传统的质心算法面临着光学像差和随机噪声对质心位置提取精度影响很难减小的问题。为了解决这一问题,对星敏感器所成星点像进行分析,结合星敏感器光学系统的调制传递函数和图像传感器像素频率响应特性,提出了一种基于星点像重采样的星敏感器质心定位算法。根据夫琅禾费衍射理论计算出离焦光学系统的点扩展函数,并对重采样质心算法的系统误差分布进行仿真,结果显示,该算法在不同像差条件下的系统误差均方根都小于0.01 pixel。用星敏感器产品进行了质心提取系统误差测量实验,重采样质心算法的系统误差为0.008 pixel,相比传统正弦曲线补偿方法的精度提高了66%。仿真和实验结果表明:基于星点像重采样的质心算法精度高,受光学系统像差的影响小,是提高星敏感器测量精度的一种有效方法。

Reducing the impact of optical aberrations and random noise on the accuracy of star centroid location extractions is difficult using traditional centroid algorithms. To solve this problem, the star image of a star sensor is analyzed herein and a star sensor centroid localization algorithm based on star image resampling is proposed. The algorithm employs the modulation transfer function of the star sensor optical system and pixel frequency response characteristics of the image sensor. The point spread function of the defocused optical system is calculated based on the Fraunhofer diffraction theory, and the systematic error of the resampling-based centroid algorithm is simulated. Simulation results show that the root mean squares of the systematic errors under different aberration conditions are <0.01 pixel. Results of the error-measurement experiments of centroid extraction system performed on a star sensor show that the systematic error of the resampling-based centroid algorithm is 0.008 pixel, which is 66% lower than that of the traditional sinusoidal curve compensation method. The proposed algorithm has high precision and is not affected by the aberrations of the optical system; therefore, it is an effective method for improving the accuracy of star sensors.