An Algorithm of High-precise Satellite Speed Prediction for UAV Operations in Low-air Space

To solve the problem of poor accuracy of satellite-based navigation velocity estimation caused by multi-path error when UAVs operate in urban low altitude environment, this paper presents an optimization velocity estimation algorithm based on satellite azimuth weighting and doppler observation. The algorithm combines the advantages of doppler and carrier phase observations, and constructs a weighted model based on satellite azimuth angle according to the characteristics of the low-altitude operating environment of the UAV, which effectively weakens the influence of multi-path effect on velocity estimation accuracy, and improves the robustness of velocity estimation through the robust Kalman filter. The algorithm evaluation results based on the measured data show that in different scenarios, the velocity estimation accuracy of the proposed algorithm is improved by 32.23%, 10.37%, 50.39% and 10.20%, 30.19%, 31.54%, respectively in the three-dimensional of velocity direction compared under two scenes with the traditional epoch differential carrier phase metho, which proves the effectiveness of the algorithm.