Underwater acoustic target recognition is a challenging task owing to the intricate underwater environments and limited data availability. Insufficient data can hinder the ability of recognition systems to support complex modeling, thus impeding their advancement. To improve the generalization capacity of recognition models, techniques such as data augmentation have been employed to simulate underwater signals and diversify data distribution. However, the complexity of underwater environments can cause the simulated signals to deviate from real scenarios, resulting in biased models that are misguided by non-true data. In this study, we propose two strategies to enhance the generalization ability of models in the case of limited data while avoiding the risk of performance degradation. First, as an alternative to traditional data augmentation, we utilize smoothness-inducing regularization, which only incorporates simulated signals in the regularization term. Additionally, we propose a specialized spectrogram-based data augmentation strategy, namely local masking and replicating (LMR), to capture inter-class relationships. Our experiments and visualization analysis demonstrate the superiority of our proposed strategies.

翻译：水下目标识别因水下环境复杂且数据稀缺而极具挑战性。数据不足会限制识别系统支持复杂建模的能力，从而阻碍其发展。为提升识别模型的泛化能力，研究者采用数据增强等技术模拟水下信号并扩展数据分布。然而，水下环境的复杂性可能导致模拟信号偏离真实场景，使模型被非真实数据误导而产生偏差。本研究提出两种策略，在有限数据条件下增强模型泛化能力的同时避免性能退化风险。首先，作为传统数据增强的替代方案，我们采用平滑诱导正则化，仅在正则化项中融入模拟信号。其次，我们提出一种专门的谱图数据增强策略——局部掩码与复制（LMR），以捕捉类间关联。实验与可视化分析验证了我们所提策略的优越性。