liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Approximate Diagonalized Covariance Matrix for Signals with Correlated Noise
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-1971-4295
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
University of Groningen, Centre for Theoretical Physics, Zernike Institute for Advanced Materials.
2016 (English)In: Proceedings of the 19th International Conference of Information Fusion, Institute of Electrical and Electronics Engineers (IEEE), 2016, 521-527 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a diagonal covariance matrix approximation for Wide-Sense Stationary (WSS) signals with correlated Gaussian noise. Existing signal models that incorporate correlations often require regularization of the covariance matrix, so that the covariance matrix can be inverted. The disadvantage of this approach is that matrix inversion is computational intensive and regularization decreases precision. We use Bienayme's theorem to approximate the covariance matrix by a diagonal one, so that matrix inversion becomes trivial, even with nonuniform rather than only uniform sampling that was considered in earlier work. This approximation reduces the computational complexity of the estimator and estimation bound significantly. We numerically validate this approximation and compare our approach with the Maximum Likelihood Estimator (MLE) and Cramer-Rao Lower Bound (CRLB) for multivariate Gaussian distributions. Simulations show that our approach differs less than 0.1% from this MLE and CRLB when the observation time is large compared to the correlation time. Additionally, simulations show that in case of non-uniform sampling, we increase the performance in comparison to earlier work by an order of magnitude. We limit this study to correlated signals in the time domain, but the results are also applicable in the space domain.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. 521-527 p.
Keyword [en]
Signal processing, approximate estimation
National Category
Control Engineering Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-130162ISI: 000391273400071ISBN: 978-0-9964527-4-8 (print)OAI: oai:DiVA.org:liu-130162DiVA: diva2:948531
Conference
19th International Conference of Information Fusion, Heidelberg, Germany, July 5-8 2016
Funder
Swedish Research Council
Available from: 2016-07-12 Created: 2016-07-12 Last updated: 2017-02-03Bibliographically approved

Open Access in DiVA

fulltext(151 kB)59 downloads
File information
File name FULLTEXT02.pdfFile size 151 kBChecksum SHA-512
1c9715ea79bdc8d8fde6852ea6171102da73cf9c77ff5e8880496c84d9a05bd73501573b9ea02893a7508e6315feca39700bee9b2fae12ae31a4d4b309641604
Type fulltextMimetype application/pdf

Other links

Link to publication

Authority records BETA

Dil, BramHendeby, GustafGustafsson, Fredrik

Search in DiVA

By author/editor
Dil, BramHendeby, GustafGustafsson, Fredrik
By organisation
Automatic ControlFaculty of Science & Engineering
Control EngineeringSignal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 103 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 405 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf