Broadband underwater source localization via multitask learning

Passive sonar is an attractive technology for stealthy underwater source localization. Notwithstanding its appeal, passive-sonar-based localization is challenging due to the complexities of underwater acoustic propagation. This work casts broadband underwater source localization as a multitask learning (MTL) problem, where each task refers to a robust sparse signal approximation problem over a single frequency. MTL provides a framework for exchanging information across the individual regression problems and constructing an aggregate (across frequencies) source localization map. Efficient algorithms based on block coordinate descent are developed for solving the localization problem. Numerical tests on the SWellEX-3 dataset illustrate and compare the localization performance of the proposed algorithm to the one of competitive alternatives.