Student's t-Filters for Noise Scale Estimation
Research output: Contribution to journal › Article › Scientific › peer-review
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.
|Number of pages||5|
|Journal||IEEE Signal Processing Letters|
|Publication status||Published - 1 Feb 2019|
|MoE publication type||A1 Journal article-refereed|
- Kalman, Kalman filtering, Variances, model mis-specification, noise covariance estimation, student's t-filtering