Two fusion predictors for continuous-time linear systems with different types of observations

Haryong Song, Kyung Lee and Vladimir Shin

International Journal of Systems Science, 2011, vol. 43, issue 1, 41-49

Abstract: This article proposes new fusion predictors for continuous-time linear systems with different types of observations. The fusion predictors are formed by the summation of the local Kalman estimators (filters and predictors) with matrix weights depending only on time instants. Both fusion predictors represent the optimal linear combination of an arbitrary number of local Kalman estimators and each is fused by the minimum mean square error criterion. As a consequence of the parallel structure of the proposed predictors, parallel computers can be used for their design. This article also establishes the relationship between fusion predictors. High accuracy and computational efficiency of the fusion predictors are demonstrated through several examples, including the damper harmonic oscillator motion with a multisensory environment.

Date: 2011
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721003768159 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tsysxx:v:43:y:2011:i:1:p:41-49

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TSYS20

DOI: 10.1080/00207721003768159

Access Statistics for this article

International Journal of Systems Science is currently edited by Visakan Kadirkamanathan

More articles in International Journal of Systems Science from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().