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Conference papers
A hierarchical variational Bayesian approximation approach in acoustic imaging
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.
Cited literature [12 references]
https://hal-supelec.archives-ouvertes.fr/hal-01103784
Contributor : Alexandra Siebert <>
Submitted on : Thursday, January 15, 2015 - 1:46:00 PM
Last modification on : Saturday, October 3, 2020 - 3:54:49 AM
Long-term archiving on: : Thursday, April 16, 2015 - 10:40:51 AM
Contributor : Alexandra Siebert <>
Submitted on : Thursday, January 15, 2015 - 1:46:00 PM
Last modification on : Saturday, October 3, 2020 - 3:54:49 AM
Long-term archiving on: : Thursday, April 16, 2015 - 10:40:51 AM
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Maxent2014-CHU.pdf
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