Variational Iterations for Smoothing with Unknown Process and Measurement Noise Covariances
2015 (English)Report (Other academic)
Abstract [en]
In this technical report, some derivations for the smoother proposed in [1] are presented. More specifically, the derivations for the cyclic iteration needed to solve the variational Bayes smoother for linear state-space models with unknownprocess and measurement noise covariances in [1] are presented. Further, the variational iterations are compared with iterations of the Expectation Maximization (EM) algorithm for smoothing linear state-space models with unknown noise covariances.
[1] T. Ardeshiri, E. Özkan, U. Orguner, and F. Gustafsson, ApproximateBayesian smoothing with unknown process and measurement noise covariances, submitted to Signal Processing Letters, 2015.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2015. , p. 12
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3086
Keywords [en]
Adaptive smoothing, variational Bayes, sensor calibration, Rauch-Tung-Striebel smoother, Kalman filtering, noise covariance
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-120700ISRN: LiTH-ISY-R-3086OAI: oai:DiVA.org:liu-120700DiVA, id: diva2:849686
2015-08-302015-08-212015-09-17Bibliographically approved