Student's t-Filters for Noise Scale Estimation

Filip Tronarp, Toni Karvonen, Simo Sarkka

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Article number8606947
Pages (from-to)352-356
Number of pages5
JournalIEEE Signal Processing Letters
Volume26
Issue number2
DOIs
Publication statusPublished - 2019
Externally publishedYes

Subject classification (UKÄ)

  • Probability Theory and Statistics

Free keywords

  • Kalman filtering
  • model misspecification
  • noise covariance estimation
  • student's t-filtering

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