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Paper I. Robust Filtering and Smoothing with Gaussian Processes. Edited version of the paper: M. P. Deisenroth, R. D. Turner, M. F. Huber, U. D. Hanebeck, and C. E. Rasmussen. Robust Filtering and Smoothing with Gaussian Processes. In IEEE Transactions on Automatic Control, vol. 57, no. 7, pages 1865-1871, July 2012

Deisenroth, Marc P.; Turner, Ryan D.; Huber, Marco F.; Rasmussen, Carl E.; Hanebeck, Uwe D.

Abstract:
We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and themeasurement function are described by non-parametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing,machine learning, robotics, and control for representing unknown system functions by posterior probability distributions. This modern way of "systemidentification" is more robust than finding point estimates of a parametric function representation. In this article, we present a principled algorithm for robust analytic smoothing in GP dynamic systems, which are increasingly used in robotics and control. ... mehr

Abstract (englisch):
We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and themeasurement function are described by non-parametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing,machine learning, robotics, and control for representing unknown system functions by posterior probability distributions. This modern way of "systemidentification" is more robust than finding point estimates of a parametric function
representation. In this article, we present a principled algorithm for robust analytic smoothing in GP dynamic systems, which are increasingly used in robotics and control. ... mehr


Volltext §
DOI: 10.5445/IR/1000046060
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Buchaufsatz
Publikationsjahr 2015
Sprache Englisch
Identifikator urn:nbn:de:swb:90-460706
KITopen-ID: 1000046070
Erschienen in Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications. Ed.: M. Huber
Verlag Karlsruher Institut für Technologie (KIT)
Seiten 402-424
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