Sun Ting, Xing Fei, Bao Jingyu, Ji Songsong, Li Jin
Appl Opt. 2018 Nov 1;57(31):9239-9245. doi: 10.1364/AO.57.009239.
The star tracker plays a critical role in precision aerospace missions due to its high accuracy, absolute attitude output, and low power consumption. For an optical sensor, the problem of stray light is always an important research issue. A star energy information mining method for stray light suppression is proposed in this study. The gray-level co-occurrence matrix and k-nearest neighbor algorithm are adopted to identify the types of stray light that enter the optical system. Effective recognition of the stray light types is an important premise for the following steps. Then the parameters are optimized during background estimation. When star spots are extracted, the local differential encoding combined with Levenshtein distance filtering is conducted to eliminate the interference noise spots. The proposed algorithm can achieve accurate star spot extraction even when stray light exists in real night sky observation experiments.
星敏感器因其高精度、绝对姿态输出和低功耗，在精确航天任务中发挥着关键作用。对于光学传感器而言，杂散光问题一直是重要的研究课题。本研究提出一种用于抑制杂散光的星能量信息挖掘方法。采用灰度共生矩阵和k近邻算法来识别进入光学系统的杂散光类型。有效识别杂散光类型是后续步骤的重要前提。然后在背景估计过程中对参数进行优化。在提取星点时，进行局部差分编码并结合莱文斯坦距离滤波以消除干扰噪声点。所提算法即使在实际夜空观测实验中存在杂散光的情况下，也能实现准确的星点提取。
