Tracking randomly varying parameters-analysis of a standard algorithm

Author

Guo, L. ; Xia, L. ; Moore, J.B.

Author_Institution

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

fYear

1988

fDate

7-9 Dec 1988

Firstpage

1514

Abstract

Concerns the use of the Kalman filter as an algorithm for the parameter estimation of a linear stochastic system where the unknown parameters are randomly time-varying and can be represented by a Markov model. The authors develop asymptotic properties of the algorithm. In particular they establish the tracking error bounds for the unknown parameters. It is shown that the Kalman filter has quite reasonable tracking properties even in the non-Gaussian case when it is not an optimal filter. If the parameters are generated from a stable model, it is found that there is no restriction on the regressors to achieve tracking error bounds. The bounds obtained have application for adaptive controller analysis

Keywords

Kalman filters; Markov processes; filtering and prediction theory; parameter estimation; stochastic systems; time-varying systems; Kalman filter; Markov model; adaptive controller analysis; asymptotic properties; linear system; parameter estimation; randomly varying parameters; regressors; stochastic system; time-varying parameters; tracking properties; Algorithm design and analysis; Australia; Convergence; Estimation error; Gaussian noise; Least squares methods; Stochastic processes; Stochastic systems; Systems engineering and theory; Time varying systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1988., Proceedings of the 27th IEEE Conference on

Conference_Location

Austin, TX

Type

conf

DOI

10.1109/CDC.1988.194579

Filename

194579