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At present, most of the rotary-wing UAV integrated navigation systems use the extended Kalman filter algorithm. However, due to the influence of the navigation system modeling error and the sensor measurement accuracy, the navigation information solution error is large. In order to improve the flight control effect of the rotorless drone, this paper applies the adaptive fading Kalman filter (AFKF) for the combined navigation of the rotorcraft. The algorithm calculates the forgetting factor in real time and weights the past data. Cuts are made to improve the adaptive ability of the extended Kalman filter algorithm. The simulation results of the real flight data of the rotorless UAV are carried out. The simulation results show that the adaptive fading Kalman filter algorithm can effectively suppress the modeling error, make up for the insufficient measurement accuracy of the sensor, and improve the solution result of the integrated navigation of the rotorcraft.