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**Harvard**

García-Fernández, A., Svensson, L. och Morelande, M. (2014) *Iterated statistical linear regression for Bayesian updates*.

** BibTeX **

@conference{

García-Fernández2014,

author={García-Fernández, A.F. and Svensson, Lennart and Morelande, M.R.},

title={Iterated statistical linear regression for Bayesian updates},

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

isbn={978-849012355-3},

pages={Art. no. 6916133},

abstract={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.},

year={2014},

keywords={Bayes'; Kalman filter; nonlinear filtering; rule; sigma-points; statistical linear regression},

}

** RefWorks **

RT Conference Proceedings

SR Electronic

ID 207097

A1 García-Fernández, A.F.

A1 Svensson, Lennart

A1 Morelande, M.R.

T1 Iterated statistical linear regression for Bayesian updates

YR 2014

T2 17th International Conference on Information Fusion, FUSION 2014; Salamanca; Spain; 7 July 2014 through 10 July 2014

SN 978-849012355-3

AB 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.

LA eng

LK http://publications.lib.chalmers.se/records/fulltext/207097/local_207097.pdf

OL 30