﻿ OPUS 4 | A fast algorithm for selective signal extrapolation with arbitrary basis functions

## A fast algorithm for selective signal extrapolation with arbitrary basis functions

• Signal extrapolation is an important task in digital signal processing for extending known signals into unknown areas. The Selective Extrapolation is a very effective algorithmto achieve this. Thereby, the extrapolation is obtained by generating a model of the signal to be extrapolated as weighted superposition of basis functions. Unfortunately, this algorithm is computationally very expensive and, up to now, efficient implementations exist only for basis function sets that emanate from discrete transforms.Within the scope of this contribution, a novel efficient solution for Selective Extrapolation is presented for utilization with arbitrary basis functions. The proposed algorithm mathematicSignal extrapolation is an important task in digital signal processing for extending known signals into unknown areas. The Selective Extrapolation is a very effective algorithmto achieve this. Thereby, the extrapolation is obtained by generating a model of the signal to be extrapolated as weighted superposition of basis functions. Unfortunately, this algorithm is computationally very expensive and, up to now, efficient implementations exist only for basis function sets that emanate from discrete transforms.Within the scope of this contribution, a novel efficient solution for Selective Extrapolation is presented for utilization with arbitrary basis functions. The proposed algorithm mathematically behaves identically to the original Selective Extrapolation but is several decades faster. Furthermore, it is able to outperform existent fast transform domain algorithms which are limited to basis function sets that belong to the corresponding transform.With that, the novel algorithm allows for an efficient use of arbitrary basis functions, even if they are only numerically defined

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Author: Jürgen Seiler, Andre Kaup urn:nbn:de:bvb:29-opus-31258 Article English 2011 Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Lehrstuhl für Multimediakommunikation und Signalverarbeitung 2012/03/22 - EURASIP Journal on Advances in Signal Processing 2011.Jan (2011): 22.03.2012 Technische Fakultät / Technische Fakultät -ohne weitere Spezifikation- Informatik, Informationswissenschaft, allgemeine Werke / Informatik, Wissen, Systeme / Datenverarbeitung; Informatik open_access Universität Erlangen-Nürnberg / Von der FAU geförderte Open Access Artikel / Von der FAU geförderte Open Access Artikel 2011

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