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

This 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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Aerospace Engineering

Department dc:contributor.department

Aerospace and Ocean Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2018

Author dc:creator

• Burns, Meghan Colleen

Chair dc:contributor.committeechair

• Woolsey, Craig A.

Committee members dc:contributor.committeemember

• Patil, Mayuresh J.
• Adams, Richard E.

• Dynamic Bayesian Network
• Unmanned Aerial Systems
• Risk

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:15170

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/83381