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Robust Inference for State-Space Models with Skewed Measurement Noise

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Details

Original languageEnglish
Pages (from-to)1898-1902
Number of pages5
JournalIEEE Signal Processing Letters
Volume22
Issue number11
DOIs
Publication statusPublished - 1 Nov 2015
Publication typeA1 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.

Keywords

  • Kalman filter, robust filtering, RTS smoother, skew t, skewness, t-distribution, variational Bayes

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