Sparsity-Cognizant Source Location Mapping for Underwater Acoustics

Matched-field processing (MFP) is a generalization of classical beamforming that has been traditionally used in underwater source localization problems. However, MFP suffers from low resolution, sensitivity to model mismatch, and is challenged when more than one source is present. This work develops a robust high-resolution underwater source localization algorithm that capitalizes on the sparsity inherent in the underwater source localization problem. Similar to MFP, the sparsity-cognizant approach developed here capitalizes on a model for the acoustic propagation environment and casts the localization problem as a regularized least-squares (LS) one. The resulting regularizer encourages sparsity on the grid-based source location map. An efficient solver whose computational complexity scales linearly with the grid size is developed and its performance illustrated via numerical tests.