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.
Original language | English |
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Article number | 8606947 |
Pages (from-to) | 352-356 |
Number of pages | 5 |
Journal | IEEE Signal Processing Letters |
Volume | 26 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2019 |
Externally published | Yes |
Subject classification (UKÄ)
- Probability Theory and Statistics
Free keywords
- Kalman filtering
- model misspecification
- noise covariance estimation
- student's t-filtering