Robust Inference for State-Space Models with Skewed Measurement Noise
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Details
| Original language | English |
|---|---|
| Pages (from-to) | 1898-1902 |
| Number of pages | 5 |
| Journal | IEEE Signal Processing Letters |
| Volume | 22 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - 1 Nov 2015 |
| Publication type | A1 Journal article-refereed |
Abstract
Filtering and smoothing algorithms for linear discrete-time state-space models with skewed and heavy-tailed measurement noise are presented. The algorithms use a variational Bayes approximation of the posterior distribution of models that have normal prior and skew-t-distributed measurement noise. The proposed filter and smoother are compared with conventional low-complexity alternatives in a simulated pseudorange positioning scenario. In the simulations the proposed methods achieve better accuracy than the alternative methods, the computational complexity of the filter being roughly 5 to 10 times that of the Kalman filter.
ASJC Scopus subject areas
Keywords
- Kalman filter, robust filtering, RTS smoother, skew t, skewness, t-distribution, variational Bayes
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Field of science, Statistics Finland
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