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Analytic Moment-based Gaussian Process Filtering

Deisenroth, Marc P.; Huber, Marco F.; Hanebeck, Uwe D.

Abstract:
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).


Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2009
Sprache Englisch
Identifikator ISBN: 978-1-60558-516-1
URN: urn:nbn:de:swb:90-349589
KITopen ID: 1000034958
Erschienen in ICML '09. Proceedings of the 26th Annual International Conference on Machine Learning
Verlag ACM, New York
Seiten 225-232
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