Shen Chao, Ma Caiwen, Gao Wei, Wang Yuanbo
Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences, Xi'an 710119, China.
Sensors (Basel). 2024 Mar 4;24(5):1661. doi: 10.3390/s24051661.
In order to solve the star identification problem in the lost space mode for scientific cameras with small fields of view and higher instruction magnitudes, this paper proposes a star identification algorithm based on a hybrid grid pattern. The application of a hybrid pattern generated by multi-calibration stars in the initial matching enables the position distribution features of neighboring stars around the main star to be more comprehensively described and avoids the interference of position noise and magnitude noise as much as possible. Moreover, calibration star filtering is adopted to eliminate incorrect candidates and pick the true matched navigation star from candidate stars in the initial match. Then, the reference star image is utilized to efficiently verify and determine the final identification results of the algorithm via the nearest principle. The performance of the proposed algorithm in simulation experiments shows that, when the position noise is 2 pixels, the identification rate of the algorithm is 96.43%, which is higher than that of the optimized grid algorithm by 2.21% and the grid algorithm by 4.05%; when the magnitude noise is 0.3 mag, the star identification rate of the algorithm is 96.45%, which is superior to the optimized grid algorithm by 2.03% and to the grid algorithm by 3.82%. In addition, in the actual star image test, star magnitude values of ≤12 mag can be successfully identified using the proposed algorithm.
为解决小视场、高指令量科学相机在失锁空间模式下的恒星识别问题，本文提出一种基于混合网格模式的恒星识别算法。在初始匹配中应用由多颗校准恒星生成的混合模式，能够更全面地描述主星周围邻近恒星的位置分布特征，并尽可能避免位置噪声和星等噪声的干扰。此外，采用校准恒星滤波来消除错误候选，从初始匹配中的候选恒星中挑选出真正匹配的导航恒星。然后，利用参考恒星图像通过最近原则高效验证并确定算法的最终识别结果。仿真实验中该算法的性能表明，当位置噪声为2像素时，算法的识别率为96.43%，比优化网格算法高2.21%，比网格算法高4.05%；当星等噪声为0.3星等时，算法的恒星识别率为96.45%，优于优化网格算法2.03%，优于网格算法3.82%。此外，在实际恒星图像测试中，使用该算法可以成功识别星等≤12星等的恒星。
