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The purpose of this paper is to propose the application of an extended Kalman filter (EKF) for the sensors fusion task within the problem of aerial refueling for unmanned aerial vehicles (UAVs). Specifically, the EKF is used to combine the position data from a global positioning system (GPS) and a machine vision (MV)-based system for providing a reliable estimation of the tanker-UAV relative position throughout the docking and the refueling phase. The performance of the scheme has been evaluated using a virtual environment specifically developed for the study of the UAV aerial refueling problem. Particularly, the EKF-based sensor fusion scheme integrates GPS data with MV-based estimates of the tanker-UAV position derived through a combination of feature extraction, feature classification, and pose estimation algorithms. The achieved results indicate that the accuracy of the relative position using GPS or MV estimates can be improved by at least one order of magnitude with the use of EKF in lieu of other sensor fusion techniques.