Iterated statistical linear regression for Bayesian updates
Paper in proceeding, 2014

This paper deals with Gaussian approximations to the posterior probability density function (PDF) in Bayesian nonlinear filtering. In this setting, using sigma-point based approximations to the Kalman filter (KF) recursion is a prominent approach. In the update step, the sigma-point KF approximations are equivalent to performing the statistical linear regression (SLR) of the (nonlinear) measurement function with respect to the prior PDF. In this paper, we indicate that the SLR of the measurement function with respect to the posterior is expected to provide better results than the SLR with respect to the prior. The resulting filter is referred to as the posterior linearisation filter (PLF). In practice, the exact PLF update is intractable but can be efficiently approximated by carrying out iterated SLRs based on sigma-point approximations. On the whole, the resulting filter, the iterated PLF (IPLF), is expected to outperform all sigma-point KF approximations as demonstrated by numerical simulations.

sigma-points

rule

Bayes'

statistical linear regression

Kalman filter

nonlinear filtering

Author

Angel Garcia

Curtin University

Lennart Svensson

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

M. Morelande

University of Melbourne

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

Art. no. 6916133-
978-849012355-3 (ISBN)

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

ISBN

978-849012355-3

More information

Created

10/7/2017