A simple suboptimal Kalman filter implementation for a gyro-corrected satellite attitude determination system
Identifieur interne : 001B42 ( Main/Exploration ); précédent : 001B41; suivant : 001B43A simple suboptimal Kalman filter implementation for a gyro-corrected satellite attitude determination system
Auteurs : A. De Ruiter [Canada]Source :
- Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering [ 0954-4100 ] ; 2010.
Abstract
This article presents a simple Kalman filter implementation for correcting gyro-determined satellite attitude estimates with attitude measurements made using external sensors such as sun sensors, magnetometers, star trackers, and so on. This article first generalizes a recently developed non-linear observer for the gyro-corrected attitude determination problem. By implementing the steady-state Kalman filter in the framework of this non-linear observer, a computationally simple filter is obtained with suboptimal steady-state performance. This is important for applications where computational power is limited, such as in micro-/nano-satellite applications. Additionally, in the absence of process and measurement noise, this implementation of the Kalman filter is globally stable. The resulting filter uses constant steady-state Kalman filter gains. It is demonstrated that close-to-optimal steady-state performance is obtained.
Url:
DOI: 10.1243/09544100JAERO633
Affiliations:
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<front><div type="abstract">This article presents a simple Kalman filter implementation for correcting gyro-determined satellite attitude estimates with attitude measurements made using external sensors such as sun sensors, magnetometers, star trackers, and so on. This article first generalizes a recently developed non-linear observer for the gyro-corrected attitude determination problem. By implementing the steady-state Kalman filter in the framework of this non-linear observer, a computationally simple filter is obtained with suboptimal steady-state performance. This is important for applications where computational power is limited, such as in micro-/nano-satellite applications. Additionally, in the absence of process and measurement noise, this implementation of the Kalman filter is globally stable. The resulting filter uses constant steady-state Kalman filter gains. It is demonstrated that close-to-optimal steady-state performance is obtained.</div>
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