Modal estimation in underwater acoustics by data-driven structured sparse decompositions

In underwater acoustics, shallow water environments act as modal dispersive waveguides when considering lowfrequency sources. In this context, propagating signals can be described as a sum of few modal components, each of them propagating according to its own wavenumber. Estimating these wavenumbers...

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Bibliographic Details
Published in2021 29th European Signal Processing Conference (EUSIPCO) pp. 286 - 290
Main Authors Dorffer, Clement, Paviet-Salomon, Thomas, Le Chenadec, Gilles, Dremeau, Angelique
Format Conference Proceeding
LanguageEnglish
Published EURASIP 23.08.2021
Subjects
Approximation algorithms
Bayes methods
Bayesian algorithm
Estimation
Europe
Restricted Boltzmann Machine
Signal processing algorithms
structured sparse approximation
Training
Underwater acoustics
Underwater modal estimation
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Summary:In underwater acoustics, shallow water environments act as modal dispersive waveguides when considering lowfrequency sources. In this context, propagating signals can be described as a sum of few modal components, each of them propagating according to its own wavenumber. Estimating these wavenumbers is of key interest to understand the propagating environment as well as the emitting source. To solve this problem, we proposed recently a Bayesian approach exploiting a sparsity-inforcing prior. When dealing with broadband sources, this model can be further improved by integrating the particular dependence linking the wavenumbers from one frequency to the other. In this contribution, we propose to resort to a new approach relying on a restricted Boltzmann machine, exploited as a generic structured sparsity-inforcing model. This model, derived from deep Bayesian networks, can indeed be efficiently learned on physically realistic simulated data using well-known and proven algorithms.
ISSN:2076-1465
DOI:10.23919/EUSIPCO54536.2021.9616238