Iterative Estimation Algorithm of Star Tracker's Star Imaging Model

被引：0
|
作者：
Lian Da [1 ,2 ]
Mao Xiao-nan [1 ,2 ]
Zheng Xun-jiang [1 ,2 ]
Zhou Qi [1 ,2 ]
Yu Lu-wei [1 ,2 ]
Hu Xiong-chao [1 ,2 ]
机构：
[1] Shanghai Inst Spaceflight Control Technol, Shanghai 201109, Peoples R China
[2] China Aerosp Sci & Technol Corp, Res & Dev Ctr Infrared Detect Technol, Shanghai 201109, Peoples R China
来源：
ACTA PHOTONICA SINICA | 2019年 / 48卷 / 01期
关键词：
Star imaging model; Skewed Gaussian model; Characteristics extraction; Kalman filtering; Model parameter determination; Iterative estimation; Gaussian distribution; PERFORMANCE;
D O I：
10.3788/gzxb20194801.0104002
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
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.
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页数：8
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