Underwater Acoustic Target Recognition Method Based on Feature Fusion and Residual CNN

This article presents an underwater acoustic target recognition method using feature fusion and residual convolutional neural network (CNN). Mel-frequency cepstrum coefficient (MFCC), Gammatone frequency cepstral coefficient (GFCC), low-frequency analyzer and recorder (LOFAR) spectrum, and constant Q transform (CQT) are extracted and fused first. On this basis, their Delta features are calculated and fused second. The feature dimension is reduced by neighborhood component analysis (NCA). With the fused features after the dimensionality reduction as input features, the residual CNN based on the ResNet18 model is used as classifier to recognize the underwater acoustic target. The other machine-learning models, such as support vector machine (SVM), VGG19, and common CNN, are also compared for inputting different features separately. Experimental results show that, MGCL-Delta-NCA-ResNet18 has the best recognition results among these models, with the recognition accuracy of 97.29%, because this model allows full play to the rich information advantages of feature fusion, advantages of feature dimensionality reduction by the NCA and the ability of ResNet18 to extract abundant characteristics. It can also realize the recognition effectively at low signal-to-noise ratio (SNR). Especially at 0 dB, the recognition accuracy can still reach 86.25%. The proposed method can also recognize multitarget signal effectively in the multiple target scenario. Although this model is used in ship and natural voice recognition, it can also be applied to the recognition of other target sounds, such as marine mammals.