Deisenroth, MP and Turner, RD and Huber, MF and Hanebeck, UD and Rasmussen, CE (2012) Robust filtering and smoothing with gaussian processes. IEEE Transactions on Automatic Control, 57. pp. 1865-1871. ISSN 0018-9286Full text not available from this repository.
We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing, machine learning, robotics, and control for representing unknown system functions by posterior probability distributions. This modern way of system identification is more robust than finding point estimates of a parametric function representation. Our principled filtering/smoothing approach for GP dynamic systems is based on analytic moment matching in the context of the forward-backward algorithm. Our numerical evaluations demonstrate the robustness of the proposed approach in situations where other state-of-the-art Gaussian filters and smoothers can fail. © 2011 IEEE.
|Uncontrolled Keywords:||Bayesian inference filtering Gaussian processes machine learning nonlinear systems smoothing|
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Unnamed user with email email@example.com|
|Date Deposited:||16 Jul 2015 13:22|
|Last Modified:||10 Oct 2015 01:08|