Gaussian process quadratures in nonlinear sigma-point filtering and smoothing
Paper in proceedings, 2014

This paper is concerned with the use of Gaussian process regression based quadrature rules in the context of sigma-point-based nonlinear Kalman filtering and smoothing. We show how Gaussian process (i.e., Bayesian or Bayes-Hermite) quadratures can be used for numerical solving of the Gaussian integrals arising in the filters and smoothers. An interesting additional result is that with suitable selections of Hermite polynomial covariance functions the Gaussian process quadratures can be reduced to unscented transforms, spherical cubature rules, and to Gauss-Hermite rules previously proposed for approximate nonlinear Kalman filter and smoothing. Finally, the performance of the Gaussian process quadratures in this context is evaluated with numerical simulations.

Author

S. Särkkä

Aalto University

J. Hartikainen

Rocsole Ltd.

Lennart Svensson

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Lennart Svensson

Volvo Group

17th International Conference on Information Fusion, FUSION 2014; Salamanca; Spain; 7 July 2014 through 10 July 2014

6916176

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

ISBN

978-849012355-3

More information

Latest update

11/19/2018