On Gaussian Optimal Smoothing of Non-Linear State Space Models

被引：77
|
作者：
Sarkka, Simo [1 ]
Hartikainen, Jouni [1 ]
机构：
[1] Aalto Univ, FI-00076 Aalto, Finland
来源：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2010年 / 55卷 / 08期
关键词：
Bayesian smoothing; Gaussian assumed density smoothing; non-linear optimal smoothing; non-linear Rauch-Tung-Striebel smoothing; FILTERS; ALGORITHMS;
D O I：
10.1109/TAC.2010.2050017
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this note we shall present a new Gaussian approximation based framework for approximate optimal smoothing of non-linear stochastic state space models. The approximation framework can be used for efficiently solving non-linear fixed-interval, fixed-point and fixed-lag optimal smoothing problems. We shall also numerically compare accuracies of approximations, which are based on Taylor series expansion, unscented transformation, central differences and Gauss-Hermite quadrature.
引用
收藏
页码：1938 / 1941
页数：4
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