| Summary: | As resident space object populations grow, and satellite propulsion capabilities improve, it will become increasingly challenging for space-reliant nations to maintain space situational awareness using current human-in-the-loop methods. This dissertation develops several real-time adaptive approaches to autonomous sensor network management for tracking multiple maneuvering and non-maneuvering satellites with a diversely populated Space Object Surveillance and Identification network. The proposed methods integrate suboptimal Partially Observed Markov Decision Processes (POMDPs) with covariance inflation or multiple model adaptive estimation techniques to task sensors and maintain viable orbit estimates for all targets. The POMDPs developed in this dissertation use information-based and system-based metrics to determine the rewards and costs associated with tasking a specific sensor to track a particular satellite. Like in real-world situations, the population of target satellites vastly outnumbers the available set of sensors. Robust and adaptable tasking algorithms are needed in this scenario to determine how and when sensors should be tasked. The strategies developed in this dissertation successfully track 207 non-maneuvering and maneuvering spacecraft using only 24 ground and space-based sensors. The results show that multiple model adaptive estimation coupled with a multi-metric, suboptimal POMDP can effectively and efficiently task a diverse network of sensors to track multiple maneuvering spacecraft, while simultaneously monitoring a large number of non-maneuvering objects. Overall, this dissertation demonstrates the potential for autonomous and adaptable sensor network command and control for real-world space situational awareness. === Ph. D.
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