Risk-aware sensor scheduling and tracking of large constellations

Commercial proposals for large constellations of satellites present a major problem in the field of Space Situational Awareness (SSA). Space traffic management systems must produce accurate and timely predictions of potential collisions for operators to perform evasive maneuvers. This requires precise orbit determination solutions, but limitations on sensor resources can impair tracker performance. This work presents an integrated tasking and tracking system that can autonomously task a network of ground sensors to track large constellations with the goal of identifying hazardous conjunctions. The sensor scheduling approach combines information gain maximization with predicted probability of collision to produce a single reward function for tasking. The labeled multi-Bernoulli filter, a multi target filter capable of resolving data ambiguity, processes the data and the resulting catalog estimate drives another round of scheduling. A tracking simulation involving 4,545 satellites in low-Earth orbit and a network of twelve ground-based sensors demonstrates how this scheduling method can focus on high-risk targets while maintaining custody of the entire constellation. A conjunction assessment is performed using the estimated catalog at the end of the tracking phase, and the accuracy of the analysis is used to evaluate the scheduling method's efficacy. (C) 2021 Published by Elsevier B.V. on behalf of COSPAR.