Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/34442
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Validated analysis of modulated signals: From de Prony to Padé and beyond
Author(s): Cuyt, Annie
Hou, Yuan
Lee, Wen-shin
Contact Email: wen-shin.lee@stir.ac.uk
Keywords: Exponential analysis
Modulation
Validation
Prony polynomial
Padé approximant
Froissart doublet
Issue Date: 15-Oct-2022
Date Deposited: 22-Jun-2022
Citation: Cuyt A, Hou Y & Lee W (2022) Validated analysis of modulated signals: From de Prony to Padé and beyond. Journal of Computational and Applied Mathematics, 413, Art. No.: 114346. https://doi.org/10.1016/j.cam.2022.114346
Abstract: The spectral analysis of modulated signals has attracted quite some research, mainly because of the fact that Fourier methods are not particularly suitable. Among the challenges, we mention the separation of close components that differ significantly in magnitude, the limitation of the sampling duration, the probable ill-conditioning of certain structured matrices. We show how a validated exponential analysis add-on, for use with any standard exponential analysis method, offers a lot of advantages in the context of these challenges. The add-on uses an alias-free decimation technique and essentially combines the basics of de Prony’s method for exponential fitting with the theory of Padé approximation theory.
DOI Link: 10.1016/j.cam.2022.114346
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. Accepted refereed manuscript of: Cuyt A, Hou Y & Lee W (2022) Validated analysis of modulated signals: From de Prony to Padé and beyond. Journal of Computational and Applied Mathematics, 413, Art. No.: 114346. https://doi.org/10.1016/j.cam.2022.114346 © 2022, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Licence URL(s): http://creativecommons.org/licenses/by-nc-nd/4.0/

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