Fusion techniques using distributed Kalman filtering for detecting changes in systems

Celeste M. Belcastro, Robert Fischl, Moshe Kam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A comparison is made of the performances of two detection strategies that are based on different data fusion techniques. The strategies detect changes in a linear system. One detection strategy involves combining the estimates and error covariance matrices of distributed Kalman filters, generating a residual from the used estimates, comparing this residual to a threshold, and making a decision. The other detection strategy involves a distributed decision process in which estimates from distributed Kalman filters are used to generate distributed residuals which are compared locally to a threshold. Local decisions are made and these decisions are then fused into a global decision. The performances of each of these detection schemes are compared, and it is concluded that better performance is achieved when local decisions are made and then fused into a global decision.

Original languageEnglish (US)
Title of host publicationProceedings of the American Control Conference
Editors Anon
PublisherPubl by American Automatic Control Council
Pages2296-2298
Number of pages3
Volume3
ISBN (Print)0879425652
StatePublished - Dec 1 1991
Externally publishedYes
EventProceedings of the 1991 American Control Conference - Boston, MA, USA
Duration: Jun 26 1991Jun 28 1991

Other

OtherProceedings of the 1991 American Control Conference
CityBoston, MA, USA
Period6/26/916/28/91

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

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