基于机器学习的大视场星敏感器畸变在轨标定技术

随着遥感卫星在轨任务复杂性的不断提升，对卫星精度的要求也不断提高。星敏感器是星上精度最高态敏感器，因而其在轨标定是提高精度的有效手段。由于大视场星敏感器的镜头畸变复杂，目前广泛采用的基于星对角距的最小二乘法存在一定局限性。因此提出一种基于机器学习的星敏感器在轨标定算法，该方法结合机器学习预测建模思想，通过构造特征建立镜头畸变模型，并结合主成分分析方法进行冗余特征的消除，最后从星角距和模型泛化能力两方面对标定效果进行评价。仿真结果表明:算法对镜头畸变程度较大的星敏感器有良好的校正效果，标定精度始终能保持在0.8内，与目前几种主流算法相比，具有精度高，鲁棒性好等优点。

Machine learning based on-orbit distortion calibration technique for large field-of-view star tracker

As on-orbit missions of satellites are increasingly complicated, the demand for satellites' attitude determination accuracy is becoming higher. Star sensor is the most accurate attitude sensor on the satellite, so calibration of the star sensor is critical to further improvements of satellites' attitude determination. However, conventional on-orbit method which take advantage of inter-star cosine angle to solve the calibration problem of large field-of-view (FOV) star sensor was not sufficient due to the complexity of lens distortions. Motivated by this observation, a novel technique for the large FOV star sensor's calibration based on machine learning theory was presented. The technique used the created features to build complex distortion models. These created features were redundant, so Principal Components Analysis (PCA) was employed to reduce the useless features. Moreover, two methods were employed to judge the efficiency and the effectiveness of the calibration algorithms. The results show that machine-leaning based on-orbit calibration method can achieve remarkable improvement when the distortion of the large FOV star sensor is relatively huge. The calibration error is less than 0.8 under the working condition in the paper. Compared with current algorithms, this algorithm can achieve a higher accuracy and is more robust.