In order to obtain more accurate navigation data by fusing the data obtained from various similar sensors of UAV, a dynamic weighted fusion algorithm is proposed. The concept of observation support degree is introduced to improve the traditional average weighted algorithm. By calculating the mutual support degree information between the measurement data of each sensor, and according to the change characteristics of the observation support degree, the fusion weight is dynamically updated in real time to fuse the multi-sensor data. The algorithm is applied to the analysis and processing of actual flight data, and the results show that the algorithm can adjust the weight distribution in real time according to the characteristics of sensor data, and the fusion results are more accurate and reliable than the traditional average weighted algorithm.