A Multitask Learning Framework for Broadband Source-Location Mapping Using Passive Sonar

Abstract

Underwater source localization via passive sonar is a challenging task due to the dynamic and complex nature of the acoustic environment. Different from approaches based on matched-field processing, this work explores broadband underwater source localization within a multitask learning (MTL) framework. Here, each task refers to a robust signal approximation problem over a single frequency. MTL provides a natural framework for exchanging information across the narrowband signal-approximation problems and constructing an aggregate (across frequencies) source-localization map. Efficient algorithms based on block coordinate descent (BCD) are developed for solving the source-localization problem. Complex-valued predictor screening rules for reducing the computational complexity of the algorithm are also developed. These rules discard map locations from the set of possible source locations prior to using BCD. They reduce the computational complexity of the localization algorithm without compromising the localization results. Tests of these approaches on synthetic and real data for the SWellEX-3 environment compare the performance of the proposed algorithm to that of alternative methods.Index Terms-Block coordinate descent, group sparsity, multitask learning, underwater source localization.

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