Journal
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
Volume 57, Issue 7, Pages 1865-1871Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2011.2179426
Keywords
Bayesian inference; filtering; Gaussian processes; machine learning; nonlinear systems; smoothing
Funding
- ONR MURI [N00014-09-1-1052]
- Intel Labs
- DataPath, Inc.
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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.
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