Estimating time-varying parameters by the Kalman filter based algorithm: stability and convergence

Author

Guo, Lei

Author_Institution

Dept. of Syst. Eng., Australian Nat. Univ., Canberra, ACT, Australia

Volume

35

Issue

2

fYear

1990

fDate

2/1/1990 12:00:00 AM

Firstpage

141

Lastpage

147

Abstract

Convergence and stability properties of the Kalman filter-based parameter estimator are established for linear stochastic time-varying regression models. The main features are: both the variances and sample path averages of the parameter tracking error are shown to be bounded; the regression vector includes both stochastic and deterministic signals, and no assumptions of stationarity or independence are requires; and the unknown parameters are only assumed to have bounded variations in an average sense

Keywords

Kalman filters; convergence; parameter estimation; stability; statistics; Kalman filter-based parameter estimator; bounded variations; convergence; deterministic signals; linear stochastic time-varying regression models; parameter tracking error; regression vector; sample path averages; stability; stochastic signals; time-varying parameters; variances; Bayesian methods; Convergence; Linear regression; Parameter estimation; Signal processing algorithms; Stability; Stochastic processes; Stochastic resonance; Systems engineering and theory; Vectors;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.45169

Filename

45169