Determining Joint Periodicities in Multi-Time Data with Sampling Uncertainties

David Svedberg, Filip Elvander, Andreas Jakobsson

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

Abstract

In this work, we introduce a novel approach for determining a joint sparse spectrum from several non-uniformly sampled data sets, where each data set is assumed to have its own, and only partially known, sampling times. The problem originates in paleoclimatology, where each data point derives from a separate ice core measurement, resulting in that even though all measurements reflect the same periodicities, the sampling times and phases differ among the data sets, with the sampling times being only approximately known. The proposed estimator exploits all available data using a sparse reconstruction framework allowing for a reliable and robust estimation of the underlying periodicities. The performance of the method is illustrated using both simulated and measured ice core data sets.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Pages5737-5741
Number of pages5
ISBN (Electronic)9781665405409
DOIs
Publication statusPublished - 2022
Event47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, Singapore
Duration: 2022 May 232022 May 27

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2022-May
ISSN (Print)1520-6149

Conference

Conference47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
Country/TerritorySingapore
CityVirtual, Online
Period2022/05/232022/05/27

Bibliographical note

Publisher Copyright:
© 2022 IEEE

Subject classification (UKÄ)

  • Mathematics

Free keywords

  • Irregular Sampling
  • Misspecified Modelling
  • Multi-time
  • Paleoclimatology

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