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Integration of Star and Inertial Sensors for Spacecraft Attitude Determination

Published online by Cambridge University Press:  27 June 2017

Kedong Wang ,
Tongqian Zhu ,
Yujie Qin ,
Chao Zhang  and
Yong Li
Kedong Wang*
Affiliation:
(School of Astronautics, Beihang University, Beijing 100191, China)
Tongqian Zhu
Affiliation:
(School of Astronautics, Beihang University, Beijing 100191, China)
Yujie Qin
Affiliation:
(School of Astronautics, Beihang University, Beijing 100191, China)
Chao Zhang
Affiliation:
(School of Astronautics, Beihang University, Beijing 100191, China)
Yong Li
Affiliation:
(School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia)
*
(E-mail: [email protected])
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Abstract

A new integration of the acquisition and tracking modes is proposed for the integration of a Celestial Navigation System (CNS) and a Strapdown Inertial Navigation System (SINS). After the integration converges in the acquisition mode, it switches to the tracking mode. In the tracking mode, star pattern recognition is unnecessary and the integration is implemented in a cascaded filter scheme. A pre-filter is designed for each identified star and the output of the pre-filter is fused with the attitude of the SINS in the cascaded navigation filter. Both the pre-filter and the navigation filter are designed in detail. The measurements of the pre-filter are the positions on the image plane of one identified star. Both the starlight direction and its error are estimated in the pre-filter. The estimated starlight directions of all identified stars are the measurements of the navigation filter. The simulation results show that both the reliability and accuracy of the integration are improved and the integration is effective when only one star is identified in a period.

Keywords

Star sensorCelestial navigation systemInertial navigation systemKalman filter
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 6 , November 2017 , pp. 1335 - 1348
Copyright
Copyright © The Royal Institute of Navigation 2017 

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