Decentralized decision making over random graphs for space domain awareness

Abstract

The expansion of satellites in near-Earth and cislunar environments necessitates scalable and efficient space object tracking methodologies. Recent literature poses this problem as a sequential decision making problem for many observing agents, but decentralized decision making methods often require significant communication between agents. A two-stage approach is presented to ensure a convergent decision making methodology with limited lines of communication. First, a scheme is outlined for randomized, directed communication between agents. Guarantees on connectivity and communication times are illustrated. Additionally, communications of optimal action trajectories for another agent often lead to changes in locally optimal actions. With the assumption that such breakpoints occur as a power law in search time, asymptotic convergence toward a globally optimal consensus is demonstrated. The decision making methodology is then presented for a variety of space object sensor tasking problems in the local geostationary environment and the cislunar domain. The developed methodology is demonstrated to successfully maintain large catalogs of space objects in a manner robust to nonlinearities and failures in communication.