A 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.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/2700 |
| Date | 01 November 2005 |
| Creators | Bowers, Roshawn Elizabeth |
| Contributors | Valasek, John |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | 3281743 bytes, electronic, application/pdf, born digital |
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