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Adaptive Denoising Algorithm Using Peak Statistics-Based Thresholding and Novel Adaptive Complementary Ensemble Empirical Mode Decomposition
Information Sciences ( IF 8.1 ) Pub Date : 2021-02-26 , DOI: 10.1016/j.ins.2021.02.040
Mengfei Hu , Shuqing Zhang , Wei Dong , Fengjiao Xu , Haitao Liu

This paper proposes an adaptive denoising methodology for noisy signals that employs a novel adaptive complementary ensemble empirical mode decomposition (NACEEMD) and a peak statistics (PS)-based thresholding technique. The key idea in this paper is the peak statistics (PS)-based thresholding technique,which breaks the traditional strategy with respect to selecting more accurate and more adaptive thresholds. The NACEEMD algorithm is proposed to decompose the noisy signal into a series of intrinsic mode functions (IMFs). At the same time, NACEEMD is also used to verify the applicability of the PS-based thresholding technique in different decomposition algorithms. The PS-based threshold is used to remove the noise inherent in noise-dominant IMFs, and the denoised signal is reconstructed by combining the denoised noise-dominant IMFs and the signal-dominant IMFs. This paper uses a various of simulated signals in various noisy environments for experiments, the experimental results indicate that the proposed algorithm outperforms traditional threshold denoising methodologies in terms of signal-to-noise ratio, root mean square error, and percent root distortion. Moreover, through real ECG signal and multi-sensor data fusion experiments, the application of the proposed algorithm in the field of engineering is explored and expanded.



中文翻译:

基于峰值统计的阈值和新型自适应互补集合经验模式分解的自适应降噪算法

本文提出了一种适用于噪声信号的自适应去噪方法,该方法采用了新型的自适应互补集成经验模式分解(NACEEMD)和基于峰值统计(PS)的阈值技术。本文的主要思想是基于峰值统计(PS)的阈值技术,该技术打破了传统的选择更准确,更自适应阈值的策略。提出了NACEEMD算法,将噪声信号分解为一系列固有模式函数(IMF)。同时,NACEEMD还用于验证基于PS的阈值技术在不同分解算法中的适用性。基于PS的阈值用于消除噪声为主的IMF中固有的噪声,通过组合去噪噪声为主的IMF和信号为主IMF来重构去噪信号。本文在各种嘈杂环境中使用了各种模拟信号进行实验,实验结果表明,该算法在信噪比,均方根误差和均方根失真方面均优于传统的阈值去噪方法。此外,通过实际的心电信号和多传感器数据融合实验,探索并扩展了该算法在工程领域的应用。均方根误差和百分比失真。此外,通过实际的心电信号和多传感器数据融合实验,探索并扩展了该算法在工程领域的应用。均方根误差和百分比失真。此外,通过实际的心电信号和多传感器数据融合实验,探索并扩展了该算法在工程领域的应用。

更新日期:2021-02-26
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