A momentum-based adversarial training approach for generalization in underwater acoustic target recognition: An individual-vessel perspective

Underwater passive acoustic recognition, which focuses on classifying targets based on ship-radiated noise, is a key challenge in underwater acoustics. Deep learning-based methods have gained popularity in recent years because of their strong performance. However, these methods often fail to generalize well in real-world scenarios. This work reveals one underlying challenge: the characteristics of ship-radiated noise are influenced by factors such as vessel structures and propulsion systems. Although vessels of the same type may exhibit different patterns in these aspects, vessels of different categories share similarities. As a result, data-driven models often tend to overemphasize individual-specific features, leading to "overfitting" and poor generalization. The momentum-based adversarial training (MBAT) framework is proposed to mitigate this challenge. MBAT leverages a momentum adversarial strategy to use category information and individual vessel relationships, helping extract class-discriminative features. A homoscedastic uncertainty algorithm is employed to balance the optimization objectives of category-related and vessel-specific features. These strategies allow the model to capture category-discriminative patterns more effectively and generalize to unseen targets. Experiments on DeepShip and ShipsEar demonstrate that MBAT significantly improves generalization capability on unseen individual vessels, outperforming existing state-of-the-art methods. Visualizations further confirm the efficacy and necessity of the proposed approach.