KOMATY, Ali; BOUDRAA, Abdel-Ouahab; AUGIER, Benoit; DARE, Delphine

doi:10.1109/TIM.2013.2275243

Metadatos

Mostrar el registro completo del ítem

Licencia de uso del documento

KOMATY, Ali
Institut de Recherche de l'Ecole Navale [IRENAV]

BOUDRAA, Abdel-Ouahab
Institut de Recherche de l'Ecole Navale [IRENAV]

AUGIER, Benoit
Institut de Recherche de l'Ecole Navale [IRENAV]

Idioma

Article de revue

Este ítem está publicado en

IEEE Transactions on Instrumentation and Measurement. 2014-01, vol. 63, n° 1, p. 27-34

Institute of Electrical and Electronics Engineers

Resumen en inglés

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.< Leer menos

Palabras clave en inglés

Consecutive mean squared error (CMSE)

Empirical mode decomposition (EMD)

Intrinsic mode function (IMF)

Probability density function (pdf)

Signal filtering

Similarity measure

Metadatos

Compartir este ítem

Licencia de uso del documento

EMD-Based Filtering Using Similarity Measure Between Probability Density Functions of IMFs

Idioma

Este ítem está publicado en

Resumen en inglés

Palabras clave en inglés

URI

DOI

Orígen

Centros de investigación