Student's t-Filters for Noise Scale Estimation

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2019-02-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
5
352-356
Series
IEEE Signal Processing Letters, Volume 26, issue 2
Abstract
In this letter, we analyze certain student's t-filters for linear Gaussian systems with misspecified noise covariances. It is shown that under appropriate conditions, the filter both estimates the state and re-scales the noise covariance matrices in a Kullback-Leibler optimal fashion. If the noise covariances are misscaled by a common scalar, then the re-scaling is asymptotically exact. We also compare the student's t.-filter scale estimates to the maximum-likelihood estimates. Simulations demonstrating the results on the Wiener velocity model are provided.
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Keywords
Kalman, Kalman filtering, Variances, model mis-specification, noise covariance estimation, student's t-filtering
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Citation
Tronarp , F , Karvonen , T & Särkkä , S 2019 , ' Student's t-Filters for Noise Scale Estimation ' , IEEE Signal Processing Letters , vol. 26 , no. 2 , 8606947 , pp. 352-356 . https://doi.org/10.1109/LSP.2018.2889440