Underwater Acoustic Target Classification Using Convolutional Neural Network Combined with Continuous Wavelet Transform

Abstract

Underwater acoustic target (UAT) classification is a critical task in submarine warfare because acoustic waves are the reliable information source that allows sonar operators and commanders to understand the surrounding situation in the operational area. To solve the problem of UAT classification, this paper proposes a convolutional neural network combined with continuous wavelet transform for improving the accuracy of UAT classification. The classification focuses on types of noise emitted from the propellers of different ships. Signal processing methods such as Short Time Fourier Transform, Continuous Wavelets Transform, and CNN are executed to extract signal features that provide information for classifier. Simulation results are achieved with an accuracy of 99.64\% when using the Continuous Wavelets Transform method combined with the convolutional neural network, which is higher than other traditional methods.