Iterated statistical linear regression for Bayesian updates
MetadataShow full item record
© 2014 International Society of Information Fusion.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.
Showing items related by title, author, creator and subject.
Garcia-Fernandez, Angel; Svensson, Lennart; Morelande, M.; Sarkka, S. (2015)This paper is concerned with Gaussian approximations to the posterior probability density function (PDF) in the update step of Bayesian filtering with nonlinear measurements. In this setting, sigma-point approximations ...
Garcia Fernandez, Angel; Morelande, M.; Grajal, J. (2011)A fast algorithm to approximate the first two moments of the posterior probability density function (pdf) in nonlinear non-Gaussian Bayesian filtering is proposed. If the pdf of the measurement noise has a bounded support ...
Tseng, Chien H. (1999)The design of envelope-constrained (EC) filters is considered for the time-domain synthesis of filters for signal processing problems. The objective is to achieve minimal noise enhancement where the shape of the filter ...