Graph Neural Networks for source localization using Ships-of-Opportunity spectrograms

Abstract

Traditional underwater source localization methods have primarily relied on optimization techniques, matched-field processing, beamforming, and, more recently, deep learning approaches. However, these methods often fail to exploit the spatial correlation of data due to their representation in a regular domain. Nowadays, data collection commonly occurs in complex domains, like sensor networks, where signals and features are best represented as graphs based on feature similarity metrics. In a graph representation setting, each sensor or feature corresponds to a node in the graph, accompanied by a feature vector that may or may not vary with time. As edges define spatial relationships, spatio-temporal information is considered simultaneously during the learning process. This work proposes a novel graph learning module for source localization using Ships-of-Opportunity (SOO) spectrograms, which represent mid-frequency acoustic broadband signals (360-1100Hz) collected during the 2017 Seabed Characterization Experiment (SBCEX17). The proposed approach employs a semi-supervisedlearning algorithm on a graph constructed through a k-nearest neighbor (k-NN) algorithm, incorporating features extracted from the spectrograms using a backbone architecture. The efficacy of the proposed approach is demonstrated through model evaluation on both synthetic and measured data, validating the generalization power of the architecture. [Work supported by ONR under Grant No. N00014-21-1-2760.]