Computer Science – Performance
Scientific paper
Feb 2011
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011opten..50b3604l&link_type=abstract
Optical Engineering, Volume 50, Issue 2, pp. 023604-023604-8 (2011).
Computer Science
Performance
1
Astronomical Techniques, Calibration, Iterative Methods, Kalman Filters, Least Squares Approximations, Star Trackers, Stars, Imaging Detectors And Sensors, Standards And Calibration, Observation And Data Reduction Techniques, Computer Modeling And Simulation
Scientific paper
We have developed a calibration approach for a star tracker camera. A modified version of the least-squares iteration algorithm combining Kalman filter is put forward, which allows for autonomous on-orbit calibration of the star tracker camera even with nonlinear camera distortions. In the calibration approach, the optimal principal point and focal length are achieved at first via the modified algorithm, and then the high-order focal-plane distortions are estimated using the solution of the first step. To validate this proposed calibration approach, the real star catalog and synthetic attitude data are adopted to test its performance. The test results have demonstrated the proposed approach performs well in terms of accuracy, robustness, and performance. It can satisfy the autonomous on-orbit calibration of the star tracker camera.
Jia Hui
Li Xiu-Jian
Liu Hai-Bo
Tan Ji-Chun
Wang Jiong-Qi
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