Novel underwater acoustic signal denoising: Combined optimization secondary decomposition coupled with original component processing algorithms

Abstract

Measurement and analysis of underwater acoustic signal (UAS), extensively applied in oceanic target identification and environmental monitoring, often confronts substantial noise in UAS, thereby posing significant challenge for subsequent signal processing task. For removing the noise in UAS, novel UAS denoising method based on combined optimization secondary decomposition is proposed. Firstly, improved successive variational mode decomposition with original variable step size decomposition model and secondary complete ensemble empirical mode decomposition with adaptive noise are proposed to effectively decomposing UAS. Secondly, agglomerative hierarchical clustering assisted by refined composite multi-scale dispersion entropy and original index operator is proposed to precisely classify each component into three groups-dominant information, mixed information and dominant noise by chaotic and complexity inherent. Ultimately, noise in dominant information and mixed information is eliminated by time domain screening and index operator, cross-modality maximum between-classes variance denoising algorithm is proposed to denoise effectively dominant noise component, and the final denoised signal is obtained through each group reconstruction. The proposed denoising method, when applied to four simulated signals, achieves SNR improvement ranging from 10 to 40 dB. Furthermore, its implementation on four measured signals results in smoother and more regular phase diagrams, and significant enhancement in signal quality.