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

页码：1938 / 1941

页数：4

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