Adaptive Sensor Tasking for Collision Assessment Under Epistemic Uncertainty

The increasing number of space objects poses a growing collision risk to space objects. Given limited sensor resources, it is essential to develop an effective sensor tasking method capable of simultaneously tracking multiple targets and assessing collisions. However, traditional Random Finite Set (RFS) based sensor tasking methods overlook epistemic uncertainty and different targets’ need for observations, resulting in inaccurate collision assessment and state estimation. To address this problem, this paper introduces an adaptive sensor tasking method for collision assessment that accounts for epistemic uncertainty. An enhanced possibility information gain is proposed by integrating targets’ collision risk into the sensor tasking objective function. For the multi-target tracking, an augmented LMB Uncertain Finite Set (UFS) is employed to characterize the collision risk alongside trajectory-related parameters, applying in the possibility LMB filter. Simulation results, including sensor tasking for 1010 LEO objects validated the enhanced performance of the developed method.