Low-frequency Underwater Acoustic Signal Denoising Method in the Shallow Sea with a Low Signal-to-noise Ratio

Abstract

Due to the serious and complicated noise in the shallow sea environment, the received signal obtained by the hydrophone is disturbed by the noise to a large extent. It has a low signal-to-noise ratio (SNR), which leads to problems such as difficulty in processing the underwater acoustic signal. To solve this problem, to more effectively remove the ocean noise in the useful signal, a denoising method based on sparse decomposition and dictionary learning is adopted. First, a complete Discrete Cosine Transform (DCT) dictionary is randomly constructed. Then the orthogonal matching pursuit (OMP) is used to represent the noisy underwater acoustic signal sparsely, the method of optimal directions (MOD) and K-singular value decomposition algorithm (K-SVD) are used to update the complete DCT dictionary respectively. According to the updated new dictionary and sparse coefficients, the underwater acoustic signal is reconstructed, and the ocean noise is removed. By denoising the different form of simulated signals with different SNRs, the results show that two methods can remove various noises mixed in the underwater acoustic signal effectively and retain the signal details while denoising. The SNR gain can reach about 20dB.