PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Sparse underwater acoustic imaging: a case study
Nikolaos Stefanakis, Jacques Marchal, Valentin Emiya, Nancy Bertin, Rémi Gribonval and Pierre Cervenka
In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Kyoto, Japan(2012).


Underwater acoustic imaging is traditionally performed with beam- forming: beams are formed at emission to insonify limited angular regions; beams are (synthetically) formed at reception to form the image. We propose to exploit a natural sparsity prior to perform 3D underwater imaging using a newly built flexible-configuration sonar device. The computational challenges raised by the high- dimensionality of the problem are highlighted, and we describe a strategy to overcome them. As a proof of concept, the proposed approach is used on real data acquired with the new sonar to obtain an image of an underwater target. We discuss the merits of the obtained image in comparison with standard beamforming, as well as the main challenges lying ahead, and the bottlenecks that will need to be solved before sparse methods can be fully exploited in the context of underwater compressed 3D sonar imaging.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:9022
Deposited By:Valentin Emiya
Deposited On:21 February 2012