Estimation of Spacecraft Angular Velocity Based on the Optical Flow of Star Images Using an Optimized Kalman Filter

Biomimetic vision is a promising method for efficient navigation and perception, showing great potential in modern navigation systems. Optical flow information, which comes from changes in an image on an organism's retina as it moves relative to objects, is crucial in this process. Similarly, the star sensor is a critical component to obtain the optical flow for attitude measurement using sequences of star images. Accurate information on angular velocity obtained from star sensors could guarantee the proper functioning of spacecraft in complex environments. In this study, an optimized Kalman filtering method based on the optical flow of star images for spacecraft angular velocity estimation is proposed. The optimized Kalman filtering method introduces an adaptive factor to enhance the adaptability under dynamic conditions and improve the accuracy of angular velocity estimation. This method only requires optical flow from two consecutive star images. In simulation experiments, the proposed method has been compared with the classic Kalman filtering method. The results demonstrate the high precision and robust performance of the proposed method.