Reactive, Autonomous, Markovian Sensor Tasking in Communication Starved Environments

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dc.contributor.authorKadan, Jonathan Evanen
dc.contributor.committeechairBlack, Jonathan T.en
dc.contributor.committeechairSchroeder, Kevin Kenten
dc.contributor.committeememberFitzgerald, Riley McCreaen
dc.contributor.committeememberRoss, Shane Daviden
dc.contributor.departmentAerospace and Ocean Engineeringen
dc.date.accessioned2024-01-03T09:00:47Zen
dc.date.available2024-01-03T09:00:47Zen
dc.date.issued2024-01-02en
dc.description.abstractThe 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.en
dc.description.abstractgeneralSpace is getting crowded at an increasing rate. Communication issues and rigid scheduling of the Space Surveillance Network (SSN) prevent reactive sensor tasking, which is needed to alleviate this issue. This dissertation was designed to survey different sensor tasking strategies and develop a methodology for evaluating these techniques. A discrete time estimator called 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. Multiple simulation scenarios were evaluated, with the first focused on determining the proper metrics in the ideal sensor network distribution case. From there, hypotheses were tested against simulations of a geographically disperse network of 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 restrictionsen
dc.description.degreeDoctor of Philosophyen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:39238en
dc.identifier.urihttps://hdl.handle.net/10919/117287en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectspace traffic managementen
dc.subjectautonomyen
dc.titleReactive, Autonomous, Markovian Sensor Tasking in Communication Starved Environmentsen
dc.typeDissertationen
thesis.degree.disciplineAerospace Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.nameDoctor of Philosophyen

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