsity, and to model nonGaussian noise respectively. Compared to conventional methods, the proposed approach can achieve the higher spatial resolution and wider dynamic range of source powers for real data from automobile wind tunnel.