In this paper, a new algorithm called blind star identification is presented to identify the stars in night sky images without using the intrinsic parameters of the star sensor camera (focal length, principal point, and pixel size), and in lost in space mode. The accuracy and reliability of the proposed algorithm were successfully validated by using the real night sky images and Monte Carlo simulations. Accordingly, the proposed algorithm was successfully able to identify in more than 90% of the images containing more than five stars and no wrong identification was observed in the Monte Carlo simulations. On the other hand, the rotation around the optical axis, which cannot be estimated using vector observations, should be minimized in the process of designing and manufacturing the star sensor and carefully measured in the ground calibration, like as the aberration and lens distortion. Ultimately, another advantage of this algorithm is the simultaneous use of planar and interstellar angles. This advantage leads to data redundancy and greater reliability of the algorithm so that the performance of the algorithm is guaranteed under severe error conditions.

中文翻译：

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盲星识别算法

在本文中，提出了一种称为盲星识别的新算法，用于在不使用星敏感相机的内在参数（焦距、主点和像素大小）和空间丢失模式下识别夜空图像中的星星。通过使用真实夜空图像和蒙特卡罗模拟成功验证了所提出算法的准确性和可靠性。因此，所提出的算法能够成功识别超过 90% 的包含超过五颗星的图像，并且在蒙特卡罗模拟中没有观察到错误识别。另一方面，在星敏感器的设计和制造过程中，应尽量减少绕光轴的旋转，而这种旋转是用矢量观测无法估计的，并在地面校准中仔细测量，像像差和镜头畸变一样。最终，该算法的另一个优点是同时使用平面角和星际角。这一优势导致了算法的数据冗余和更高的可靠性，从而保证了算法在严重错误情况下的性能。