The scarcity of underwater acoustic data in deep and remote sea environments poses a significant challenge to data-driven target recognition models, severely restricting their performance. To address this challenge, this study presents a ray-theory-based data augmentation method for generating synthetic ship-radiated noise datasets in oceanic environments at a depth of 3500 m—DS3500, encompassing both direct and shadow zones. Additionally, a novel MEG (multi-task, multi-expert, multi-gate) framework is developed to achieve simultaneous target localization and recognition by integrating relative positional information between the target and sonar, which dynamically partitions parameter spaces through multi-expert mechanisms and adaptively combines task-specific representations using multi-gate attention to simultaneously predict target localization and recognition. Experimental results on the DS3500 dataset demonstrate that the MEG framework achieves 95.93% recognition accuracy, a range localization error of 0.2011 km and a depth localization error of 20.61 m with a maximum detection range of 11 km and depth of 1100 m. This study provides a new technical solution for underwater acoustic target recognition in deep and remote seas, offering innovative approaches for practical applications in marine monitoring and defense.
Peng et al. (Tue,) studied this question.
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