InfoGAN-Enhanced Underwater Acoustic Target Recognition Method Based on Deep Learning
Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022)(2023)
摘要
Aiming at the small sample problem of underwater acoustic target recognition (UATR) task, this paper proposes a method which uses InfoGAN to generate underwater acoustic target data for UATR. In this method, the underwater acoustic target audio data is transformed into time-frequency image data by short-time Fourier transform, and then input the time-frequency image data into InfoGAN to generate underwater acoustic target data. Finally, the method establishes the IG_DCNN recognition model and the real data samples are mixed with input training. To test and verify the feasibility and effectiveness of the InfoGAN-enhanced UATR method, the original dataset was input into CNN to establish a comparative model. The experimental reveal that InfoGAN can generate diverse underwater acoustic target data samples, and the IG_DCNN model constructed by the InfoGAN-enhanced UATR method proposed in this paper has better network performance and recognition accuracy.
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关键词
deep learning,infogan-enhanced
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