Star recognition method based on hybrid particle swarm optimization algorithm

A new star recognition method based on hybrid particle swarm algorithm was developed to increase recognition speed and success rate for large field high sensitivity star sensors. Firstly, several candidate recognition main stars were determined with the gray information. Then a circle was drawn with the given circle radius, and all stars in the circle were selected to constitute a characteristics data collection. Hybrid particle swarm algorithm was used for fast path optimization to construct recognition characteristics. Finally, the optimal path length was used for indexing to search matching star, and preceding three star angular distance and magnitude in the optimal path were used for matching recognition to enhance recognition speed and success rate. Experimental results show that, compared with existing recognition methods, star recognition method based on hybrid particle swarm optimization algorithm has a higher recognition rate, good real-time and robustness to noise, and it requires small star database capacity.