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

被引：2630

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.

引用

页码：477 / 482

页数：6

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