Student-t process quadratures for filtering of non-linear systems with heavy-tailed noise

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Date
2017-07
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Language
en
Pages
8
875-882
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20th International Conference on Information Fusion, Fusion 2017 - Proceedings
Abstract
The aim of this article is to design a moment transformation for Student-t distributed random variables, which is able to account for the error in the numerically computed mean. We employ Student-t process quadrature, an instance of Bayesian quadrature, which allows us to treat the integral itself as a random variable whose variance provides information about the incurred integration error. Advantage of the Student-t process quadrature over the traditional Gaussian process quadrature, is that the integral variance depends also on the function values, allowing for a more robust modelling of the integration error. The moment transform is applied in nonlinear sigma-point filtering and evaluated on two numerical examples, where it is shown to outperform the state-of-the-art moment transforms.
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Prüher, J, Tronarp, F, Karvonen, T, Särkkä, S & Straka, O 2017, Student-t process quadratures for filtering of non-linear systems with heavy-tailed noise . in 20th International Conference on Information Fusion, Fusion 2017 - Proceedings . IEEE, pp. 875-882, International Conference on Information Fusion, Xian, China, 10/07/2017 . https://doi.org/10.23919/ICIF.2017.8009742