State and information estimation for linear and nonlinear systems

Arthur G.O. Mutambara; M. S. Al-Haik

doi:10.1115/1.2802474

State and information estimation for linear and nonlinear systems

Arthur G.O. Mutambara, M. S. Al-Haik

FAMU-FSU College of Engineering

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

A new information space based estimation algorithm for nonlinear systems is presented. First, an outline of the algebraic equivalent of the Kalman filter, the Information -filter, is discussed. Employing the principles used in the derivation of the extended Kalman filter (EKF), the linear information space is then extended to nonlinear information space. In this way, the extended Information filter (EIF) is derived, which is the key contribution of the paper.

Original language	English
Pages (from-to)	318-319
Number of pages	2
Journal	Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME
Volume	121
Issue number	2
DOIs	https://doi.org/10.1115/1.2802474
Publication status	Published - Jun 1999
Externally published	Yes

ASJC Scopus subject areas

Control and Systems Engineering
Information Systems
Instrumentation
Mechanical Engineering
Computer Science Applications

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Cite this

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AB - A new information space based estimation algorithm for nonlinear systems is presented. First, an outline of the algebraic equivalent of the Kalman filter, the Information -filter, is discussed. Employing the principles used in the derivation of the extended Kalman filter (EKF), the linear information space is then extended to nonlinear information space. In this way, the extended Information filter (EIF) is derived, which is the key contribution of the paper.

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State and information estimation for linear and nonlinear systems

Abstract

ASJC Scopus subject areas

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