title: A star recognition method based on the Adaptive Ant Colony algorithm for star sensors.
abstract: A new star recognition method based on the Adaptive Ant Colony (AAC) algorithm has been developed to increase the star recognition speed and success rate for star sensors. This method draws circles, with the center of each one being a bright star point and the radius being a special angular distance, and uses the parallel processing ability of the AAC algorithm to calculate the angular distance of any pair of star points in the circle. The angular distance of two star points in the circle is solved as the path of the AAC algorithm, and the path optimization feature of the AAC is employed to search for the optimal (shortest) path in the circle. This optimal path is used to recognize the stellar map and enhance the recognition success rate and speed. The experimental results show that when the position error is about 50″, the identification success rate of this method is 98% while the Delaunay identification method is only 94%. The identification time of this method is up to 50 ms.
authors.affiliation: Novel Inertial Instrument and Navigation System Technology Laboratory, School of Instrumentation Science and Optoelectronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China. quanwei@buaa.edu.cn
authors.forename: Wei
authors.initials: W
authors.lastname: Quan
authors.forename: Jiancheng
authors.initials: J
authors.lastname: Fang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001256:Astronomy / Q000379:methods; D003198:Computer Simulation; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D055609:Stars, Celestial