A hierarchical variational Bayesian approximation approach in acoustic imaging

Ning Chu 1, 2 Ali Mohammad-Djafari 2 Nicolas Gac 2 José Picheral 1
1 E3S - Supélec Sciences des Systèmes [Gif-sur-Yvette]
2 L2S - Laboratoire des signaux et systèmes
Abstract : 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.
Keywords : Student-t prior Variational Bayesian Approximation Non-Gaussian noise Acoustic imaging
Type de document :
Communication dans un congrès
AIP Conference Proceedings. 34th International Workshop on Bayesian Inference and Maximun Entropy Methods in Science and Engineering (MaxEnt'14), Sep 2014, Amboise, France. Proceedings of the 34th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, 1641, pp.572 - 579, 2015, 〈10.1063/1.4906024〉
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Ning Chu, Ali Mohammad-Djafari, Nicolas Gac, José Picheral. A hierarchical variational Bayesian approximation approach in acoustic imaging. AIP Conference Proceedings. 34th International Workshop on Bayesian Inference and Maximun Entropy Methods in Science and Engineering (MaxEnt'14), Sep 2014, Amboise, France. Proceedings of the 34th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, 1641, pp.572 - 579, 2015, 〈10.1063/1.4906024〉. 〈hal-01103784〉

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