Empirical mode decomposition using variable filtering with time scale calibrating

A novel and efficient method for decomposing a signal into a set of intrinsic mode functions （IMFs） and a trend is proposed. Unlike the original empirical mode decomposition （EMD）, which uses spline fits to extract variations from the signal by separating the local mean from the fluctuations in the decomposing process, this new method being proposed takes advantage of the theory of variable finite impulse response （FIR） filtering where filter coefficients and breakpoint frequencies can be adjusted to track any peak-to-peak time scale changes. The IMFs are results of a multiple variable frequency response FIR filtering when signals pass through the filters. Numerical examples validate that in contrast with the original EMD, the proposed method can fine-tune the frequency resolution and suppress the aliasing effectively.