hal-01561299
https://hal.science/hal-01561299
doi:10.1109/JOE.2016.2644780
[UNIV-BREST] Université de Bretagne occidentale - Brest (UBO)
[INSTITUT-TELECOM] Institut Mines Télécom
[ENSTA-BRETAGNE] ENSTA Bretagne
[CNRS] CNRS - Centre national de la recherche scientifique
[UNIV-UBS] Université de Bretagne Sud
[ENSTA-BRETAGNE-STIC] Département STIC
[ENIB] Ecole Nationale d'Ingénieurs de Brest
[LAB-STICC] Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
[INSTITUTS-TELECOM] composantes instituts telecom
Reconstruction of Dispersion Curves in the Frequency-Wavenumber Domain Using Compressed Sensing on a Random Array
Dremeau, Angélique
Courtois, Florent Le
Bonnel, Julien
[SPI.ACOU] Engineering Sciences [physics]/Acoustics [physics.class-ph]
ART
Compressed sensing
Frequency-domain analysis
Mathematical model
Context
Estimation
Underwater acoustics
Dispersion
In underwater acoustics, shallow-water environments act as modal dispersive waveguides when considering low-frequency sources, and propagation can be described by modal theory. In this context, propagated signals are composed of few modal components, each of them propagating according to its own wavenumber. Frequency-wavenumber (f−k) representations are classical methods allowing modal separation. However, they require large horizontal line sensor arrays aligned with the source. In this paper, to reduce the number of sensors, a sparse model is proposed and combined with prior knowledge on the wavenumber physics. The method resorts to a state-of-the-art Bayesian algorithm exploiting a Bernoulli–Gaussian model. The latter, well suited to the sparse representations, makes possible a natural integration of prior information through a wise choice of the Bernoulli parameters. The performance of the method is quantified on simulated data and finally assessed through a successful application on real data.
2017-03-13
en
IEEE Journal of Oceanic Engineering
Institute of Electrical and Electronics Engineers