COMPLEMENTARY ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOVEL NOISE ENHANCED DATA ANALYSIS METHOD

TLDR

Mode‑mixing caused by intermittent signals is a major issue in Empirical Mode Decomposition, and although Ensemble EMD mitigates this, it introduces residue noise that can only be reduced by prohibitively large ensembles. The authors propose an improved noise‑enhanced data analysis algorithm to address these limitations. Their method extracts the residue of added white noise from data mixtures using complementary ensemble IMFs with positive and negative noise, and numerical experiments confirm its effectiveness while illustrating mode‑splitting and translation issues. The new approach achieves IMFs with RMS noise comparable to EEMD yet effectively eliminates residue noise.

Abstract

The phenomenon of mode-mixing caused by intermittence signals is an annoying problem in Empirical Mode Decomposition (EMD) method. The noise assisted method of Ensemble EMD (EEMD) has not only effectively resolved this problem but also generated a new one, which tolerates the residue noise in the signal reconstruction. Of course, the relative magnitude of the residue noise could be reduced with large enough ensemble, it would be too time consuming to implement. An improved algorithm of noise enhanced data analysis method is suggested in this paper. In this approach, the residue of added white noises can be extracted from the mixtures of data and white noises via pairs of complementary ensemble IMFs with positive and negative added white noises. Though this new approach yields IMF with the similar RMS noise as EEMD, it effectively eliminated residue noise in the IMFs. Numerical experiments were conducted to demonstrate the new approach and also illustrate the problems of mode splitting and translation.