A Novel Joint Denoising Method for Hydrophone Signal Based on Improved SGMD and WT

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 4; p. 1340
• Main Authors: Xing, Tianyu, Wang, Xiaohao, Ni, Kai, Zhou, Qian
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19.02.2024; MDPI
• Subjects: Acoustics; Algorithms; Analysis; Buildings; denoising; Fault diagnosis; Fourier transforms; Geometry; improved symplectic geometry modal decomposition (ISGMD); Kalman filters; Medical equipment; Methods; Remodeling, restoration, etc; Signal processing; spectral clustering (SC); spectral entropy (SE); the energy contribution (EC); Time series; wavelet threshold (WT); Wavelet transforms; denoising; spectral entropy (SE); wavelet threshold (WT); improved symplectic geometry modal decomposition (ISGMD); the energy contribution (EC); spectral clustering (SC)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24041340

Underwater acoustic technology as an important means of exploring the oceans is receiving more attention. Denoising for underwater acoustic information in complex marine environments has become a hot research topic. In order to realize the hydrophone signal denoising, this paper proposes a joint denoising method based on improved symplectic geometry modal decomposition (ISGMD) and wavelet threshold (WT). Firstly, the energy contribution (EC) is introduced into the SGMD as an iterative termination condition, which efficiently improves the denoising capability of SGMD and generates a reasonable number of symplectic geometry components (SGCs). Then spectral clustering (SC) is used to accurately aggregate SGCs into information clusters mixed-clusters, and noise clusters. Spectrum entropy (SE) is used to distinguish clusters quickly. Finally, the mixed clusters achieve the signal denoising by wavelet threshold. The useful information is reconstructed to achieve the original signal denoising. In the simulation experiment, the denoising effect of different denoising algorithms in the time domain and frequency domain is compared, and SNR and RMSE are used as evaluation indexes. The results show that the proposed algorithm has better performance. In the experiment of hydrophone, the denoising ability of the proposed algorithm is also verified.