EMD-Based Filtering Using Similarity Measure Between Probability Density Functions of IMFs
Langue
en
Article de revue
Ce document a été publié dans
IEEE Transactions on Instrumentation and Measurement. 2014-01, vol. 63, n° 1, p. 27-34
Institute of Electrical and Electronics Engineers
Résumé en anglais
This paper introduces a new signal-filtering which combines the empirical mode decomposition (EMD) and a similarity measure. A noisy signal is adaptively broken down into oscillatory components called intrinsic mode functions ...Lire la suite >
This paper introduces a new signal-filtering which combines the empirical mode decomposition (EMD) and a similarity measure. A noisy signal is adaptively broken down into oscillatory components called intrinsic mode functions (IMFs) by EMD followed by an estimation of the probability density function (pdf) of each extracted mode. The key idea of this paper is to make use of partial reconstruction, the relevant modes being selected on the basis of a striking similarity between the pdf of the input signal and that of each mode. Different similarity measures are investigated and compared. The obtained results, on simulated and real signals, show the effectiveness of the pdf-based filtering strategy for removing both white Gaussian and colored noises and demonstrate its superior performance over partial reconstruction approaches reported in the literature.< Réduire
Mots clés en anglais
Consecutive mean squared error (CMSE)
Empirical mode decomposition (EMD)
Intrinsic mode function (IMF)
Probability density function (pdf)
Signal filtering
Similarity measure
Origine
Importé de halUnités de recherche