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Open AccessArticle

Noise Reduction Method of Underwater Acoustic Signals Based on Uniform Phase Empirical Mode Decomposition, Amplitude-Aware Permutation Entropy, and Pearson Correlation Coefficient

School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
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Entropy 2018, 20(12), 918; https://doi.org/10.3390/e20120918
Received: 6 October 2018 / Revised: 25 November 2018 / Accepted: 28 November 2018 / Published: 30 November 2018
(This article belongs to the Special Issue Permutation Entropy & Its Interdisciplinary Applications)
Noise reduction of underwater acoustic signals is of great significance in the fields of military and ocean exploration. Based on the adaptive decomposition characteristic of uniform phase empirical mode decomposition (UPEMD), a noise reduction method for underwater acoustic signals is proposed, which combines amplitude-aware permutation entropy (AAPE) and Pearson correlation coefficient (PCC). UPEMD is a recently proposed improved empirical mode decomposition (EMD) algorithm that alleviates the mode splitting and residual noise effects of EMD. AAPE is a tool to quantify the information content of nonlinear time series. Unlike permutation entropy (PE), AAPE can reflect the amplitude information on time series. Firstly, the original signal is decomposed into a series of intrinsic mode functions (IMFs) by UPEMD. The AAPE of each IMF is calculated. The modes are separated into high-frequency IMFs and low-frequency IMFs, and all low-frequency IMFs are determined as useful IMFs (UIMFs). Then, the PCC between the high-frequency IMF with the smallest AAPE and the original signal is calculated. If PCC is greater than the threshold, the IMF is also determined as a UIMF. Finally, all UIMFs are reconstructed and the denoised signal is obtained. Chaotic signals with different signal-to-noise ratios (SNRs) are used for denoising experiments. Compared with EMD and extreme-point symmetric mode decomposition (ESMD), the proposed method has higher SNR and smaller root mean square error (RMSE). The proposed method is applied to noise reduction of real underwater acoustic signals. The results show that the method can further eliminate noise and the chaotic attractors are smoother and clearer. View Full-Text
Keywords: underwater acoustic signals; uniform phase empirical mode decomposition; amplitude-aware permutation entropy; Pearson correlation coefficient; noise reduction underwater acoustic signals; uniform phase empirical mode decomposition; amplitude-aware permutation entropy; Pearson correlation coefficient; noise reduction
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Li, G.; Yang, Z.; Yang, H. Noise Reduction Method of Underwater Acoustic Signals Based on Uniform Phase Empirical Mode Decomposition, Amplitude-Aware Permutation Entropy, and Pearson Correlation Coefficient. Entropy 2018, 20, 918.

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