Due to the strong-nonlinearity and non-Gaussianity of the UAV (unmanned aerial vehicle) relative navigation system, the accuracy of the traditional relative navigation filter, which is designed based on the Kalman filtering algorithm, decreases or even diverge. In view of the advantages of HCKF (high degree cubature Kalman filter) and MCKF (maximum correntropy Kalman filter) in coping with nonlinear problems and non-Gaussian problems, respectively, the measurement update equation was modified by the measurement update method of MCKF, and the traditional measurement update problem was recast as a linear regression problem. In addition, the Gaussian assumption of the measurement noise was avoided, the system nonlinearity and measurement noise non-Gaussianity were solved at the same time. The simulation was conducted, and the simulation results indicated that RHCF is superior to HCKF and MCKF. Hence, the effectiveness of the proposed algorithm is verified.