CUED Publications database

Robust filtering and smoothing with gaussian processes

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 0018-9286

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Bayesian inference filtering Gaussian processes machine learning nonlinear systems smoothing
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 11:24
Last Modified: 12 Dec 2014 19:04
DOI: 10.1109/TAC.2011.2179426