A Bayesian Hybrid Attention Module for Underwater Acoustic Target Recognition

The attention mechanism improves underwater acoustic target recognition (UATR) by suppressing irrelevant features. However, due to the uncertainty and scarcity of underwater acoustic target (UWAT) signals, complicated deterministic attention modules increase the risk of model overfitting, resulting in limited improvement or even degradation in the performance of UATR. This letter proposes a Bayesian Hybrid Attention Module (BHAM) that enhances UATR based on time–frequency (T–F) features. BHAM models attention weights as random variables following Beta and Dirichlet distributions to capture uncertainty of UWAT signals and mitigate overfitting, while strengthening T–F feature representation via Bayesian channel attention and Bayesian T–F attention. By learning attention distributions in a Bayesian manner, BHAM effectively models complex dependencies in UWAT signals. Experiments on the DeepShip dataset demonstrate that BHAM alleviates overfitting and generalizes well across different network backbones.