Study of boundary conditions in the Iterative Filtering method for the decomposition of nonstationary signals
For researchers using Iterative Filtering, this work provides guidance on choosing boundary conditions to improve signal decomposition accuracy.
The paper studies how different boundary conditions affect the decomposition produced by the Iterative Filtering method for nonstationary signals, showing that periodic extension is not always optimal and other conditions can improve decomposition quality.
Nonstationary and nonlinear signals are ubiquitous in real life. Their decomposition and analysis is an important topic of research in signal processing. Recently a new technique, called Iterative Filtering, has been developed with the goal of decomposing such signals into simple oscillatory components. Several papers have been published regarding the analysis of this technique from a mathematical point of view. All these work start with the assumption that each compactly supported signal is extended outside the boundaries periodically. In this work we tackle the problem of studying the influence of different boundary conditions on the decompositions produce by the Iterative Filtering method.