Robust Online Trajectory Prediction for Non-cooperative Small Unmanned Aerial Vehicles

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dc.contributor.authorBadve, Prathamesh Maheshen
dc.contributor.committeechairChen, Xien
dc.contributor.committeememberL'Afflitto, Andreaen
dc.contributor.committeememberBoone, Edward L.en
dc.contributor.departmentIndustrial and Systems Engineeringen
dc.date.accessioned2022-01-22T09:00:27Zen
dc.date.available2022-01-22T09:00:27Zen
dc.date.issued2022-01-21en
dc.description.abstractIn recent years, unmanned aerial vehicles (UAVs) have got a boost in their applications in civilian areas like aerial photography, agriculture, communication, etc. An increasing research effort is being exerted to develop sophisticated trajectory prediction methods for UAVs for collision detection and trajectory planning. The existing techniques suffer from problems such as inadequate uncertainty quantification of predicted trajectories. This work adopts particle filters together with Löwner-John ellipsoid to approximate the highest posterior density region for trajectory prediction and uncertainty quantification. The particle filter is tuned and tested on real-world and simulated data sets and compared with the Kalman filter. A parallel computing approach for particle filter is further proposed. This parallel implementation makes the particle filter faster and more suitable for real-time online applications.en
dc.description.abstractgeneralIn recent years, unmanned aerial vehicles (UAVs) have got a boost in their applications in civilian areas like aerial photography, agriculture, communication, etc. Over the coming years, the number of UAVs will increase rapidly. As a result, the risk of mid-air collisions grows, leading to property damages and possible loss of life if a UAV collides with manned aircraft. An increasing research effort has been made to develop sophisticated trajectory prediction methods for UAVs for collision detection and trajectory planning. The existing techniques suffer from problems such as inadequate uncertainty quantification of predicted trajectories. This work adopts particle filters, a Bayesian inferencing technique for trajectory prediction. The use of minimum volume enclosing ellipsoid to approximate the highest posterior density region for prediction uncertainty quantification is also investigated. The particle filter is tuned and tested on real-world and simulated data sets and compared with the Kalman filter. A parallel computing approach for particle filter is further proposed. This parallel implementation makes the particle filter faster and more suitable for real-time online applications.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:33804en
dc.identifier.urihttp://hdl.handle.net/10919/107851en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectparticle filtersen
dc.subjectpath planningen
dc.subjectBayesian inferenceen
dc.subjectnon-cooperative UAVsen
dc.subjectparallel computingen
dc.subjectmessage passing interfaceen
dc.titleRobust Online Trajectory Prediction for Non-cooperative Small Unmanned Aerial Vehiclesen
dc.typeThesisen
thesis.degree.disciplineIndustrial and Systems Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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