A fast star map simulation method based on genetic algorithm optimization of variable star point number

• Published in: Proceedings of SPIE, the international society for optical engineering Vol. 13561; pp. 135611C - 135611C-6
• Main Authors: Zhang, Jiabao, Li, Yan, Liu, He, Zhong, Chongxin, Liang, Jiaxin
• Format: Conference Proceeding
• Language: English
• Published: SPIE 02.04.2025
• ISBN: 9781510689299; 151068929X
• ISSN: 0277-786X
• DOI: 10.1117/12.3058489

In view of the serious decline of simulation speed caused by the complexity of dynamic trailing star images in star map simulation, a fast star map simulation method based on genetic algorithm to optimize variable star number (GA-VSPN) is proposed. This method takes the angular velocity of the star and the local star image continuity of the simulated star map as constraints, and uses GA to optimize the number of sampling points of the star, so as to obtain a continuous model star map with the lowest possible number of sampling points. The effectiveness of this method is verified by a group of single star simulation experiments and a group of multi star simulation experiments. The experimental results show that compared with the traditional star map simulation method with fixed star number, the average calculation speed of GA-VSPN method in the two groups of experiments is increased by 58.77% and 40.33%, respectively, and the average calculation speed is increased by 49.55%. The proposed method effectively improves the speed of star map simulation, reduces the dependence on computing resources, and ensures the real-time performance of spacecraft navigation and positioning.