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An Autonomous Predictive Star Locations Algorithm for Star Sensor

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Abstract:

Of all current methods for measuring spacecraft attitude, the use of star sensors produces the most accurate measurements. Conventional star sensors repeat these processes with Lost-In-Space case. To advance the methods available to solve these problems, this paper presents an autonomous predictive star locations algorithm for the star sensor. The star sensor works in the star predictive star locations case after Lost-In-Space case. The predictive quaternion is predicted with previous continuous two frames quaternion at first. The predictive locations of recognized stars in star image are calculated with the predictive quaternion. The corresponding true locations of recognized stars are obtained in the threshold scan window of predictive locations of recognized stars. This results in only several pixels in star image to be scanned while the star locations are obtained from star image. Then all the unrecognized stars in FOV are searched from the star catalog stored in star sensor. The predictive locations of unrecognized stars in star image are calculated with predictive quaternion. The corresponding true locations of unrecognized stars are obtained within the threshold scan window of predictive locations of unrecognized stars. The true locations of unrecognized stars are identified with information of recognized stars. This avoids Lost-In-Space procedure to identify these unrecognized stars in FOV. The speed and the accuracy of this algorithm are successfully demonstrated in comparison with the classics star locations algorithms which use the angular velocity as provided by a rate gyro. Finally the autonomous predictive star locations algorithm was successfully demonstrated with real sky experiment in 2009 and on-orbit in 2010.

Keywords: PREDICTIVE STAR LOCATIONS; STAR SENSOR; STAR TRACKER; STAR TRACKING

Document Type: Research Article

DOI: https://doi.org/10.1166/asl.2012.2281

Publication date: 2012-03-01

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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