Variational Iterations for Smoothing with Unknown Process and Measurement Noise Covariances
2015 (English)Report (Other academic)
In this technical report, some derivations for the smoother proposed in  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  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.
 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. , 12 p.
LiTH-ISY-R, ISSN 1400-3902 ; 3086
Adaptive smoothing, variational Bayes, sensor calibration, Rauch-Tung-Striebel smoother, Kalman filtering, noise covariance
IdentifiersURN: urn:nbn:se:liu:diva-120700ISRN: LiTH-ISY-R-3086OAI: oai:DiVA.org:liu-120700DiVA: diva2:849686