Abstract
The complexities of the marine environment and the unique characteristics of underwater channels pose challenges in obtaining reliable signals underwater, necessitating the filtration of underwater acoustic noise. Herein, an underwater acoustic signal denoising method based on ensemble empirical mode decomposition (EEMD), correlation coefficient (CC), permutation entropy (PE), and wavelet threshold denoising (WTD) is proposed. Furthermore, simulation experiments are conducted using simulated and real underwater acoustic data. The experimental results reveal that the proposed denoising method outperforms other previous methods in terms of signal-to-noise ratio, root mean square error, and CC. The proposed method eliminates noise and retains valuable information in the signal.
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Funding Supported by the National Natural Science Foundation of China (No. 62033011), and Science and Technology Project of Hebei Province (No. 216Z1704G, No. 20310401D).
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Article Highlights
• This study mitigates partial loss in traditional denoising by combining denoising with PE and WTD, leveraging EEMD.
• PE segregates noise into pure and noise-dominant components, preserving effective signals through targeted denoising of the noise-dominant component.
• The method efficiently removes noise, eliminating local prominent peaks in the signal for a smoother and refined final output.
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Zhang, Y., Yang, Z., Du, X. et al. A New Method for Denoising Underwater Acoustic Signals Based on EEMD, Correlation Coefficient, Permutation Entropy, and Wavelet Threshold Denoising. J. Marine. Sci. Appl. 23, 222–237 (2024). https://doi.org/10.1007/s11804-024-00386-6
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DOI: https://doi.org/10.1007/s11804-024-00386-6