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. Bayesian Inference and Maximum Entropy Methods in Science and Engineering - MaxEnt 2014, 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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Dernière modification le : mardi 10 avril 2018 - 11:46:04
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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. Bayesian Inference and Maximum Entropy Methods in Science and Engineering - MaxEnt 2014, 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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