High accuracy star image locating and imaging calibration for star sensor technology

2010 ◽  
Author(s):  
Shaodi Zhang ◽  
Zhijun Zhang ◽  
Honghai Sun ◽  
Yanjie Wang
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Hao Zhang ◽  
Yanxiong Niu ◽  
Jiazhen Lu ◽  
He Zhang

Star sensor is a preferred attitude measurement device for its extremely high accuracy. Star acquisition is the essential and critical procedure, which is aiming at acquiring accurate star areas. However, degenerated acquisition results under complex conditions become one of the major restrictions for modern star sensor. In this paper, an accurate and autonomous star acquisition method is proposed. Mathematical morphology and variable thresholding are combined for accurate star extraction; motion PSF is estimated in frequency domain and nonlinear filter is adopted for star restoration. Accurate star acquisition can be achieved based on only one star image. Simulations and laboratory experiments are conducted for verification. Several existing methods are also reproduced for comparison. Acquisition results demonstrate that the proposed method is effective and an excellent performance can be achieved autonomously under complex conditions, along with more detected stars and improved acquisition accuracy.


2019 ◽  
Vol 39 (7) ◽  
pp. 0712003
Author(s):  
曹阳 Yang Cao ◽  
李保权 Baoquan Li ◽  
李海涛 Haitao Li ◽  
桑鹏 Peng Sang

2016 ◽  
Vol 24 (3) ◽  
pp. 609-615
Author(s):  
郭敬明 GUO Jing-ming ◽  
赵金宇 ZHAO Jin-yu ◽  
何昕 HE Xin ◽  
刘冰 LIU Bing ◽  
张同双 ZHANG Tong-shuang ◽  
...  

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2960
Author(s):  
Wang ◽  
Wei ◽  
Li ◽  
Du ◽  
Zhang

As an important development direction of star sensor technology, the All-Time star sensor technology can expand the application of star sensors to flight platforms inside the atmosphere. Due to intense atmospheric background radiation during the daytime, the commonly used star sensors operating in the visible wavelength range are significantly limited in their ability to detect stars, and hence the All-Time star sensor technology which is based on the shortwave infrared (SWIR) imaging system has become an effective research direction. All-Time star sensor detection capability is significantly affected by observation conditions and, therefore, an optimized selection of optical parameters, which mainly includes the field of view (FOV) and the detection wavelength band, can effectively improve the detection performance of All-Time star sensors under harsh observation conditions. This paper uses the model simulation method to analyze and optimize the optical parameters under various observation conditions in a high-altitude environment. A main parameter among those discussed is the analysis of detection band optimization based on the SWIR band. Due to the huge cost constraints of high-altitude experiments, we conducted experiments near the ground to verify the effectiveness of the detection band selection and the correctness of the SWIR star sensor detection model, which thereby proved that the optimization of the optical parameters for high altitudes was effective and could be used as a reference.


Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1890 ◽  
Author(s):  
Liu ◽  
Chen ◽  
Liu ◽  
Shi

The star sensor is widely used in attitude control systems of spacecraft for attitude measurement. However, under high dynamic conditions, frame loss and smearing of the star image may appear and result in decreased accuracy or even failure of the star centroid extraction and attitude determination. To improve the performance of the star sensor under dynamic conditions, a gyroscope-assisted star image prediction method and an improved Richardson-Lucy (RL) algorithm based on the ensemble back-propagation neural network (EBPNN) are proposed. First, for the frame loss problem of the star sensor, considering the distortion of the star sensor lens, a prediction model of the star spot position is obtained by the angular rates of the gyroscope. Second, to restore the smearing star image, the point spread function (PSF) is calculated by the angular velocity of the gyroscope. Then, we use the EBPNN to predict the number of iterations required by the RL algorithm to complete the star image deblurring. Finally, simulation experiments are performed to verify the effectiveness and real-time of the proposed algorithm.


2013 ◽  
Author(s):  
Qi-meng Chen ◽  
Guo-yu Zhang ◽  
Zhe Wang ◽  
Ling-yun Wang ◽  
Yu-jun Gao
Keyword(s):  

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