CEEMDAN-SVD Motor Noise Reduction Method and Application Based on Underwater Glider Noise Characteristics
Year: 2025
Type: article
Abstract: When utilizing underwater gliders to observe submerged targets, ensuring the quality and reliability of the acquired target characteristic signals is paramount. However, the signal acquisition process is significantly compromised by noise generated from various motors on the platform, which severely contaminates the authentic target signal characteristics, thereby complicating subsequent research efforts such as target identification. Given the limited capability of wavelet transforms in processing complex non-stationary signals, and considering the non-stationary and non-linear nature of the signals in question, this study focuses on the denoising of hydroacoustic signals and the characteristics of motor noise. Building upon the traditional CEEMDAN-SVD approach, we propose an adaptive noise reduction method that combines the maximum singular value of motor noise with the differential spectrum of singular values. In particular, this paper delves into the symmetry between the noise subspace and the signal subspace in SVD decomposition. By analyzing the symmetric characteristics of their singular value distributions, the process of separating noise from signals is further optimized. The effectiveness of this denoising method is analyzed and validated through simulations and experiments. The results demonstrate that under a signal-to-noise ratio (SNR) of 3 dB, the improved CEEMDAN-SVD method reduces the mean square error by an average of 22.8% and decreases the absolute value of skewness by 27.8% compared to the traditional CEEMDAN-SVD method. These findings indicate that our proposed method exhibits superior noise reduction capabilities under strong non-stationary motor noise interference, effectively enhancing the SNR and reinforcing signal characteristics. This provides a robust foundation for improving the recognition rate of hydroacoustic targets in subsequent research.
Source: Symmetry
DOI: 10.3390/sym17030378
Authors: Haotian Zhao, Maofa Wang
Institution: Hangzhou Dianzi University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4408110081