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. / As the number of spacecraft in orbit increase, and satellite propulsion capabilities improve, it will become increasingly difficult for space-reliant nations to keep track of every object orbiting earth using human-in-the-loop methods. Already, the population of target satellites vastly outnumbers the available set of sensors. At any given time, a given network of sensors cannot observe every satellite in orbit, and must manage the available sensors effectively to keep track of every object of interest. The ability to maintain actionable knowledge of every orbiting object of interest is known as space situational awareness. Conventional tracking processes have generally not changed for decades, and were designed when there were far fewer satellites in orbit with little or no ability to maneuver. These methods involve large numbers of operators and engineers who schedule a network of sensors under the assumption that the satellites will not unexpectedly change their orbits for long periods of time. In the near future, traditional space surveillance approaches will become insufficient at maintaining space situational awareness, particularly if more satellites conduct unanticipated maneuvers. This dissertation develops several real-time approaches for controlling a diverse network of ground and space-based sensors that remove the need for human intervention. These fully computer-based command and control processes adapt to dynamic situations and automatically task sensors to rapidly track multiple maneuvering and non-maneuvering satellites. The decision processes used to determine which sensors should be tasked to observe a particular spacecraft compare the amount of information that can be collected in a single observation and the workload a sensor must execute to collect the observation. The command and control 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 adaptive, fully autonomous sensor network control processes can effectively and efficiently task a diverse set of sensors to track multiple maneuvering spacecraft, while simultaneously monitoring a large number of non-maneuvering objects. Overall, this dissertation demonstrates the potential for adaptive, computer-based sensor network command and control for real-world space situational awareness.
This research was supported by the Virginia Tech New Horizons Graduate Scholar Program, the Ted and Karyn Hume Center for National Security and Technology, the DARPA Hallmark program, and the U.S. Joint Warfare Analysis Center.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/84931 |
| Date | 28 August 2018 |
| Creators | Nastasi, Kevin Michael |
| Contributors | Aerospace and Ocean Engineering, Black, Jonathan T., Paterson, Eric G., Psiaki, Mark L., Canfield, Robert A. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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