In the field of Marine remote sensing,the classification and recognition of underwater acoustic targets has al-ways been a difficult and extremely important task for sonar systems.In order to improve the accuracy of underwater acous-tic targets under different signal-to-noise ratios,this paper proposes a method of underwater acoustic target recognition using multi-domain fusion features to input dual channel models respectively.First,the features of acoustic signal in frequency do-main and time frequency are extracted by Mel-Frequency Cepstral Coefficients(MFCC)and short-time Fourier transform(STFT).Secondly,dense convolutional neural network(DenseCNN)and long short term memory network(LSTM)are con-structed.The DenseCNN channel architecture uses skip connections to reuse all previous feature maps to optimize classifica-tion rates under various damaged conditions,and SE attention mechanism enables dynamic adjustment of feature weights.LSTM channels capture temporal dependencies and complement the model's ability to handle long dependencies.Experi-mental results show that the classification accuracy of the proposed method is better than other advanced neural network models at-20～10 db SNR.
维普
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