WDNet: An Underwater Acoustic Signal Denoising Algorithm Based on Wavelet Denoising and Deep Learning

Abstract

Modulation recognition in underwater acoustic (UWA) signals is challenging due to the intricate marine environment and substantial underwater noise. Wavelet-based denoising lacks adaptivity and can be affected by the wavelet function, the number of decomposition layers, and the threshold function. Although the denoising method based on deep learning has achieved a good denoising effect, it fails to integrate with the physical model and lacks certain theoretical support. To address these problems, this paper proposes a deep fusion network for signal denoising, named WDNet, based on wavelet denoising theory and deep learning techniques. We initialize the tap coefficients of the wavelet decomposition and reconstruction filters as learnable parameter matrices and use the soft threshold function as the activation function so as to realize the decomposition, thresholding, and reconstruction of the signal. The filter and threshold are adjusted adaptively by backpropagation to achieve optimal signal denoising. Simulation results demonstrate that our model achieves a higher signal-to-noise ratio (SNR) gain and lower root mean square error (RMSE) compared to other methods. After denoising, the recognition rate of UWA modulation signals significantly improves.