DocumentCode :
3031564
Title :
A new form of the extended Kalman filter for parameter estimation in linear systems
Author :
Panuska, V.
Author_Institution :
Concordia University, Montreal, Quebec, Canada
Volume :
2
fYear :
1979
fDate :
12-14 Dec. 1979
Firstpage :
927
Lastpage :
932
Abstract :
A well-known method for estimation of parameters in linear systems with correlated noise is the extended Kalman filter where the unknown parameters are estimated as a part of an enlarged state vector. To avoid the computational burden in determining the state estimates when only the parameter estimates are required, a new simple form of the extended Kalman filter, where the state consists only of the parameters to be estimated, is proposed. The algorithm is based on the inclusion of the computed residuals in the observation matrix of a state representation of the system, an idea first introduced in the so-called extended least squares or Panuska´s method. Convergence properties of the proposed algorithm are studied and the algorithm is shown to perform a gradient-based minimization of the maximum likelihood loss function. Some special cases of the algorithm are also discussed and an extension to an estimator for randomly varying parameters is outlined.
Keywords :
Convergence; Instruments; Kalman filters; Linear systems; Nonlinear equations; Nonlinear filters; Parameter estimation; State estimation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control including the Symposium on Adaptive Processes, 1979 18th IEEE Conference on
Conference_Location :
Fort Lauderdale, FL, USA
Type :
conf
DOI :
10.1109/CDC.1979.270083
Filename :
4046563
Link To Document :
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