Dispersive Grid-free Orthogonal Matching Pursuit for Modal Estimation in Ocean Acoustics

Considering low-frequency acoustic sources, shallow-water environments act as modal dispersive waveguides. In this context, the signal can be described as a sum of a few modal components, each of them propagating with its own wavenumber. When dealing with broadband sources, wavenumber-frequency (f-k) diagrams constitute popular representations naturally enabling modal separation. Based on a Fourier transform, they require however a large number of sensors to resolve wavenumbers with a high-resolution. This limitation can be overcame by adding some physical priors to the processing method. In the continuation of previous works, we propose here a new grid-free algorithm allowing a super-resolution of the (f-k) diagram by benifiting from the sparse nature of the wavenumber spectrum and embedding the broadband behavior of the wavenumbers within the algorithm. The method is validated on simulated data.

[1]  Eero P. Simoncelli,et al.  Recovery of Sparse Translation-Invariant Signals With Continuous Basis Pursuit , 2011, IEEE Transactions on Signal Processing.

[2]  Jonathan W. Pillow,et al.  Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit , 2014, NIPS.

[3]  Julien Bonnel,et al.  Compressed sensing for wideband wavenumber tracking in dispersive shallow water. , 2015, The Journal of the Acoustical Society of America.

[4]  Barbara Nicolas,et al.  Geoacoustical parameters estimation with impulsive and boat-noise sources , 2003 .

[5]  Julien Bonnel,et al.  Reconstruction of Dispersion Curves in the Frequency-Wavenumber Domain Using Compressed Sensing on a Random Array , 2017, IEEE Journal of Oceanic Engineering.

[6]  Joel B. Harley,et al.  Broadband localization in a dispersive medium through sparse wavenumber analysis , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[7]  Quentin Denoyelle Theoretical and Numerical Analysis of Super-Resolution Without Grid , 2018 .

[8]  Laurent Daudet,et al.  Boltzmann Machine and Mean-Field Approximation for Structured Sparse Decompositions , 2012, IEEE Transactions on Signal Processing.

[9]  S. M. Doherty,et al.  Seismic Data Analysis: Processing, Inversion, and Interpretation of Seismic Data , 2000 .

[10]  Francois-Xavier Socheleau,et al.  Performance Analysis of Single-Receiver Matched-Mode Localization , 2019, IEEE Journal of Oceanic Engineering.

[11]  Peter Gerstoft,et al.  Grid-free compressive mode extraction. , 2019, The Journal of the Acoustical Society of America.

[12]  Barbara Nicolas,et al.  Physics-Based Time-Frequency Representations for Underwater Acoustics: Power Class Utilization with Waveguide-Invariant Approximation , 2013, IEEE Signal Processing Magazine.

[13]  Julien Bonnel,et al.  Wavenumber tracking in a low resolution frequency-wavenumber representation using particle filtering , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[15]  José M. F. Moura,et al.  Sparse recovery of the multimodal and dispersive characteristics of Lamb waves. , 2013, The Journal of the Acoustical Society of America.

[16]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[17]  P. Jaccard,et al.  Etude comparative de la distribution florale dans une portion des Alpes et des Jura , 1901 .

[18]  Gary R. Wilson,et al.  Matched mode localization , 1988 .