In the UAV flight control system,when the UAV adopts a single height sensor,the measurement accuracy is low,and the traditional Kalman filter is prone to be divergent.To solve the problem,a method of fusing UAV altitude information of different sensors is proposed based on the improved Kalman filter.Firstly,the noise reduction algorithm based on ARIMA model is used to reduce the noise of the original measurement data of the three kinds of sensors.After the noise reduction,the height information of the sensors is fused for the first time by using the Kalman filter algorithm.Then,the fusion result is fused for the second time with the noise-reduced differential GPS data by using the method of recursively weighted least squares.Computational analysis shows that,compared with the traditional Kalman filter algorithm,the Root Mean Square Error (RMSE) of the height estimation is reduced by 39.6% and the maximum deviation is reduced by 31.7%.The simulation results show that the positioning accuracy of the obtained results in the vertical direction is effectively improved,and the preliminary ability to deal with abnormal conditions is guaranteed,which ensures the accuracy and reliability of the UAV flight control system.