Attitude determination of passively magnetically stabilized nano satellites
Abstract/Contents
- Abstract
- The work in this thesis enables attitude determination to be performed on passively magnetically stabilized nano satellites using only a measurement of the solar vector. Existing state of the art methods can achieve an accuracy of one degree using a full attitude sensor suite. The methods presented in this thesis achieve an accuracy of five degrees at a reduced mass and power cost, opening up new mission opportunities for nano satellites and providing a backup capability for existing designs. Two approaches to attitude determination are presented. Firstly, a batch algorithm is described that simultaneously estimates both satellite attitude and unknown parameters in the satellite's attitude dynamics model. Secondly, an online attitude determination capability is developed by extending the commonly employed multiplicative extended Kalman filter (MEKF) for situations where no gyros are present. Both approaches were applied to actual flight data from NASA's O/OREOS and the University of Michigan's RAX-1 nano satellites and results verified against independent data.
Description
Type of resource | text |
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Form | electronic; electronic resource; remote |
Extent | 1 online resource. |
Publication date | 2013 |
Issuance | monographic |
Language | English |
Creators/Contributors
Associated with | Burton, Roland | |
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Associated with | Stanford University, Department of Aeronautics and Astronautics. | |
Primary advisor | Rock, Stephen M | |
Thesis advisor | Rock, Stephen M | |
Thesis advisor | Close, Sigrid, 1971- | |
Thesis advisor | Enge, Per | |
Advisor | Close, Sigrid, 1971- | |
Advisor | Enge, Per |
Subjects
Genre | Theses |
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Bibliographic information
Statement of responsibility | Roland Burton. |
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Note | Submitted to the Department of Aeronautics and Astronautics. |
Thesis | Thesis (Ph.D.)--Stanford University, 2013. |
Location | electronic resource |
Access conditions
- Copyright
- © 2013 by Roland Edwin Burton
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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