Star Tracking Algorithm Based on Local Dynamic Background Reduction for Eliminating Stray Light Interference From Star Spot Data

Star sensors determine the attitude of spacecraft on the basis of the star spot data detected by them; however, these sensors often encounter interference from stray light, which affects spots’ centroid extraction. To handle this problem, star spots must be separated from the background containing stray lights and noise through image processing. The operating parameters of star sensors in the star tracking mode can be used to construct a dynamic background template. In this study, a star tracking algorithm based on an extended Kalman filter (EKF) was designed. This algorithm regularly updates reference star parameters and uses a low-pass filter to identify the background. It performs thresholding in a local region by using mapping windows set according to the centroid locations predicted by the EKF. After reducing the background and eliminating small spikes, star spots can be extracted. A rotation simulation was performed in this study to generate a sequence of stellar images. A rotation period during which sensors would encounter stray light with a small incident angle was selected for the simulation. Furthermore, a limiting magnitude was applied for examining the performance of the developed algorithm with a low-sensitivity camera. In the simulation, the developed tracking algorithm provided continuous and stable attitude estimates despite the occurrence of stray light interference.