Robust Extended Kalman Filtering for Nonlinear Systems With Stochastic Uncertainties

被引：144
|
作者：
Kai, Xiong [1 ]
Wei, Chunling [1 ]
Liu, Liangdong [1 ]
机构：
[1] Beijing Inst Control Engn, Natl Lab Space Intelligent Control, Beijing 100080, Peoples R China
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS | 2010年 / 40卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Nonlinear estimation; nonlinear uncertain system; pulsar positioning system; robust extended Kalman filter (REKF); X-RAY PULSARS; H-INFINITY; NAVIGATION; TRACKING; STATE;
D O I：
10.1109/TSMCA.2009.2034836
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this correspondence paper, a novel robust extended Kalman filter (REKF) for discrete-time nonlinear systems with stochastic uncertainties is proposed. The filter is derived to guarantee an optimized upper bound on the state estimation error covariance despite the model uncertainties as well as the linearization errors. Further analysis shows that the proposed filter has robustness against process noises, measurement noises, and model uncertainties. In addition, the new method is applied in an X-ray pulsar positioning system. It is illustrated through numerical simulations that the REKF is more effective than the standard extended Kalman filter and the extended robust H(infinity) filter.
引用
收藏
页码：399 / 405
页数：7
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