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Star Recognition Based on Path Optimization in Star Sensor with Multiple Fields of View 时间:2018-10-03 22:35   来源:未知   作者:admin   点击: 次 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.
1. Introduction
  Astronomical attitude determination for spacecrafts is the course of obtaining celestial body information through celestial body sensors and calculating attitudes of spacecrafts. Among various celestial body sensors applied for spacecraft attitude determination, star sensors provide the optimal accuracy. Star sensor’s working patterns consist of star tracking mode and all-sky star image recognition. For the latter mode, star sensors work independently of all the other outside information. This raises the requirement of fast star recognition and spacecraft attitude reconstruction in the case of attitude losing or upheaval.
  Usually autonomous guidance of star sensors with small FOV is restricted by insufficient guidance star quantity. In order to break the restriction, celestial guidance technology for multi-FOV star sensors is researched and improved.
  In the field of recognition for multi-FOV star sensors, Ho et al. [1] propose an identification algorithm which combines recognition using extended images and recognition using combined images. In their work, the stars in the image from the two trackers are singly identified, then the rest of the stars or the stars in the extended images are identified using the stars identified previously. Li et al. [2] come up with a novel recognition algorithm for double FOV star sensors. Firstly, the star coordinates in the image system of each FOV are singly obtained. Secondly the star coordinates of the first FOV are transformed into the image space system of the second FOV. Then, in the image space system of the second FOV, all the observed stars are sorted and their pattern characteristics are extracted for recognition.
  As for improvement on star recognition algorithms, Hernandez et al. [3] introduce recognition algorithm based on polygons with similarity invariants. They create polygons using neighbouring stars as vertices and map each polygon to a complex number. The number is used as index for match. Quan et al. [4] develop recognition method based on the adaptive ant colony algorithm. This method draws circles, with the centre of each one being a bright star point and the radius being a special angular distance. For the stars in each star point set, the angular distance of any pair of star points in the star sets is calculated and the star point closest to the centre of the star point set is taken as the starting point. Then the optimal path of this star point set beginning from the starting point is retrieved with the AAC. The feature extracted from the optimal path is used for recognition. Li et al. [5] propose an improved triangle algorithm for all-sky recognition. They select guide stars according to celestial subblocks and optimize observation triangle selection. Then they use angular distances between stars for matching identification. Besides, novel recognition algorithms and database formation methods [6–18] are developed.