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Discrete-time unscented Kalman filter: Comprehensive study of stochastic stability

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Titel: Discrete-time unscented Kalman filter: Comprehensive study of stochastic stability
Autoren: Dimirovski, Georgi M., Ying, Juanwei, Xu, Jiahe
Verlagsinformationen: Haifa
Publikationsjahr: 2012
Bestand: Doğuş University Institutional Repository (DSpace@Dogus) / Doğuş Üniversitesi Akademik Arşiv Sistemi
Schlagwörter: Kalman Filter, Stochastic Stability, Stability Analysis
Beschreibung: Dimirovski, Georgi M. (Dogus Author) -- Conference full title: Itzhack Y. Bar-Itzhack Memorial Symposium on Estimation, Navigation, and Spacecraft Control : Book of abstracts : Dan Panorama Hotel, Haifa, Israel, October 14-17, 2012. ; The performance of the Unscented Kalman Filter (UKF) for a class of general nonlinear stochastic discretetime systems is investigated in this paper. It is proved that the estimation error of the UKF remains bounded provided a under certain conditions are satisfied. It is further shown that the estimation error remains bounded provided the system satisfies the nonlinear observability rank condition. Furthermore, it is shown that the design of noise covariance matrix plays an important role in improving the stability of the UKF algorithm. These results are verified by simulations for a given illustrative example of an inherently nonlinear plant. ; Doğuş Üniversitesi, Technion--Israel Institute of Techlnology.
Publikationsart: conference object
Dateibeschreibung: application/pdf
Sprache: English
Relation: Itzhack Y. Bar-Itzhack Memorial Symposium on Estimation, Navigation, and Spacecraft Control; Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı; https://hdl.handle.net/11376/2899; 782; 789
Verfügbarkeit: https://hdl.handle.net/11376/2899
Rights: info:eu-repo/semantics/openAccess
Dokumentencode: edsbas.73944F33
Datenbank: BASE
Beschreibung
Abstract:Dimirovski, Georgi M. (Dogus Author) -- Conference full title: Itzhack Y. Bar-Itzhack Memorial Symposium on Estimation, Navigation, and Spacecraft Control : Book of abstracts : Dan Panorama Hotel, Haifa, Israel, October 14-17, 2012. ; The performance of the Unscented Kalman Filter (UKF) for a class of general nonlinear stochastic discretetime systems is investigated in this paper. It is proved that the estimation error of the UKF remains bounded provided a under certain conditions are satisfied. It is further shown that the estimation error remains bounded provided the system satisfies the nonlinear observability rank condition. Furthermore, it is shown that the design of noise covariance matrix plays an important role in improving the stability of the UKF algorithm. These results are verified by simulations for a given illustrative example of an inherently nonlinear plant. ; Doğuş Üniversitesi, Technion--Israel Institute of Techlnology.