Paper
21 May 2013 Long-integration star tracker image processing for combined attitude-attitude rate estimation
Brad Sease, Ryan Koglin, Brien Flewelling
Author Affiliations +
Abstract
A method is proposed for extracting attitude data from streaked star images using traditional image processing methods. The process enables collection of both attitude and angular velocity estimates from streaking star images, where traditional star identification methods would produce poor results. Star streak endpoints are localized as "corner-like" portions of a streak. A vector cross-product based method is developed to produce a proper grouping in time for endpoints. Star identification and single point attitude determination methods can be applied to each set of endpoints, retrieving two sets of data from a single image. Monte Carlo results are presented, and the implications of the results are discussed. Multiple corner detection methods are considered and compared. Future work needed to mature the process is discussed. Results indicate an endpoint detection accuracy of less than a tenth of a pixel for a camera angle to the rotation axis of greater than 20 degrees and streak lengths up to 40% of the field of view.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Brad Sease, Ryan Koglin, and Brien Flewelling "Long-integration star tracker image processing for combined attitude-attitude rate estimation", Proc. SPIE 8739, Sensors and Systems for Space Applications VI, 87390Y (21 May 2013); https://doi.org/10.1117/12.2020004
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CITATIONS
Cited by 4 scholarly publications.
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KEYWORDS
Stars

Cameras

Image processing

Monte Carlo methods

Sensors

Space operations

Electroluminescent displays

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