Broadband acoustic-source localization using passive sonar via multitask learning.

Passive sonar is an attractive technology for underwater acoustic-source localization that enables the localization system to conceal its presence and does not perturb the maritime environment. Notwithstanding its appeal, passive-sonar-based localization is challenging due to the complexities of underwater acoustic propagation. Different from alternatives based on matched-field processing whose localization performance severely deteriorate when localizing multiple sources and when faced with model mismatch, this work casts the broadband underwater acoustic-source localization problem as a multitask learning (MTL) problem, thereby enabling robust and high-resolution localization. Here, each task refers to a sparse signal approximation problem over a single frequency. MTL provides an elegant framework for exchanging information across the individual regression problems and constructing an aggregate (across frequencies) source localization map. The localization problem is formulated as a stochastic least-squares optimization problem with a group sparsity constraint enforcing a common support across frequency maps. Efficient algorithms based on block coordinate descent are developed for solving the localization problem. Predictor screening rules are also developed to further reduce the computational complexity of the proposed method. Numerical tests on real data illustrate and compare the localization performance of the proposed algorithm to that of competitive alternatives.