Title :
Sigma point methods in optimal smoothing of non-linear stochastic state space models
Author :
Särkkä, Simo ; Hartikainen, Jouni
Author_Institution :
Dept. of Biomed. Eng. & Comput. Sci., Aalto Univ., Espoo, Finland
fDate :
Aug. 29 2010-Sept. 1 2010
Abstract :
In this article, we shall show how the sigma-point based approximations that have previously been used in optimal filtering can also be used in optimal smoothing. In particular, we shall consider unscented transformation, Gauss-Hermite quadrature and central differences based optimal smoothers. We briefly present the smoother equations and compare performance of different methods in simulated scenarios.
Keywords :
approximation theory; filtering theory; optimisation; state-space methods; stochastic processes; Gauss-Hermite quadrature; approximation; optimal filtering; optimal smoothing; sigma point method; stochastic state space model; Approximation methods; Computational modeling; Equations; Kalman filters; Mathematical model; Monte Carlo methods; Smoothing methods;
Conference_Titel :
Machine Learning for Signal Processing (MLSP), 2010 IEEE International Workshop on
Conference_Location :
Kittila
Print_ISBN :
978-1-4244-7875-0
Electronic_ISBN :
1551-2541
DOI :
10.1109/MLSP.2010.5589160
