Stochastic reconstructibility and estimability

Author

Liu, Andrew R. ; Bitmead, Robert R.

Author_Institution

Dept. of Mech. & Aerosp. Eng., Univ. of California, San Diego, La Jolla, CA, USA

fYear

2009

fDate

15-18 Dec. 2009

Firstpage

2339

Lastpage

2344

Abstract

The connections between the linear systems concepts of deterministic reconstructibility, stochastic reconstructibility, and estimability are described. Deterministic reconstructibility is shown to be a special case of stochastic reconstructibility, linking the notion of uncertainty reduction in terms of covariances to the deterministic definition. Examples are given to demonstrate properties of each definition and to compare them. Finally, a nonlinear extension of stochastic reconstructibility and a summary of its analysis is given to show the adaptability of our definition to more general cases. This work contributes to the larger study on concepts and applications of stochastic observability.

Keywords

estimation theory; linear systems; nonlinear control systems; observability; stochastic systems; deterministic reconstructibility; linear systems; nonlinear extension; stochastic estimability; stochastic observability application; stochastic reconstructibility; Aerospace engineering; Filtering; Kalman filters; Linear systems; Observability; Stochastic processes; Stochastic systems; Testing; Uncertainty; Yield estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on

Conference_Location

Shanghai

ISSN

0191-2216

Print_ISBN

978-1-4244-3871-6

Electronic_ISBN

0191-2216

Type

conf

DOI

10.1109/CDC.2009.5400425

Filename

5400425