Numerical Analysis for Iterative Filtering with New Efficient Implementations Based on FFT
Provides a faster and theoretically grounded implementation of Iterative Filtering for signal processing researchers working with non-stationary data.
The paper analyzes the convergence of the Iterative Filtering method for non-stationary signal decomposition and proposes new efficient implementations using FFT, reducing the iterative algorithm to a direct method. Numerical experiments demonstrate significant speed improvements.
Real life signals are in general non--stationary and non--linear. The development of methods able to extract their hidden features in a fast and reliable way is of high importance in many research fields. In this work we tackle the problem of further analyzing the convergence of the Iterative Filtering method both in a continuous and a discrete setting in order to provide a comprehensive analysis of its behavior. Based on these results we provide new ideas for efficient implementations of Iterative Filtering algorithm which are based on Fast Fourier Transform (FFT), and the reduction of the original iterative algorithm to a direct method.