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

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

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 c...

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Veröffentlicht in:IEEE transactions on automatic control 2000-10, Vol.45 (10), p.1854-1863
Hauptverfasser: Wiberg, D.M., Powell, T.D., Ljungquist, D.
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Asymptotic properties
Convergence
Estimators
Filtering
Linear systems
Moment methods
Nonlinear filters
On-line systems
Online
Parameter estimation
Recursive estimation
State estimation
Stochastic systems
Time varying systems
Tuning
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Beschreibung
Zusammenfassung: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.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2000.880986