Title: Empirical Mode Decomposition De-noising Method Based on Principal Component Analysis
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 3σ 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.
Publication Year: 2013
Publication Date: 2013-01-01
Language: en
Type: article
Access and Citation
Cited By Count: 11
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot