Empirical Mode Decomposition De-noising Method Based on Principal Component Analysis

WANG Wen-bo; ZHANG Xiao-dong; WANG Xiang-li

doi:10.3969/j.issn.0372-2112.2013.07.028

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Empirical Mode Decomposition De-noising Method Based on Principal Component Analysis

更新时间：2025-07-16

- Empirical Mode Decomposition De-noising Method Based on Principal Component Analysis
- Acta Electronica Sinica Vol. 41, Issue 7, Pages: 1425-1430(2013)
- 作者机构：
  
  1. 武汉科技大学信息与计算科学系,湖北,武汉,430065
  2. 遥感科学国家重点实验室,北京,100101
  3. 武汉大学测绘遥感信息工程国家重点实验室,湖北,武汉,430072
  4. 武汉理工大学计算机科学与技术学院,湖北,武汉,430063
  5. 武汉科技大学信息与计算科学系,湖北,武汉,430065
  6. 遥感科学国家重点实验室,北京,100101
  7. 武汉大学测绘遥感信息工程国家重点实验室,湖北,武汉,430072
  8. 武汉理工大学计算机科学与技术学院,湖北,武汉,430063
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2013.07.028
  CLC： TN911.72
- Published：2013
- 稿件说明：
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WANG Wen-bo, ZHANG Xiao-dong, WANG Xiang-li. Empirical Mode Decomposition De-noising Method Based on Principal Component Analysis[J]. Acta Electronica Sinica, 2013, 41(7): 1425-1430.
DOI：

WANG Wen-bo, ZHANG Xiao-dong, WANG Xiang-li. Empirical Mode Decomposition De-noising Method Based on Principal Component Analysis[J]. Acta Electronica Sinica, 2013, 41(7): 1425-1430. DOI： 10.3969/j.issn.0372-2112.2013.07.028.

摘要

针对非线性非平稳信号的去噪问题

提出一种基于主成分分析(PCA)的经验模态分解(EMD)消噪方法.该方法根据EMD的分解特性

利用PCA对噪声信号经EMD分解后的内蕴模态函数(IMF)进行去噪处理:首先利用3

法则对第一层IMF进行细节信息提取

并估计每层IMF中所含噪声的能量;然后对IMF进行PCA变换

根据IMF中所含噪声的能量选择合适数目的主成分分量进行重构

以去除IMF中的噪声.为验证本文方法的有效性

进行了数字仿真与实例应用实验.实验结果均表明

所提方法的消噪效果整体上优于Bayesian小波阈值消噪方法和基于模态单元的EMD阈值消噪方法

是一种有效的信号消噪新方法.

Abstract

In order to solve the problem of nonlinear and nonstationary signal de-noising

a novel de-noising method is proposed by combining the principal component analysis(PCA) and empirical mode decomposition(EMD).The method removes noise of intrinsic mode functions(IMFs) using PCA

after the noisy signal is decomposed by EMD.Firstly

the signal details of the first IMF are extracted by using 3

criterion

and the noise energy of each level IMF is estimated.Secondly

the PCA is implemented on each IMF

and the part of principle components are selected to reconstruct the IMF according to noise energy of IMFs

then the noise of IMF is removed efficiently.Numerical simulation and real data test were carried

out to evaluate the performance of the proposed method.The experimental results showed that the proposed method outperformed the Bayesian wavelet threshold de-noising algorithm and mode cell EMD de-noising algorithm.So it is an effective signal de-noising method.

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