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EMPIRICAL MODE DECOMPOSITIONS AS DATA-DRIVEN WAVELET-LIKE EXPANSIONS

2004; World Scientific; Volume: 02; Issue: 04 Linguagem: Inglês

10.1142/s0219691304000561

1793-690X

Patrick Flandrin, Paulo Gonçalvès,

Structural Health Monitoring Techniques

Huang's data-driven technique of Empirical Mode Decomposition (EMD) is applied to the versatile, broadband, model of fractional Gaussian noise (fGn). The experimental spectral analysis and statistical characterization of the obtained modes reveal an equivalent filter bank structure which shares most properties of a wavelet decomposition in the same context, in terms of self-similarity, quasi-decorrelation and variance progression. Furthermore, the spontaneous adaptation of EMD to "natural" dyadic scales is shown, rationalizing the method as an alternative way for estimating the fGn Hurst exponent.