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, 687-692 p.Conference paper (Refereed)
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. 687-692 p.
, IEEE International Conference on Control Applications, ISSN 1085-1992
Communication system control, Control systems, Estimation error, Filtering, Kalman filters, Measurement uncertainty, Noise measurement, Sensor fusion, Sensor systems, State estimation
IdentifiersURN: urn:nbn:se:kth:diva-81213DOI: 10.1109/CCA.2007.4389369ISI: 000253024000118ScopusID: 2-s2.0-43049167430ISBN: 978-1-4244-0442-1OAI: oai:DiVA.org:kth-81213DiVA: diva2:497242
IEEE Conference on Control Applications, Singapore,
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QC 201202162012-02-162012-02-102012-02-16Bibliographically approved