nahavandi-robustfilteringforuncertain-2008.pdf (196.98 kB)
Robust filtering for uncertain discrete-time systems with uncertain noise covariance and uncertain observations
conference contribution
posted on 2008-01-01, 00:00 authored by Shady MohamedShady Mohamed, Saeid NahavandiThe use of Kalman filtering is very common in state estimation problems. The problem with Kalman filters is that they require full prior knowledge about the system modeling. It is also assumed that all the observations are fully received. In real applications, the previous assumptions are not true all the time. It is hard to obtain the exact system model and the observations may be lost due to communication problems. In this paper, we consider the design of a robust Kalman filter for systems subject to uncertainties in the state and white noise covariances. The systems under consideration suffer from random interruptions in the measurements process. An upper bound for the estimation error covariance is proposed. The proposed upper bound is further minimized by selection of optimal filter parameters. Simulation example shows the effectiveness of the proposed filter.
History
Event
IEEE International Conference on Industrial Informatics (6th : 2008 : Daejeon, Korea)Pagination
667 - 672Publisher
IEEELocation
Daejeon, KoreaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2008-07-13End date
2008-07-16ISBN-13
9781424421718Language
engNotes
©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Publication classification
E1 Full written paper - refereedCopyright notice
2008, IEEETitle of proceedings
INDIN 2008 : Proceedings of the 6th IEEE International Conference on Industrial InformaticsUsage metrics
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