Improved EMD Using Doubly-Iterative Sifting and High Order Spline Interpolation
Empirical mode decomposition (EMD) is a signal analysis method which has received much attention lately due to its application in a number of fields. The main disadvantage of EMD is that it lacks a theoretical analysis and, therefore, our understanding of EMD comes from an intuitive and experimental...
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Series: | EURASIP Journal on Advances in Signal Processing |
Online Access: | http://dx.doi.org/10.1155/2008/128293 |
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doaj-79b6e0b8b0f44c7ea2ad59cd82d9632b2020-11-25T01:56:13ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802008-04-01200810.1155/2008/128293Improved EMD Using Doubly-Iterative Sifting and High Order Spline InterpolationSteve McLaughlinYannis KopsinisEmpirical mode decomposition (EMD) is a signal analysis method which has received much attention lately due to its application in a number of fields. The main disadvantage of EMD is that it lacks a theoretical analysis and, therefore, our understanding of EMD comes from an intuitive and experimental validation of the method. Recent research on EMD revealed improved criteria for the interpolation points selection. More specifically, it was shown that the performance of EMD can be significantly enhanced if, as interpolation points, instead of the signal extrema, the extrema of the subsignal having the higher instantaneous frequency are used. Even if the extrema of the subsignal with the higher instantaneous frequency are not known in advance, this new interpolation points criterion can be effectively exploited in doubly-iterative sifting schemes leading to improved decomposition performance. In this paper, the possibilities and limitations of the developments above are explored and the new methods are compared with the conventional EMD.http://dx.doi.org/10.1155/2008/128293 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Steve McLaughlin Yannis Kopsinis |
spellingShingle |
Steve McLaughlin Yannis Kopsinis Improved EMD Using Doubly-Iterative Sifting and High Order Spline Interpolation EURASIP Journal on Advances in Signal Processing |
author_facet |
Steve McLaughlin Yannis Kopsinis |
author_sort |
Steve McLaughlin |
title |
Improved EMD Using Doubly-Iterative Sifting and High Order Spline Interpolation |
title_short |
Improved EMD Using Doubly-Iterative Sifting and High Order Spline Interpolation |
title_full |
Improved EMD Using Doubly-Iterative Sifting and High Order Spline Interpolation |
title_fullStr |
Improved EMD Using Doubly-Iterative Sifting and High Order Spline Interpolation |
title_full_unstemmed |
Improved EMD Using Doubly-Iterative Sifting and High Order Spline Interpolation |
title_sort |
improved emd using doubly-iterative sifting and high order spline interpolation |
publisher |
SpringerOpen |
series |
EURASIP Journal on Advances in Signal Processing |
issn |
1687-6172 1687-6180 |
publishDate |
2008-04-01 |
description |
Empirical mode decomposition (EMD) is a signal analysis method which has received much attention lately due to its application in a number of fields. The main disadvantage of EMD is that it lacks a theoretical analysis and, therefore, our understanding of EMD comes from an intuitive and experimental validation of the method. Recent research on EMD revealed improved criteria for the interpolation points selection. More specifically, it was shown that the performance of EMD can be significantly enhanced if, as interpolation points, instead of the signal extrema, the extrema of the subsignal having the higher instantaneous frequency are used. Even if the extrema of the subsignal with the higher instantaneous frequency are not known in advance, this new interpolation points criterion can be effectively exploited in doubly-iterative sifting schemes leading to improved decomposition performance. In this paper, the possibilities and limitations of the developments above are explored and the new methods are compared with the conventional EMD. |
url |
http://dx.doi.org/10.1155/2008/128293 |
work_keys_str_mv |
AT stevemclaughlin improvedemdusingdoublyiterativesiftingandhighordersplineinterpolation AT yanniskopsinis improvedemdusingdoublyiterativesiftingandhighordersplineinterpolation |
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1724980798757535744 |