Multi-channel underwater target recognition using deep learning

In order to solve the problem of low recognition rate under low SNR conditions in underwater target recognition, a deep learning based underwater target recognition framework for multi-channel hydrophone arrays is proposed. Firstly, sub-channel feature splicing is used for utilizing multi-channel information. In feature extraction, a feature extractor that weighted different frequency range of the signal is used and then regularizes the extracted features. Finally, a deep neural network was used for target recognition. The effectiveness of the proposed method was verified in the simulation data. The results showed that the recognition accuracy rate of the deep neural network using cascaded features of multichannel signals reached 96.7% in the five-target recognition task under -15 dB SNR, which was significantly higher than the SVM based method. In the experiments on the lake, the average accuracy rate of the deep neural network reached 96.0%, which further illustrated the effectiveness of the method we proposed. © 2020 Acta Acustica.