Multiple space object tracking via a space-based optical sensor

Multiple space object tracking is vital to space situational awareness. In this paper, the multiple hypothesis filter is used to track multiple space objects via a space-based optical sensor, which has many distinct advantages over ground-based sensors. Due to the limited observations obtained from the space-based sensor, the constrained admissible area is used to initialize the orbit. A semi-greedy track selection (SGTS) algorithm is used to solve the multidimensional assignment problem in the observation to track association. The Cubature Kalman filter (CKF) will be used to update the orbit for each space object. Because of the various geometric relationships between the Sun, space objects, and the Earth, many trackelets are generated by the same space object. To facilitate the space object classification, the least squares method associates tracklets to objects. A set of objects with geosynchronous equatorial orbit obtained from the space catalogue is used to test and demonstrate the effectiveness of the proposed algorithm.