面向低空无人机运行的高精度卫星测速算法

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

针对无人机在城市低空环境下运行时存在多径误差导致星基导航测速精度较差的问题,提出一种利用卫星方位角加权和多普勒观测值辅助的优化测速算法。该算法融合多普勒观测值与载波相位观测值优势,根据无人机低空运行环境特点构建基于卫星方位角的加权模型,有效削弱了多路径效应对测速精度的影响,并通过抗差卡尔曼滤波提升速度估计的鲁棒性。基于实测数据的算法评估结果表明,该算法的测速精度较传统TDCP算法在2种场景下的三维速度分别提升了32.23%、10.37%、50.39%和10.20%、30.19%、31.54%,证明了该算法的有效性。

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 operat-ing environment of the UAV,which effectively weakens the influence of multi-path effect on velocity es-timation 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 estim-ation 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 tra-ditional epoch differential carrier phase metho,which proves the effectiveness of the algorithm.