A Robust Feature Extraction Method for Underwater Acoustic Target Recognition Based on Multi-Task Learning

Target classification and recognition have always been complex problems in underwater acoustic signal processing because of noise interference and feature instability. In this paper, a robust feature extraction method based on multi-task learning is proposed, which provides an effective solution. Firstly, an MLP-based network model suitable for underwater acoustic signal processing is proposed to optimize feature extraction. Then, multi-task learning is deployed on the model in hard parameter-sharing so that the model can extract anti-noise interference features and embed prior feature extraction knowledge. In the model training stage, the simultaneous training method enables the model to improve the robustness and representation of classification features with the knowledge of different tasks. Furthermore, the optimized classification features are sent to the classification network to complete target recognition. The proposed method is evaluated by the dataset collected in the real environment. The results show that the proposed method effectively improves recognition accuracy and maintains high performance under different noise levels, which is better than popular methods.

Automated summary

In this paper, a robust feature extraction method based on multi-task learning is proposed to solve the complex problems of target classification and recognition in underwater acoustic signal processing. The method optimizes feature extraction by deploying a multi-task learning model and improves the robustness and representation of classification features. Experimental results show that the proposed method effectively improves recognition accuracy and maintains high performance under different noise levels.