Optimal pseudo-steady-state estimators for systems with Markovian intermittent measurements

Author

Smith, S. Craig ; Seiler, Peter

Author_Institution

Texas A&M Univ., TX, USA

Volume

4

fYear

2002

fDate

2002

Firstpage

3021

Abstract

A state estimator design is described for discrete time systems having observably intermittent measurements. A stationary Markov process is used to model probabilistic measurement losses. The stationarity of the Markov process suggests an analogous stationary estimator design related to the Markov states. A precomputable time-varying state estimator is proposed as an alternative to Kalman´s optimal time-varying estimation scheme applied to a discrete linear system with Markovian intermittent measurements. An iterative scheme to find optimal precomputed estimators is given. The results here naturally extend to Markovian jump linear systems.

Keywords

Markov processes; discrete time systems; linear systems; optimisation; probability; state estimation; time-varying systems; Kalman optimal time-varying estimation scheme; Markovian intermittent measurements; Markovian jump linear systems; analogous stationary estimator design; discrete linear system; discrete time systems; iterative scheme; observably intermittent measurements; optimal precomputed estimators; optimal pseudo-steady-state estimators; precomputable time-varying state estimator; probabilistic measurement losses; state estimator design; stationary Markov process; Covariance matrix; Error correction; Gain measurement; Kalman filters; Linear systems; Loss measurement; Markov processes; Mechanical engineering; State estimation; Time varying systems;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 2002. Proceedings of the 2002

ISSN

0743-1619

Print_ISBN

0-7803-7298-0

Type

conf

DOI

10.1109/ACC.2002.1025252

Filename

1025252