Resumen
The acquisition of target data for underwater acoustic target recognition (UATR) is difficult and costly. Although deep neural networks (DNN) have been used in UATR, and some achievements have been made, the performance is not satisfactory when recognizing underwater targets with different Doppler shifts, signal-to-noise ratios (SNR), and interferences. On the basis of this, this paper proposed a Siamese network with two identical one-dimensional convolutional neural networks (1D-CNN) that recognize the detection of envelope modulation on noise (DEMON) spectra of underwater target-radiated noise. The parameters of underwater samples were diverse, but the states of the collected samples were very homogeneous. Traditional underwater target recognition uses multi-state samples to train the network, which is costly. This article trained the network using samples from a single state. The expectation was to be able to identify samples with different parameters. Datasets of targets with different Doppler shifts, SNRs, and interferences were designed to evaluate the generalization performance of the proposed Siamese network. The experimental results showed that when recognizing samples with Doppler shifts, the classification accuracy of the proposed network reached 95.3%. For SNRs, the classification accuracy reached 85.5%. The outstanding generalization ability of the proposed model shows that it is suitable for practical engineering applications.