Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 22, Issue 11, Pages 1898-1902Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2015.2437456
Keywords
Kalman filter; robust filtering; RTS smoother; skew t; skewness; t-distribution; variational Bayes
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Funding
- Tampere University of Technology Graduate School
- Finnish Doctoral Programme in Computational Sciences (FICS)
- Foundation of Nokia Corporation
- Swedish research council (VR), project ETT [621-2010-4301]
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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.
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