Reducibility and unobservability of Markov processes: the linear system case

Author

Davis, M.H.A. ; Lasdas, V.

Author_Institution

Dept. of Electr. Eng., Imperial Coll. of Sci. Technol. & Med., London, UK

Volume

37

Issue

4

fYear

1992

fDate

4/1/1992 12:00:00 AM

Firstpage

505

Lastpage

508

Abstract

A vector Markov process will be called stochastically unobservable by the measurement process if there exists an initial distribution such that some marginal conditional distributions equal the corresponding unconditional ones. It will be called reducible if there exists an invertible transformation such that the transformed process is stochastically unobservable. Necessary and sufficient conditions are derived in the context of linear diffusions. It is also shown that reducibility can be regarded as a natural extension of the concept of estimability, defined for linear stochastic systems.<>

Keywords

Markov processes; State estimation; filtering and prediction theory; linear systems; state estimation; Markov processes; estimability; filtering; linear diffusions; linear system; marginal conditional distributions; measurement process; reducibility; state estimation; unobservability; vector Markov process; Computer aided software engineering; Covariance matrix; Linear systems; Markov processes; Observability; State estimation; Stochastic processes; Stochastic systems; Sufficient conditions; Vectors;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.126587

Filename

126587