针对精细导星仪(Fine Guidance Sensor, FGS)姿态测量精度受星点提取系统误差影响的问题, 提出了一种基于梯度提升决策树(Gradient Boosting Decision Tree, GBDT)拟合法的高精度星点定位系统误差补偿方法。为了解决拟合样本少、输入特征差别大等问题, 采用对输入范围不敏感、易于训练的决策树作为基模型, 并根据当前模型拟合残差梯度, 结合集成学习中的提升方法生成新的基模型得到系统误差与探测器填充率、采样窗口尺寸、星斑束腰半径以及星点质心坐标计算值之间的函数关系, 以此函数关系为基础对星点质心坐标估计值进行系统误差校正。实验结果表明: 与支持向量回归机(Support Vector Regression, SVR)相比, 基于GBDT的高精度星点定位算法的误差减小了60.6%, 经该算法补偿后的质心误差为0.014 5 pixel, 相比于质心法误差减小了61.5%。

Aiming at the problem that the attitude measurement accuracy of Fine Guidance Sensor (FGS) was affected by the error of star point extraction system, a high-precision star point positioning system error compensation method based on Gradient Boosting Decision Tree(GBDT) fitting method was proposed. In order to solve the problems of less fitting samples and large differences in input characteristics, a decision tree that was insensitive to the input range and easy to train was used as the base model. Combining the boosting method in ensemble learning to generate a new base model to obtain the functional relationship between the systematic error and the detector fill rate, sampling window size, Gaussian width of star image and star point centroid coordinate calculation value, and based on this function relationship to the star point centroid. The coordinate estimate was systematically corrected. The experimental results show that compared with the support vector regression machine, the error of the high-precision star point localization algorithm based on GBDT is reduced by 60.6%. The corrected centroid error is 0.014 5 pixel, and the error is reduced by 61.5%.