Liu Di, Chen Xiyuan, Liu Xiao, Shi Chunfeng
Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
School of Electrical Engineering and Automation, Qilu University of Technology, Jinan 250353, China.
Sensors (Basel). 2019 Apr 20;19(8):1890. doi: 10.3390/s19081890.
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.
星敏感器广泛应用于航天器姿态控制系统中进行姿态测量。然而，在高动态条件下，可能会出现星图帧丢失和图像拖影现象，导致星质心提取和姿态确定的精度下降甚至失败。为了提高星敏感器在动态条件下的性能，提出了一种陀螺辅助星图预测方法和一种基于集成反向传播神经网络（EBPNN）的改进理查森- Lucy（RL）算法。首先，针对星敏感器的帧丢失问题，考虑星敏感器镜头的畸变，利用陀螺的角速率得到星点位置的预测模型。其次，为了恢复拖影的星图，通过陀螺的角速度计算点扩散函数（PSF）。然后，利用EBPNN预测RL算法完成星图去模糊所需的迭代次数。最后，进行仿真实验验证了所提算法的有效性和实时性。
