Deep attention-based multi-task learning for underwater acoustic target recognition

Abstract

Underwater acoustic target recognition is a key step in underwater acoustic signal processing, which is to judge the target attributes by extracting the features of the target radiated noise. However, the traditional feature extraction method is easily affected by the complex marine environment, resulting in a great decline in the correct recognition rate. In previous studies, the deep learning method was introduced to underwater acoustic target recognition and achieved good results. In this study, deep attention-based multi-task learning for underwater acoustic target recognition is proposed. This strategy trains encoder, decoder, and classifier in a multi-task framework to extract features that are more essential and suitable for classification. In addition, the attention mechanism is introduced in the process of feature extraction by the encoder, and the feature weight is given in the process of extraction, so as to improve the representation ability of the network. Experiments on measured underwater acoustic data are performed and the results show that this method can achieve a high accuracy rate in target recognition.