Reactive, Autonomous, Markovian Sensor Tasking in Communication Starved Environments

The current Space Traffic Management (STM) community was not prepared for the exponential increase in the resident space object (RSO) population that has taken place over the last several years. The combination of poor communication infrastructure and long scheduling lead times of the Space Surveillance Network (SSN) prevent any type of reactive sensor tasking, which is required in event of anomaly detection. This dissertation was designed to survey extensions to the classical notions of covariance based sensor tasking strategies and develop a methodology for evaluating these techniques. A suboptimal partially observable Markov decision process (POMDP) was used as the simulation framework to test various reward functions and decision algorithms while enabling autonomous, reactive sensor tasking. The goal of this work was used the developed evaluation methodology to perform statistical analyses to determine which metrics were most reliable and efficient for Space Traffic Management (STM) of the geosynchronous Earth orbit (GEO) resident space object (RSO) catalog. Hypotheses were tested against simulations of 873 resident space object (RSO) in geosynchronous Earth orbit (GEO) being tracked by 18 heterogeneous, geographically disperse ground-based electro-optical (EO) sensors. This dissertation evaluates the ability of various sensor tasking metrics to produce rewards that maximize geosynchronous Earth orbit (GEO) catalog coverage capability of a sensor network under realistic communication restrictions.