An unknown input extended Kalman filter for nonlinear stochastic systems

被引：8

作者：

Meyer, Luc ^{[1
]}

Ichalal, Dalil ^{[2
]}

Vigneron, Vincent ^{[2
]}

机构：

[1] Univ Paris Saclay, Onera, Chemin Huniere, F-91120 Palaiseau, France

[2] Univ Paris Saclay, Univ Evry, IBISC Lab, 43 Rue Pelvoux, F-91080 Courcouronnes, France

来源：

EUROPEAN JOURNAL OF CONTROL | 2020年 / 56卷

关键词：

Unknown inputs; Extended Kalman filter; State estimation; Aerospace navigation; MINIMUM-VARIANCE ESTIMATION;

D O I：

10.1016/j.ejcon.2020.01.009

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 proposes an Unknown Input Extended Kalman Filter (UIEKF) for stochastic non linear systems affected by Gaussian noises and Unknown Inputs (UI) in both state and measurement equations. The proposed approach is based on a total decoupling of the UI, in spite of the presence of nonlinearities in the measurement equation. The UI is decoupled under some structural constraints, and a state estimator is provided. Besides an UI estimator is also proposed. Finally, the proposed filter is applied on a classical navigation example, illustrating its advantages. (c) 2020 European Control Association. Published by Elsevier Ltd. All rights reserved.

引用

页码：51 / 61

页数：11

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