State and information space estimation: a comparison

Author

Mutambara, Arthur G O ; Al-Haik, Marwan S Y

Author_Institution

Dept. of Mech. Eng., FAMU-FSU, Tallahassee, FL, USA

Volume

4

fYear

1997

fDate

4-6 Jun 1997

Firstpage

2374

Abstract

State and information space estimation methods used in both linear and nonlinear systems are compared. The (linear) information filter is introduced as an algebraic equivalent to the Kalman filter. Linear information space is extended to nonlinear information space by outlining the extended information filter. The algebraic equivalence of this filter to the extended Kalman filter and the benefits of nonlinear information space are illustrated by considering a system involving both nonlinear state evolution and nonlinear observations

Keywords

Kalman filters; nonlinear filters; nonlinear systems; observers; sensor fusion; state-space methods; algebraic equivalence; extended Kalman filter; extended information filter; information space estimation; linear information filter; linear information space; nonlinear information space; nonlinear observations; nonlinear state evolution; state space estimation; Information filtering; Information filters; Integrated circuit modeling; Integrated circuit noise; Linear systems; Nonlinear equations; Nonlinear systems; Recursive estimation; State estimation; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 1997. Proceedings of the 1997

Conference_Location

Albuquerque, NM

ISSN

0743-1619

Print_ISBN

0-7803-3832-4

Type

conf

DOI

10.1109/ACC.1997.609109

Filename

609109