Signal extrapolation based on generalized singular value decomposition using prior information

Akira Sano, Hiroyuki Tsuji, Hiromitsu Ohmori

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Extrapolation of band-limited signals in noisy conditions is an ill-posed least-squares estimation problem. To stabilize the extrapolation, derivative smoothness of signals to be extrapolated is introduced to a weighted-least-squares error criterion as prior information. One can adjust the weighting of the smoothness by employing multiple regularization parameters to be determined optimally. The extrapolated signal is given by using the generalized singular value decomposition, which is modified by the regularization. On the basis of a Bayesian statistical approach, a new information-theoretic criterion is presented to determined the optimal regularization parameters, which can give optimal balance between the smoothness prior and the observed signal data to attain stabilized extrapolation by optimal regularization.

Original languageEnglish
Title of host publicationProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
PublisherPubl by IEEE
Pages1749-1752
Number of pages4
ISBN (Print)0780300033
DOIs
Publication statusPublished - 1991
Externally publishedYes
EventProceedings of the 1991 International Conference on Acoustics, Speech, and Signal Processing - ICASSP 91 - Toronto, Ont, Can
Duration: 1991 May 141991 May 17

Publication series

NameProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Volume3
ISSN (Print)0736-7791

Other

OtherProceedings of the 1991 International Conference on Acoustics, Speech, and Signal Processing - ICASSP 91
CityToronto, Ont, Can
Period91/5/1491/5/17

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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