DocumentCode
1219114
Title
A subspace fitting method for identification of linear state-space models
Author
Swindlehust, A. ; Roy, R. ; Ottersten, B. ; Kailath, T.
Author_Institution
Dept. of Electr. & Comput. Eng., Brigham Young Univ., Provo, UT, USA
Volume
40
Issue
2
fYear
1995
fDate
2/1/1995 12:00:00 AM
Firstpage
311
Lastpage
316
Abstract
A new method is presented for the identification of systems parameterized by linear state-space models. The method relies on the concept of subspace fitting, wherein an input/output data model parameterized by the state matrices is found that best fits, in the least-squares sense, the dominant subspace of the measured data. Some empirical results are included to illustrate the performance advantage of the algorithm compared to standard techniques
Keywords
Hankel matrices; MIMO systems; identification; least squares approximations; linear systems; state-space methods; MIMO systems; identification; input/output data model; least-squares; linear state-space models; state matrices; subspace fitting method; time invariant linear systems; Bibliographies; Data models; Electrons; Linear systems; MIMO; Optimal control; Sensor arrays; Signal processing algorithms; Stochastic processes; Target tracking;
fLanguage
English
Journal_Title
Automatic Control, IEEE Transactions on
Publisher
ieee
ISSN
0018-9286
Type
jour
DOI
10.1109/9.341800
Filename
341800
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