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dc.contributor.advisorValasek, Johnen_US
dc.creatorBowers, Roshawn Elizabethen_US
dc.date.accessioned2005-11-01T15:49:47Z
dc.date.available2005-11-01T15:49:47Z
dc.date.created2005-08en_US
dc.date.issued2005-11-01
dc.identifier.urihttp://hdl.handle.net/1969.1/2700
dc.description.abstractA new impetus to develop autonomous aerial refueling has arisen out of the growing demand to expand the capabilities of unmanned aerial vehicles (UAVs). With autonomous aerial refueling, UAVs can retain the advantages of being small, inexpensive, and expendable, while offering superior range and loiter-time capabilities. VisNav, a vision based sensor, offers the accuracy and reliability needed in order to provide relative navigation information for autonomous probe and drogue aerial refueling for UAVs. This thesis develops a Kalman filter to be used in combination with the VisNav sensor to improve the quality of the relative navigation solution during autonomous probe and drogue refueling. The performance of the Kalman filter is examined in a closed-loop autonomous aerial refueling simulation which includes models of the receiver aircraft, VisNav sensor, Reference Observer-based Tracking Controller (ROTC), and atmospheric turbulence. The Kalman filter is tuned and evaluated for four aerial refueling scenarios which simulate docking behavior in the absence of turbulence, and with light, moderate, and severe turbulence intensity. The docking scenarios demonstrate that, for a sample rate of 100 Hz, the tuning and performance of the filter do not depend on the intensity of the turbulence, and the Kalman filter improves the relative navigation solution from VisNav by as much as 50% during the early stages of the docking maneuver. For the aerial refueling scenarios modeledin this thesis, the addition of the Kalman filter to the VisNav/ROTC structure resulted in a small improvement in the docking accuracy and precision. The Kalman filter did not, however, significantly improve the probability of a successful docking in turbulence for the simulated aerial refueling scenarios.en_US
dc.format.extent3281743 bytes
dc.format.mediumelectronicen_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.publisherTexas A&M Universityen_US
dc.subjectestimationen_US
dc.subjectcontrolen_US
dc.subjectvision based sensorsen_US
dc.titleEstimation algorithm for autonomous aerial refueling using a vision based relative navigation systemen_US
dc.typeBooken
dc.typeThesisen
thesis.degree.departmentAerospace Engineeringen_US
thesis.degree.disciplineAerospace Engineeringen_US
thesis.degree.grantorTexas A&M Universityen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelMastersen_US
dc.contributor.committeeMemberHurtado, John E.en_US
dc.contributor.committeeMemberJunkins, John L.en_US
dc.contributor.committeeMemberLangari, Rezaen_US
dc.type.genreElectronic Thesisen_US
dc.type.materialtexten_US
dc.format.digitalOriginborn digitalen_US


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