针对现有描述星点能量分布的高斯模型的局限性, 基于恒星辐射特性和星敏感器的成像特性, 提出了改进的偏正态分布恒星成像模型, 并确定其关键参数向量.根据星敏感器对恒星成像为平稳随机过程的特点, 设计了Kalman滤波器迭代估计星点像斑特征, 提取四个星点像斑特征量, 建立状态空间, 得到特征量在最小二乘意义下的最优估计值, 以此作为参考基准, 利用查表法实现星点成像模型参数寻优求解.地面观星实验结果表明, 基于Kalman滤波可以快速有效地对实测星点像斑特征量进行估计, 相对于高斯模型, 改进的偏高斯模型对星点像斑能量分布的仿真精度更高.

In view of limitations of the Gaussian model for describing star energy distribution, based on the radiation characteristic of stars and the imaging properties of star tracker, an improved skewed normal distribution model of star imaging was proposed and its key parameter vector was determined. The Kalman filtering was designed to estimate the characteristics of star spot. Then, based on the characteristic that the stellar imaging process of star sensor is a stationary random process, the state space composed of the characteristic of star spot was established and the optimal estimation value of the characteristics in the least square sense was obtained. Finally, the parameter optimization of star point imaging model was achieved by using look-up table method. The result of the ground star observation shows that the Kalman filter can estimate the characteristic quantity quickly and effectively. Compared with the Gaussian model, the improved skewed normal distribution model has higher simulation accuracy for the energy distribution of the star spot.