A new impetus to develop autonomous aerial refueling has arisen out of the growingdemand to expand the capabilities of unmanned aerial vehicles (UAVs). Withautonomous 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 toprovide relative navigation information for autonomous probe and drogue aerial refuelingfor UAVs. This thesis develops a Kalman filter to be used in combination withthe VisNav sensor to improve the quality of the relative navigation solution duringautonomous probe and drogue refueling. The performance of the Kalman filter is examinedin a closed-loop autonomous aerial refueling simulation which includes modelsof the receiver aircraft, VisNav sensor, Reference Observer-based Tracking Controller(ROTC), and atmospheric turbulence. The Kalman filter is tuned and evaluatedfor four aerial refueling scenarios which simulate docking behavior in the absence ofturbulence, and with light, moderate, and severe turbulence intensity. The dockingscenarios demonstrate that, for a sample rate of 100 Hz, the tuning and performanceof the filter do not depend on the intensity of the turbulence, and the Kalman filterimproves the relative navigation solution from VisNav by as much as 50% duringthe 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 resultedin a small improvement in the docking accuracy and precision. The Kalmanfilter did not, however, significantly improve the probability of a successful dockingin turbulence for the simulated aerial refueling scenarios.
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