Open AccessEnsemble EMD‐based signal denoising using modified interval thresholding
Author(s) -
Wang Hongrui,
Liu Zhigang,
Song Yang,
Lu Xiaobing
Publication year - 2017
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2016.0147
Subject(s) - hilbert–huang transform , thresholding , noise reduction , artificial intelligence , aliasing , computer science , pattern recognition (psychology) , signal (programming language) , step detection , noise (video) , algorithm , white noise , computer vision , filter (signal processing) , image (mathematics) , programming language , telecommunications , undersampling
Empirical mode decomposition (EMD) is extensively realised in its potential of non‐parametric signal denoising. Ensemble EMD (EEMD) is an improved self‐adapting signal decomposition approach that can produce signal components with no frequency aliasing. In this study, the interval thresholding and iteration operation of EMD‐based denoising techniques are applied to the EEMD and found not entirely feasible in the EEMD case. A modified interval thresholding is proposed, which can be adjustable for the intrinsic mode functions from EEMD. By taking advantage of the characteristics of EEMD, the internal and external iterations are compared and properly adopted in the EEMD‐based denoising strategy. As a result, the EEMD‐based denoising methods are proposed by combining the modified interval thresholding and the iterations. The denoising results on synthetic and real‐life signals indicate that the presented methods exhibit better performance comparing with EMD‐based methods, especially for signals with low signal‐to‐noise ratio. Based on the time complexities of the proposed methods, the acceptable sampling frequencies of the methods in real‐time denoising are given.