Dynamic Star Positioning Accuracy Improving Method Using Coded Exposure for Star Sensor

The inaccurate positioning of dynamic stars is the main challenge faced by dynamic star sensors. To reduce the star positioning error, researchers have focused on refining detection and positioning methods, but such methods still employ conventional exposure for star images. This study introduces a novel approach to reducing star positioning errors by using coded exposure. In this study, two key models for the encoded star strip are established: 1) the energy distribution model, based on the coded line-spread function (CLSF), and 2) the positioning error model, described by coded length factors. Based on the models, we demonstrate the principle of star positioning accuracy improvement by using coded exposure, and we derive the optimal code for minimizing positioning errors. The experimental results validate the correctness of the proposed models and show that using identical detection and positioning methods, compared with conventional exposure, the proposed coded exposure approach can decrease star positioning errors by more than 35% under the condition of 5 degrees/s.