A sparse dictionary learning-based denoising method for underwater acoustic sensors

Abstract

The complex and changeable marine environment will cause the signal detected by underwater acoustic sensors to be extremely weak, and traditional signal processing methods are usually complicated. To improve the detection efficiency and application scope of underwater acoustic sensors, we use sparse decomposition theory and dictionary learning algorithms to directly study the denoising of single hydrophone signals under different sea conditions. Then we use pulse compression technology to solve the problem of traditional methods that long signal causes construction difficulties of the dictionary. The method in this paper can randomly construct a discrete cosine transform (DCT) dictionary without a priori information. The noisy signal is trained and updated via orthogonal matching pursuit (OMP) and method of optimal directions (MOD), and the reconstruction of the signal is completed according to the updated dictionary and sparse coefficients. The results demonstrate that the method in this paper can be applied to wideband long pulse signals, and the signal-to-noise ratio (SNR) gain can reach about 20 dB under different sea conditions. By comparing with the OMP algorithm, the proposed method can reduce the number of atoms so that improving the system performance, reduce the algorithm complexity, and improve the operation efficiency.