Data augmentation method for underwater acoustic target recognition based on underwater acoustic channel modeling and transfer learning

Data augmentation methods as a critical technique in deep learning have not been well studied in the underwater acoustic target recognition, which leads difficult for recognition models to cope with data scarcity and noise interference. This study proposes a data augmentation method based on underwater acoustic channel modeling and Transfer learning to address these challenges. A underwater acoustic channel modeling approach is proposed to generate the augmented signal. A feature-based transfer learning method is presented to narrow the distribution differences between augmented and observed data, and the noise is randomly added to enhance model robustness during training. Dataset acquired in a real-world scenario is used to verify the proposed methods. The proposed methods' effectiveness is proved by utilizing data augmentation in the model training process, which effectively improves the accuracy and noise robustness of the recognition model, especially when observed data is scarce.