基于深度神经网络的星敏感器成像在轨校正方法

Correction Method for On-Orbit Distortion in Star Sensor Imaging Based on Deep Neural Networks

由于卫星在轨时受空间恶劣环境干扰,星敏感器相机在轨输出的图像相对于地面标定时产生畸变,制约其姿态测量的精度,且受在轨运行条件的限制难以进行实时有效的图像校正,造成星敏感器精度受限。针对以上问题,文章提出一种可用于在轨校正星敏感器畸变的方法,通过构建星点畸变坐标与星库理论坐标的映射数据集来训练神经网络模型,拟合星敏感器成像的非线性畸变,实现高精度图像畸变校正。为了验证该方法的图像畸变校正能力,文章进行了模拟星图仿真试验,试验结果显示,星点测量坐标与理论坐标平均误差在校正后从0.7237像素降至0.0586像素。该结果表明文中所提出的校正方法可以使星敏感器解算精度得以显著提升,且具备在轨学习能力,可扩展应用到其他星上载荷的图像校正。

Due to the interference of the harsh space environment on satellites in orbit,the images outputed by the star sensor cameras in orbit are distorted compared to those calibrated on the ground.This distortion restricts the accuracy of attitude measurement,and real-time effective image correction is difficult due to the limitations of in-orbit operating conditions,thus limiting the accuracy of the star sensors.To address these issues,this paper proposes a method for on-orbit correction of star sensor distortions.The method involves constructing a mapping dataset between the distorted coordinates of star points and the theoretical coordinates from the star catalog to train a neural network model,which fits the nonlinear distortions in star sensor imaging,achieving high-precision image distortion correction.To verify the distortion correction capability of this method,a simulated star map experiment was conducted.Through the simulation experiment,the average error between the measured coordinates of the star points and the theoretical coordinates was reduced from 0.7237 pixels to 0.0586 pixels after correction.This result indicates that the proposed correction method can significantly enhance the resolution accuracy of star sensors and has the potential for on-orbit learning capabilities and extending to other onboard payload image correction applications.