Using the Joint Probabilistic Data Association Filter for Improving Star Trackers Performance to Accurate Attitude Determination of Spacecrafts

Automatic control of satellites and spacecrafts, has been extensively paid attention. Attitude determination is one of the most important procedures to control a spacecraft. Star trackers are widely used for attitude determining. A star tracker provides images from the around space and try to identify the stars in the images. Several algorithms are proposed to this end. However, most of these algorithms use the raw measurements to star identification and attitude determination. As the measurements are often affected by various types of noise, the performance of such algorithms is degraded. Here, we employ tracking algorithms to improve the performance of existing methods for attitude determining. The Kalman filter-based tracking algorithms are shown to have satisfactory results for object tracking. We use the JPDAF to build an algorithm for accurate tracking of stars locations in successive images and, consequently, determining the attitude of spacecraft. The presented algorithm is compared with the well known algorithm for attitude determining called SNA.