View in EDS

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

Saved in:
Bibliographic Details
Title: Discrete-time unscented Kalman filter: Comprehensive study of stochastic stability
Authors: Dimirovski, Georgi M., Ying, Juanwei, Xu, Jiahe
Publisher Information: Haifa
Publication Year: 2012
Collection: Doğuş University Institutional Repository (DSpace@Dogus) / Doğuş Üniversitesi Akademik Arşiv Sistemi
Subject Terms: Kalman Filter, Stochastic Stability, Stability Analysis
Description: 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.
Document Type: conference object
File Description: application/pdf
Language: 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
Availability: https://hdl.handle.net/11376/2899
Rights: info:eu-repo/semantics/openAccess
Accession Number: edsbas.73944F33
Database: BASE
Description
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.