On the relation between Gaussian process quadratures and sigma-point methods
Journal article, 2016

This article is concerned with Gaussian process quadratures, which are numerical integration methods based on Gaussian process regression methods, and sigma-point methods, which are used in advanced non-linear Kalman filtering and smoothing algorithms. We show that many sigma-point methods can be interpreted as Gaussian process quadrature based methods with suitably selected covariance functions. We show that this interpretation also extends to more general multivariate Gauss-Hermite integration methods and related spherical cubature rules. Additionally, we discuss different criteria for selecting the sigma-point locations: exactness of the integrals of multivariate polynomials up to a given order, minimum average error, and quasi-random point sets. The performance of the different methods is tested in numerical experiments.


A. S. Arkkä

Aalto University

J. Hartikainen

Rocsole Ltd.

Lennart Svensson

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering, Signal Processing

Fredrik Sandblom

Volvo Group

Journal of Advances in Information Fusion

1557-6418 (ISSN)

Vol. 11 1 31-46

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

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7/1/2021 1