Unmanned Air Vehicles (UAVs) have recently generated great interest because of their potential to perform hazardous missions without endangering the lives of pilots and crews. In order to extend the mission range of these vehicles, it has been proposed that they should be refuelable in-air using currently available tanker aircraft. Since UAVs are unmanned, these refueling missions must take place autonomously. For this to be possible the position of the UAV relative to the tanker must be known very accurately in real time. In this work, Global Positioning System (GPS) is used to determine the relative position of the UAV to the tanker. In GPS, the accuracy of the estimated position is highly dependent on the number of visible satellites, their spatial geometry, and the precision of the received measurements. Because of the Autonomous Airborne Refueling system (AAR) stringent accuracy and integrity requirements (sub-meter 5sigma accuracy), this research focuses on developing high-performance carrier phase GPS-based navigation algorithms for AAR.
The AAR mission is unique because of the severe sky blockage that is introduced by the tanker, which reduces the number of visible GPS satellites and hence degrades the positioning accuracy.
in order to address this issue, a high-fidelity dynamic sky blo
ckage
model
was developed and experimentally validated. In addition, robust carrier
phase
differential GPS navigation algorithms were
derived, including a new method for high-
integrity reacquisition of carrier cycle ambiguities for recently-blocked satellites. In order to evaluate navigation performance, world-wide global availability and sensitivity covariance analyses were conducted.
The algorithms and methods developed in this work are generally applicable to realize high-performance GPS-based navigation in
partially obstructed environments. Navigation performance for AAR was quantified through covariance analysis, and it was shown that the stringent navigation requirements for this application are achievable. Finally, a real-time implementation of the
algorithms was developed and successfully validated in autopiloted flight tests (see here).
Unmanned Air Vehicles (UAVs) have recently generated great interest because of their potential to perform hazardous missions without endangering the lives of pilots and crews. In order to extend the mission range of these vehicles, it has been proposed that they should be refuelable in-air using currently available tanker aircraft. Since UAVs are unmanned, these refueling missions must take place autonomously. For this to be possible the position of the UAV relative to the tanker must be known very accurately in real time. In this work, Global Positioning System (GPS) is used to determine the relative position of the UAV to the tanker. In GPS, the 
accuracy of the estimated position is highly dependent on the number of visible satellites, their spatial geometry, and the precision of the received measurements. Because of the Autonomous Airborne Refueling system (AAR) stringent accuracy and integrity requirements (sub-meter 5sigma accuracy), this research focuses on developing high-performance carrier phase GPS-based navigation algorithms for AAR. 
The AAR mission is unique because of the severe sky blockage that is introduced by the tanker, which reduces the number of visible GPS satellites and hence degrades the positioning accuracy.
in order to address this issue, a high-fidelity dynamic sky blo
ckage
model
was developed and experimentally validated. In addition, robust carrier
phase
differential GPS navigation algorithms were
derived, including a new method for high-
integrity reacquisition of carrier cycle ambiguities for recently-blocked satellites. In order to evaluate navigation performance, world-wide global availability and sensitivity covariance analyses were conducted.