A classification method of underwater target radiated noise signals based on enhanced images and convolutional neural networks

As the economy and society continue to develop, the range of underwater vehicles is expanding and technology is constantly being upgraded. Consequently, it is becoming increasingly difficult to classify and identify them, and the traditional classification method based on signal characteristics can no longer meet the urgent need for the accurate identification of underwater targets. This paper therefore proposes multiple convolutional neural network recognition methods based on enhanced Gramian Angular Field (GAF) images. Firstly, the radiated noise signals of underwater targets are converted into enhanced images using the GAF method. Then, the converted image dataset is used as input for the convolutional neural network. The input dataset is modified accordingly for each convolutional neural network. Finally, the significant advantages of convolutional neural networks in image processing are leveraged to achieve precise classification of underwater target radiated noise. In order to propose a convolutional neural network method that matches the enhanced image method, this paper compares the calculation results of multiple convolutional neural network models. The experimental results show that the VGG-16 model achieves greater classification accuracy and efficiency, reaching 80.67%.