Acoustic imaging is a powerful technique for acoustic source localization and power reconstruction from limited noisy measurements at microphone sensors. But it inevitably con-fronts 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 non-Gaussian 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 automo-bile wind tunnel.