A new method for the nonlinear transformation of means and covariances in filters and estimators

被引：2629
|
作者：
Julier, S [1 ]
Uhlmann, J
Durrant-Whyte, HF
机构：
[1] IDAK Ind, Jefferson City, MO USA
[2] Univ Oxford, Robot Res Grp, Oxford, England
[3] Dept Mech & Mechatron Engn, Sydney, NSW, Australia
来源：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2000年 / 45卷 / 03期
关键词：
covariance matrices; estimation; filtering; missile detection and tracking; mobile robots; nonlinear filters; prediction methods;
D O I：
10.1109/9.847726
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper describes a new approach for generalizing the Kalman filter to nonlinear systems. A set of samples are used to parameterize the mean and covariance of a (not necessarily Gaussian) probability distribution. The method yields a filter that is more accurate than an extended Kalman filter (EKF) and easier to implement than an EKF or a Gauss second-order filter. Its effectiveness is demonstrated using an example.
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页码：477 / 482
页数：6
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