Abstract
Star sensors make use of astronomical information in stars to determine attitude for spacecrafts by star image recognition. For low-cost star sensors with small field of view, fusion of observed images from multiple fields of view is performed and a novel recognition algorithm based on path optimization by randomly distributed ant colony is proposed. According to pheromone intensity, the ant colony can autonomously figure out a close optimal path without starting or ending point, rather than certifying a starting point first. Feature patterns extracted from the optimal path in guiding template and observed image after fusion are compared to perform star recognition. By the proposed algorithm, starting point for path optimization has no influence on the extracted feature pattern. Thus the star recognition rate is improved due to the higher stability of the extracted pattern. Simulations indicate that the algorithm improves recognition accuracy and robustness against noise for sensors with multiple fields of view.
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