Kalman filtering with uncertain process and measurement noise covariances with application to state estimation in sensor networks
2007 (English)In: PROCEEDINGS OF THE 2007 IEEE CONFERENCE ON CONTROL APPLICATIONS, VOLS 1-3: IEEE International Conference on Control Applications, IEEE , 2007, p. 687-692Conference paper, Published paper (Refereed)
Abstract [en]
Distributed state estimation under uncertain process and measurement noise covariances is considered. An algorithm based on sensor fusion using Kalman filtering is investigated. It is shown that if the covariances are decomposed into a known nominal covariance plus an uncertainty term, then the uncertainty of the actual estimation error covariance for the Kalman filter grows linearly with the size of the uncertainty term. This result is extended to the sensor fusion scheme to give an upper bound on the actual error covariance for the fused state estimate. Examples are provided to illustrate how the theory can be applied in practice.
Place, publisher, year, edition, pages
IEEE , 2007. p. 687-692
Series
IEEE International Conference on Control Applications, ISSN 1085-1992
Keywords [en]
Communication system control, Control systems, Estimation error, Filtering, Kalman filters, Measurement uncertainty, Noise measurement, Sensor fusion, Sensor systems, State estimation
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-81213DOI: 10.1109/CCA.2007.4389369ISI: 000253024000118Scopus ID: 2-s2.0-43049167430ISBN: 978-1-4244-0442-1 (print)OAI: oai:DiVA.org:kth-81213DiVA, id: diva2:497242
Conference
IEEE Conference on Control Applications, Singapore,
Note
© 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
QC 20120216
2012-02-162012-02-102022-06-24Bibliographically approved