An improved Richardson-Lucy algorithm for star image deblurring

The star sensor is widely used in attitude control systems of spacecraft for attitude measurement. However, under dynamic conditions, the star image acquired by star sensor may appear smearing, which leads to a decline in accuracy or failure of star centroid extraction and attitude determination. In order to improve the performance of the star sensor under high dynamic environment, a Richardson-Lucy (RL) algorithm based on radial basis function neural network (RBFNN) is proposed. First, the Point Spread Function (PSF) is calculated by using the angular velocity information provided by the gyroscope. Then, we use the RBFNN 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 of the proposed algorithm.