Robust Filtering and Smoothing with Gaussian Processes

Details Robust Filtering and Smoothing with Gaussian Processes Robust Filtering and Smoothing with Gaussian Processes

2012-03-20

arxiv.org/abs/1203.4345v1

Computer Science

Systems and Control

7 pages, 1 figure, draft version of paper accepted at IEEE Transactions on Automatic Control

Scientific paper

10.1109/TAC.2011.2179426

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. In this article, we present a principled algorithm for robust analytic smoothing in GP dynamic systems, which are increasingly used in robotics and control. Our numerical evaluations demonstrate the robustness of the proposed approach in situations where other state-of-the-art Gaussian filters and smoothers can fail.

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