Abstract
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.
Highlights
Along with the development of navigation technology, the requirement for a spacecraft attitude measurement is becoming higher and higher [1,2]
To solve the shortcomings of the above methods and further improve the performance of the star sensor under highly dynamic conditions, we propose an improved gyroscope-assisted star image prediction method and RL non-blind deblurring algorithm
The optimal number of iterations and the sum of the Magnitude of Fourier Coefficients (SUMFC) of Coefficients (SUMFC) of the point spread function (PSF) of the blurred star image are used for the training of the ensemble the PSF of the blurred star image are used for the training of the ensemble back-propagation neural back-propagation neural network (EBPNN) [41]
Summary
Along with the development of navigation technology, the requirement for a spacecraft attitude measurement is becoming higher and higher [1,2]. Only by solving the frame loss and blurring problem of the star image, can the star sensor maintain good performance under dynamic conditions. Yu et al [22] proposed a method to reduce the occurrence of the frame loss by using an intensified star sensor. To improve the dynamic performance of the star sensor, many scholars have done a lot of research in the field of image processing, especially on the star image deblurring algorithms [23]. To solve the shortcomings of the above methods and further improve the performance of the star sensor under highly dynamic conditions, we propose an improved gyroscope-assisted star image prediction method and RL non-blind deblurring algorithm.
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