An online parameter estimator for quick convergence and time-varying linear systems

Author

Wiberg, Donald M. ; Powell, Thomas D. ; Ljungquist, Dag

Author_Institution

Dept. of Electr. Eng., California Univ., Los Angeles, CA, USA

Volume

45

Issue

10

fYear

2000

Firstpage

1854

Lastpage

1863

Abstract

A recursive algorithm called 3-OM is presented to estimate parameters and noise variances for discrete-time linear stochastic systems. The unprojected version of 3-OM is globally convergent with probability 1 to minima of the asymptotic negative log-likelihood function. 3-OM approximates the quick convergence attained by the optimal nonlinear filter used as a parameter estimator. The state-space form of 3-OM permits application to time-varying linear systems and to online tuning of a Kalman filter.

Keywords

Kalman filters; convergence; discrete time systems; linear systems; nonlinear filters; probability; recursive estimation; stochastic systems; 3-OM algorithm; asymptotic negative log-likelihood function; discrete-time linear stochastic systems; global convergence; noise variances; online parameter estimator; online tuning; optimal nonlinear filter; quick convergence; recursive algorithm; state-space form; time-varying linear systems; Convergence; Filtering; Linear systems; Moment methods; Nonlinear filters; Parameter estimation; Recursive estimation; State estimation; Stochastic systems; Time varying systems;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/TAC.2000.880986

Filename

880986