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Paper H. Analytic Moment-based Gaussian Process Filtering. Edited version of the paper: M. P. Deisenroth, M. F. Huber, and U. D. Hanebeck. Analytic Momentbased Gaussian Process Filtering. In Proceedings of the 26th International Conference onMachine Learning (ICML),Montreal, Canada, June 2009

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

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 [7].

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:90-460698
KITopen-ID: 1000046069
Erschienen in Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications. Ed.: M. Huber
Verlag Karlsruher Institut für Technologie (KIT)
Seiten 380-400
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