Shallow-water sparsity-cognizant source-location mapping

Abstract

Using passive sonar for underwater acoustic source localization in a shallow-water environment is challenging due to the complexities of underwater acoustic propagation. Matched-field processing (MFP) exploits both measured and model-predicted acoustic pressures to localize acoustic sources. However, the ambiguity surface obtained through MFP contains artifacts that limit its ability to reveal the location of the acoustic sources. This work introduces a robust scheme for shallow-water source localization that exploits the inherent sparse structure of the localization problem and the use of a model characterizing the acoustic propagation environment. To this end, the underwater acoustic source-localization problem is cast as a sparsity-inducing stochastic optimization problem that is robust to model mismatch. The resulting source-location map (SLM) yields reduced ambiguities and improved resolution, even at low signal-to-noise ratios, when compared to those obtained via classical MFP approaches. An iterative solver based on block-coordinate descent is developed whose computational complexity per iteration is linear with respect to the number of locations considered for the SLM. Numerical tests illustrate the performance of the algorithm.