Underwater Acoustic Target Recognition Algorithm Based on Generative Adversarial Networks

In the practical application of underwater acoustic target recognition, one of the main factors restricting the recognition results is the insufficient quantity of labeled samples. For the small sample properties of underwater acoustic target noise, a generative adversarial networks(GAN)-based recognition algorithm is proposed based on deep learning theory. It can be used to learn more effective features with more discriminative information from the game between generated model and adversarial model, and it is compared with deep auto-encoder(DAE) network and deep belief network(DBN) models. The experimental results illustrate that the recognition performance of GAN network model is higher than those of DBN network and DAE network models when the number of samples is limited, and the recognition performances of the three deep learning models are better than the conventional approach of extracting Mel frequency cepstrum coefficient(MFCC) features and then classifying by Softmax. In addition, GAN network model is superior to DBN network and DAE network models in recognition rate when using training samples and test samples with different SNRs. The smulation experimental results indicate that the GAN network model is more robust to noise. © 2021, Editorial Board of Acta Armamentarii. All right reserved.