To precisely obtain the relative pose of UAV during aerial refueling docking, a machine vision-aided INS/GPS/MV integrated navigation scheme is proposed. Feature extraction and match algorithm of machine vision image are studied. By introducing relative inertial errors, an extended state model of global filter is designed. GPS and machine vision measurement models are established using level-arm vectors. A global multirate extended Kalman filter based on federal framework is designed to realize multirate mutisensor data fusion. Comparison is made between the proposed algorithm and the standard EKF algorithm. Simulation shows that the proposed algorithm can effectively fuse INS/GPS/MV data. The navigation parameter precision and output bandwidth satisfy the requirements of UAV aerial refueling. It can improve UAV qualities and loosen requirements of flight control systems.