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
Vector ARMA estimation: A reliable subspace approach
Royal Inst Technol, Stockholm, Sweden Uppsala Univ, Syst & Control Grp, Uppsala, Sweden Linkoping Univ, Div Automat Control, Linkoping, Sweden.
Royal Inst Technol, Stockholm, Sweden Uppsala Univ, Syst & Control Grp, Uppsala, Sweden Linkoping Univ, Div Automat Control, Linkoping, Sweden.
Royal Inst Technol, Stockholm, Sweden Uppsala Univ, Syst & Control Grp, Uppsala, Sweden Linkoping Univ, Div Automat Control, Linkoping, Sweden.
2000 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 48, no 7, 2092-2104 p.Article in journal (Refereed) Published
Abstract [en]

A parameter estimation method for finite dimensional multivariate linear stochastic systems, which is guaranteed to produce valid models approximating the true underlying system in a computational time of a polynomial order in the system dimension, is presented. This is achieved by combining the main features of certain stochastic subspace identification techniques with sound matrix Schur restabilizing procedures and multivariate covariance fitting, both of which are formulated as linear matrix inequality problems. all aspects of the identification method are discussed, with an emphasis on the two issues mentioned above, and examples of the overall performance are provided for two different systems.

Place, publisher, year, edition, pages
2000. Vol. 48, no 7, 2092-2104 p.
Keyword [en]
autoregressive moving-average, covariance fitting, linear matrix inequalities, polynomial factorizations, Schur stabilization, semidefinite programming, system identification, vector-valued linear stochastic systems
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:liu:diva-49694OAI: oai:DiVA.org:liu-49694DiVA: diva2:270590
Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2017-12-12

Open Access in DiVA

No full text

In the same journal
IEEE Transactions on Signal Processing
Natural Sciences

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 15 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