Safety of Flight Prediction for Small Unmanned Aerial Vehicles Using Dynamic Bayesian Networks

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dc.contributor.authorBurns, Meghan Colleenen
dc.contributor.committeechairWoolsey, Craig A.en
dc.contributor.committeememberPatil, Mayuresh J.en
dc.contributor.committeememberAdams, Richard E.en
dc.contributor.departmentAerospace and Ocean Engineeringen
dc.date.accessioned2018-05-24T08:00:17Zen
dc.date.available2018-05-24T08:00:17Zen
dc.date.issued2018-05-23en
dc.description.abstractThis thesis compares three variations of the Bayesian network as an aid for decision-making using uncertain information. After reviewing the basic theory underlying probabilistic graphical models and Bayesian estimation, the thesis presents a user-defined static Bayesian network, a static Bayesian network in which the parameter values are learned from data, and a dynamic Bayesian network with learning. As a basis for the comparison, these models are used to provide a prior assessment of the safety of flight of a small unmanned aircraft, taking into consideration the state of the aircraft and weather. The results of the analysis indicate that the dynamic Bayesian network is more effective than the static networks at predicting safety of flight.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:15170en
dc.identifier.urihttp://hdl.handle.net/10919/83381en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectDynamic Bayesian Networken
dc.subjectUnmanned Aerial Systemsen
dc.subjectRisken
dc.titleSafety of Flight Prediction for Small Unmanned Aerial Vehicles Using Dynamic Bayesian Networksen
dc.typeThesisen
thesis.degree.disciplineAerospace Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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