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Analytic Moment-based Gaussian Process Filtering

Deisenroth, Marc P.; Huber, Marco F. 1; Hanebeck, Uwe D. 1
1 Karlsruher Institut für Technologie (KIT)


We propose an analytic moment-based filter for nonlinear stochastic dynamic systems modeled by Gaussian processes. Exact expressions for the expected value and the covariance matrix are provided for both the prediction step and the filter step, where an additional Gaussian assumption is exploited in the latter case. Our filter does not require further approximations. In particular, it avoids finite-sample approximations. We compare the filter to a variety of Gaussian filters, that is, the EKF, the UKF, and the recent GP-UKF proposed by Ko et al. (2007).

Volltext §
DOI: 10.5445/IR/1000034958
Zitationen: 69
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Informatik – Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2009
Sprache Englisch
Identifikator ISBN: 978-1-60558-516-1
KITopen-ID: 1000034958
Erschienen in ICML '09. Proceedings of the 26th Annual International Conference on Machine Learning
Verlag Association for Computing Machinery (ACM)
Seiten 225-232
Nachgewiesen in Scopus
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