ORIGINAL RESEARCH article
Front. Phys.
Sec. Physical Acoustics and Ultrasonics
Volume 13 - 2025 | doi: 10.3389/fphy.2025.1693938
A classification method of underwater target radiated noise signals based on enhanced images and convolutional neural networks
Provisionally accepted
- 1Xi’an Aeronautical Institute, Xi'an, China
- 2HanCheng Development and Reform Commission, HanCheng, China
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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%.
Keywords: underwater targets, Radiated noise, Enhanced images, Convolutional Neural Network, Gramian angular field
Received: 27 Aug 2025; Accepted: 20 Oct 2025.
Copyright: © 2025 Muye, Zhufeng, Yinuo and Zanrong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Lei Zhufeng, leizhufeng@xaau.edu.cn
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