一种用于天线面形恢复测量的无人机导航方案

天线近场面形恢复算法需要搭载发射源的无人机沿特定轨迹飞行,并获得其位置。根据面形恢复算法对无人机飞行的要求设计一种包含差分GPS和视觉传感器的导航方案,在此导航方案的数据融合时间策略下使用常规扩展卡尔曼滤波器将出现震荡问题。为此提出一种基于SageHusa滤波算法改进的自适应扩展卡尔曼滤波器,并通过仿真和实际飞行测试将其与常规扩展卡尔曼滤波器进行对比。实验结果表明,此自适应扩展卡尔曼滤波器在实际应用中表现出更好的性能,并在弱GPS信号情况下能够趋向于更可信的视觉里程计数据。此方案基本满足天线测量时无人机沿轨迹飞行并采集位置数据的要求,有望实现射电望远镜主动面实时闭环修正。

A UAV Navigation Scheme for Antenna Surface Recovery Measurements

An antenna near-field phase recovery algorithm requires an unmanned aerial vehicle (UAV) flying along a specific path and obtaining its position. A navigation scheme using the differential GPS module and visual sensor was designed according to the UAV flight requirements of the phase recovery algorithm.However, using conventional extended Kalman filter (EKF) with the time strategy designed in this scheme will cause the concussion problem. An improved adaptive EKF based on the Sage-Husa algorithm was then proposed to solve the problem, and the results were compared with those of the conventional extended Kalman filter by simulations and actual flight tests. The experimental results show that the improved adaptive EKF gets better performance in practical applications, makes the results converge to visual odometry data which are more reliable in the case of low GPS signal and meets the antenna measurement requirements of letting the UAV running along the trajectory and capturing the position data which makes the near-field shape recovery be viable at any pitch angle.