Underwater acoustic imaging: sparse models and implementation issues
Abstract
We present recent work on sparse models for underwater acoustic imaging and on implementation of imaging methods with real data. By considering physical issues like non-isotropic scattering and non-optimal calibration, we have designed several structured sparse models. Greedy algorithms are used to estimate the sparse representations. Our work includes the design of real experiments in a tank. Several series of data have been collected and processed. For such a realistic scenario, data and representations live in high-dimensional spaces. We introduce algorithmic adaptations to deal with the resulting computational issues. The imaging results obtained by our methods are finally compared to standard beamforming imaging.
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