Integrated neural networks based on feature fusion for underwater target recognition

Abstract Currently, traditional feature extraction algorithms have poor data expression and noise robustness. Moreover, traditional recognition methods are gradually falling behind the demand for increasing data, and struggle to extract deep features in targets. By considering the preceding issues, an integrated neural network has been created in this paper for underwater acoustic target recognition via feature fusion learning. Firstly, the short time Fourier transform (STFT) amplitude spectrum, STFT phase spectrum, and bispectrum feature of underwater acoustic signals are extracted and form the input for the network. They not only contain rich information about the target, but also have strong noise robustness. Secondly, an integrated neural network has been designed, which is trained with different features and contains three neural networks. Finally, in the softmax layer of the network, the shuffled frog leaping algorithm (SFLA) is utilized to train the weight coefficients of different networks. Experimental results of the measured data show that the integrated neural network method based on feature fusion has a higher recognition accuracy and stronger noise robustness.