A noise-reduction method for UUV communication using a stack-type convolutional autoencoder model of image features

Abstract

Underwater acoustic (UWA) communication is the primary interaction between UUV and underwater array. However, noise can seriously affect the quality of communication. The conventional filtering algorithms and multiple kernel learning (MKL) models are unsuitable for abrupt-varying noise environments. To address the problem, this study proposes a deep learning (DL) noise-reduction model based on image features. Firstly, in the preprocessing stage, we transform the abrupt-varying noise into a two-dimensional representation of the image characteristics and construct a time–frequency preprocessing structure based on higher-order DEMON spectral representation. Secondly, a feature extraction method based on EMD feature enhancement is built to improve computing efficiency. Finally, we propose a noise-reduction model based on a stack-type convolutional autoencoder aiming to suppress underwater noise and enhance the system’s communication quality. The simulations and experiments on a lake show that the proposed method is more robust over the changing signal-to-noise ratio (SNR) and has a lower bit error rate (BER) than conventional methods.