 |
ECOLE SUPÉRIEURE D'ÉLECTRICITÉ Une grande école d'ingénieurs au cœur des sciences de l'information,de l'énergie et des systèmes |
 |
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 : Friday, April 30, 2021 - 10:04:10 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 : Friday, April 30, 2021 - 10:04:10 AM
Long-term archiving on: : Thursday, April 16, 2015 - 10:40:51 AM
File
Maxent2014-CHU.pdf
Files produced by the author(s)