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).