PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Analytic Moment-based Gaussian Process Filtering
Marc Deisenroth, [Marco F] [Huber] and [Uwe D] [Hanebeck]
In: 26th International Conference on Machine Learning, 14-18 June 2009, Montreal, Canada.


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).

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5397
Deposited By:Marc Deisenroth
Deposited On:28 April 2009