Using star tracker to perform space surveillance is a focal point of research in aerospace engineering. However, autonomous attitude determination with star trackers in missions is a challenging task, because of spacecraft attitude dynamics and false stars. We present a novel star pattern recognition algorithm to resolve these problems. The algorithm defines a star pattern, called a flower code, composed of angular distances and circular angles. Then, a three-step strategy is adopted to find the correspondence of the sensor pattern and the catalog pattern, including initial lookup table match, cyclic dynamic match, and validation. A number of experiments are carried out on simulated and real star images. The simulation results show that the proposed method provides improved performance, especially on robustness against false stars. Also, the results for real star images demonstrate the reliability of the method for ground-based measurements.
The recent increase of space threats yields the idea of using the existing star trackers to perform surveillance of space
objects from space. In the missions, due to the observer attitude dynamics, smearing affects the observed stars on the
image in space surveillance. Besides, the reflecting flying space objects or debris as spurious stars affects the attitude
determination. These are devastating for most star identification algorithms in star trackers. To resolve the problems, this
paper defines a star pattern, called Flower code, which is composed of angular distances and circular angles as the
characteristics of the pivot star. The angular distances are used for initial lookup table match. Moreover, the circular
angles are used for the cyclic dynamic match between the sensor pattern and the pattern candidates from the initial
match. The focus of the results is the evaluation of the influence of the reflecting flying spacecraft or debris as spurious
stars and the attitude dynamics of the observer spacecraft, on the performance of the algorithms. A number of
experiments are carried out on simulated images. The results demonstrated that the proposed method is efficient and
robust.
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