Enhanced space-time covariance estimation based on a system identification approach

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Abstract

The error inflicted on a space-time covariance estimate due to the availability of only finite data is known to perturb the eigenvalues and eigenspaces of its z-domain equivalent, i.e., the cross-spectral density matrix. In this paper, we show that a significantly more accurate estimate can be obtained if the source signals driving the signal model are also accessible, such that a system identication approach for the source model becomes viable. We demonstrate this improved accuracy in simulations, and discuss its dependencies on the sample size and the signal to noise ratio of the data.

Original languageEnglish
Title of host publication2022 Sensor Signal Processing for Defence Conference, SSPD 2022 - Proceedings
Place of PublicationPiscataway, NJ.
PublisherIEEE
Number of pages5
ISBN (Electronic)9781665483483
ISBN (Print)9781665483483
DOIs
Publication statusPublished - 23 Sep 2022
Event11th International Conference in Sensor Signal Processing for Defence: from Sensor to Decision - London, United Kingdom
Duration: 13 Sep 202214 Sep 2022
Conference number: 11th
https://sspd.eng.ed.ac.uk/

Publication series

Name2022 Sensor Signal Processing for Defence Conference, SSPD 2022 - Proceedings

Conference

Conference11th International Conference in Sensor Signal Processing for Defence
Abbreviated titleSSPD 2022
Country/TerritoryUnited Kingdom
CityLondon
Period13/09/2214/09/22
Internet address

Keywords

  • enhanced space-time covariance
  • estimation
  • system identification approach
  • eigenvalues
  • eigenspaces
  • improved accuracy

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