Estimation algorithm for autonomous aerial refueling using a vision based relative navigation system

Abstract

A new impetus to develop autonomous aerial refueling has arisen out of the growing\ndemand to expand the capabilities of unmanned aerial vehicles (UAVs). With\nautonomous aerial refueling, UAVs can retain the advantages of being small, inexpensive,\nand expendable, while offering superior range and loiter-time capabilities.\nVisNav, a vision based sensor, offers the accuracy and reliability needed in order to\nprovide relative navigation information for autonomous probe and drogue aerial refueling\nfor UAVs. This thesis develops a Kalman filter to be used in combination with\nthe VisNav sensor to improve the quality of the relative navigation solution during\nautonomous probe and drogue refueling. The performance of the Kalman filter is examined\nin a closed-loop autonomous aerial refueling simulation which includes models\nof the receiver aircraft, VisNav sensor, Reference Observer-based Tracking Controller\n(ROTC), and atmospheric turbulence. The Kalman filter is tuned and evaluated\nfor four aerial refueling scenarios which simulate docking behavior in the absence of\nturbulence, and with light, moderate, and severe turbulence intensity. The docking\nscenarios demonstrate that, for a sample rate of 100 Hz, the tuning and performance\nof the filter do not depend on the intensity of the turbulence, and the Kalman filter\nimproves the relative navigation solution from VisNav by as much as 50% during\nthe 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\nin a small improvement in the docking accuracy and precision. The Kalman\nfilter did not, however, significantly improve the probability of a successful docking\nin turbulence for the simulated aerial refueling scenarios.