Information based estimation for both linear and nonlinear systems

Arthur G.O. Mutambara

Research output: Contribution to journalConference articlepeer-review

21 Citations (Scopus)


A new estimation algorithm is derived and appraised for nonlinear systems. The notion and measures of information are defined and this leads to a discussion of the algebraic equivalent of the Kalman filter, the linear information filter. Examples of dynamic systems are simulated to illustrate the algebraic equivalence of the Kalman and information filters. The benefits of information space are also explored. Estimation for systems with nonlinearities is then considered starting with the extended Kalman filter. Linear information space is extended to nonlinear information space by deriving the extended information filter. The advantages of the extended information filter over the extended Kalman filter are demonstrated for systems involving both nonlinear state evolution and nonlinear observations.

Original languageEnglish
Pages (from-to)1329-1333
Number of pages5
JournalProceedings of the American Control Conference
Publication statusPublished - 1999
Externally publishedYes
EventProceedings of the 1999 American Control Conference (99ACC) - San Diego, CA, USA
Duration: 2 Jun 19994 Jun 1999

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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