NDLI: Bayesian quadrature in nonlinear filtering

Content Provider	IEEE Xplore Digital Library
Author	Pruher, Jakub Simandl, Miroslav
Copyright Year	2015
Description	Author affiliation: European Centre of Excellence - New Technologies for the Information Society, Faculty of Applied Sciences, University of West Bohemia, Univerzitní 18, Pilsen, Czech Republic (Pruher, Jakub; Simandl, Miroslav)
Abstract	The paper deals with the state estimation of nonlinear stochastic discrete-time systems by means of quadrature-based filtering algorithms. The algorithms use quadrature to approximate the moments given by integrals. The aim is at evaluation of the integral by Bayesian quadrature. The Bayesian quadrature perceives the integral itself as a random variable, on which inference is to be performed by conditioning on the function evaluations. Advantage of this approach is that in addition to the value of the integral, the variance of the integral is also obtained. In this paper, we improve estimation of covariances in quadrature-based filtering algorithms by taking into account the integral variance. The proposed modifications are applied to the Gauss-Hermite Kalman filter and the unscented Kalman filter algorithms. Finally, the performance of the modified filters is compared with the unmodified versions in numerical simulations. The modified versions of the filters exhibit significantly improved estimate credibility and a comparable root-mean-square error.
Starting Page	380
Ending Page	387
File Size	139224
Page Count	8
File Format	PDF
ISBN	9789897581496
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-21
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	SCITEPRESS
Subject Keyword	Nonlinear Filtering Uncertainty Bayesian Quadrature Gaussian Process Approximation algorithms Bayes methods Random variables Kalman filters Approximation methods Kernel
Content Type	Text
Resource Type	Article

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