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DOKOUPIL, J. PAPEŽ, M. VÁCLAVEK, P.
Original Title
Bayesian comparison of Kalman filters with known covariance matrices
Type
conference paper
Language
English
Original Abstract
A growing-window recursive procedure for model comparison is proposed based on the Bayesian inference principle. This procedure, compared to the batch one, is capable of processing unlimited increases in the uncertainty of the initial parameter settings, which is a characteristic of Kalman type algorithms. The present paper applies the suggested procedure to assess the degree of support for the state point estimates generated by multiple Kalman filters. We investigate a case where the covariance of the measurement noise and the normalized covariance matrix of the process noise are both available.
Keywords
Kalman filter, Bayesian methods, model comparison
Authors
DOKOUPIL, J.; PAPEŽ, M.; VÁCLAVEK, P.
RIV year
2015
Released
10. 3. 2015
ISBN
978-0-7354-1287-3
Book
AIP conference proceedings
0094-243X
Periodical
Year of study
1648
State
United States of America
Pages from
1
Pages to
4
Pages count
URL
http://scitation.aip.org/content/aip/proceeding/aipcp/10.1063/1.4912383
BibTex
@inproceedings{BUT117758, author="Jakub {Dokoupil} and Milan {Papež} and Pavel {Václavek}", title="Bayesian comparison of Kalman filters with known covariance matrices", booktitle="AIP conference proceedings", year="2015", journal="AIP conference proceedings", volume="1648", pages="1--4", doi="10.1063/1.4912383", isbn="978-0-7354-1287-3", issn="0094-243X", url="http://scitation.aip.org/content/aip/proceeding/aipcp/10.1063/1.4912383" }