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Continuous-time norm-constrained Kalman filtering

delete2014-10-01
delete27
PRE
AI
J
James Richard Forbes *
D
de Ruiter, Anton H. J.
D
David Evan Zlotnik
DOI:10.1016/j.automatica.2014.08.007delete
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摘要

摘要

En 中文
This paper considers continuous-time state estimation when part of the state estimate or the entire state estimate is norm-constrained. In the former case continuous-time state estimation is considered by posing a constrained optimization problem. The optimization problem can be broken up into two separate optimization problems, one which solves for the optimal observer gain associated with the unconstrained state estimates, while the other solves for the optimal observer gain associated with the constrained state estimates. The optimal constrained state estimate is found by projecting the time derivative of an unconstrained estimate onto the tangent space associated with the norm constraint. The special case where the entire state estimate is norm-constrained is briefly discussed. The utility of the filtering results developed are highlighted through a spacecraft attitude estimation example. Numerical simulation results are included. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
State estimation
Constraints
Kalman filters
Extended Kalman filters
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期刊

Automatica 封面图
Automatica
IF:
5.9
论文数:
1.2W
被引数:
5.2W

机构

U
University of Michigan
学者数:
6.4W
论文数: 5.3W
被引数: 124
U
university of michigan system
学者数:
9.1W
论文数: 8.6W
被引数: 133
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