Noise-Assisted EMD Methods in Action

In this work we explore the capabilities of two noise-assisted EMD methods: Ensemble EMD (EEMD) and the recently proposed Complete Ensemble EMD with Adaptive Noise (CEEMDAN), to recover a pure tone embedded in different kinds of noise, both stationary and nonstationary. Experiments are carried out for assessing their performances with respect to the level of the added noise and the number of realizations used for averaging. The obtained results partly support empirical recommendations reported in the literature while evidencing new distinctive features. While EEMD presents quite different behaviors for different situations, CEEMDAN evidences some robustness with an almost unaffected performance for the studied cases.

[1]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[2]  Gregoire Nicolis,et al.  Stochastic resonance , 2007, Scholarpedia.

[3]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[4]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[5]  Gabriel Rilling,et al.  On empirical mode decomposition and its algorithms , 2003 .

[6]  Rami K. Niazy,et al.  Performance Evaluation of Ensemble Empirical Mode Decomposition , 2009, Adv. Data Sci. Adapt. Anal..

[7]  Patrick Flandrin,et al.  A complete ensemble empirical mode decomposition with adaptive noise , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[8]  P. Flandrin,et al.  Empirical Mode Decomposition , 2012 .