Risk-aware sensor scheduling and tracking of large constellations

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