Marginalized sigma-point filtering
Paper in proceedings, 2011

In this paper we present a method for estimating mean and covariance of a transformed Gaussian random variable. The method is based on evaluations of the transforming function and resembles the unscented transform or Gauss– Hermite integration in that aspect. However, the information provided by the evaluations is used in a Bayesian framework to form a posterior description of the transforming function. Estimates are then derived by marginalizing the function from the analytical expression of the mean and covariance. An estimation algorithm, based on the assumption that the transforming function is constructed by Hermite polynomials, is presented and compared to the cubature rule and the unscented transform. Contrary to the unscented transform, the resulting approximation of the covariance matrix are guaranteed to be positive-semidefinite and the algorithm performs much better than the cubature rule for the evaluated scenario.

Sigma point filtering

Kalman filtering

Numerical integration

Moment matching

Bayesian estimation

Author

Fredrik Sandblom

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

Lennart Svensson

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

14th International Conference on Information Fusion, Fusion 2011; Chicago, IL; 5 July 2011 through 8 July 2011

Areas of Advance

Information and Communication Technology

Transport

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

ISBN

978-145770267-9

More information

Created

10/7/2017