讲座题目：Space Situational Awareness: Maneuvering Spacecraft Tracking via State-Dependent Adaptive Estimation
讲座内容：As space has become highly congested by many space objects, space situational awareness (SSA) has become crucial for the safe operation of space assets. One of the challenging tasks in SSA is the surveillance and tracking of spacecraft that have the maneuver capability to perform various space missions. Those tasks are essential to accurately predict the future trajectories of the maneuvering spacecraft, and thus to effectively manage the safety of adjacent spacecraft. This emphasizes the need to develop effective and efficient techniques for maneuvering spacecraft tracking. However, the accurate tracking of impulsively maneuvering spacecraft is a challenging problem since the magnitude and the time of occurrence of impulsive maneuvers are usually unknown a priori. To deal with this problem, an adaptive state estimation, called hybrid estimation, algorithm is developed in this study using a bank of the extended Kalman filters along with interacting multiple models which account for the motion of spacecraft with and without impulsive maneuvers. Motivated by the fact that impulsive maneuvers usually occur when certain conditions on the state of the spacecraft are satisfied, the multiple extended Kalman filters are systematically blended using a state-dependent transition probability. Since the information on the conditions based on which the impulsive maneuvers occur is explicitly utilized in the state-dependent transition probability, the proposed algorithm can predict the impulsive maneuvers more accurately and thus produce more accurate state estimates. The proposed algorithm is demonstrated with two illustrative examples of tracking geostationary satellites that perform station keeping maneuvers and tracking of a spacecraft performing orbital transfers.