@Article{NMTMA-10-2,
author = {},
title = {Multidimensional Iterative Filtering Method for the Decomposition of High-Dimensional Non-Stationary Signals},
journal = {Numerical Mathematics: Theory, Methods and Applications},
year = {2017},
volume = {10},
number = {2},
pages = {278--298},
abstract = {<p style="text-align: justify;">Iterative Filtering (IF) is an alternative technique to the Empirical
Mode Decomposition (EMD) algorithm for the decomposition
of non-stationary and non-linear signals. Recently in [3] IF has been
proved to be convergent for any $L^2$&nbsp;signal and its stability
has been also demonstrated through examples. Furthermore, in [3] the so called
Fokker-Planck (FP) filters have been introduced. They are
smooth at every point and have compact supports. Based on
those results, in this paper we introduce the Multidimensional
Iterative Filtering (MIF) technique for the decomposition and
time-frequency analysis of non-stationary high-dimensional signals.
We present the extension of FP filters to higher dimensions.
We prove convergence results under general sufficient conditions on the filter shape.
Finally we illustrate the promising performance of MIF algorithm, equipped with high-dimensional FP filters, when
applied to the decomposition of two dimensional signals.</p>},
issn = {2079-7338},
doi = {https://doi.org/10.4208/nmtma.2017.s05},
url = {https://global-sci.com/article/90500/multidimensional-iterative-filtering-method-for-the-decomposition-of-high-dimensional-non-stationary-signals}
}