基于AFKF的多旋翼无人机组合导航技术研究

Research on Integrated Navigation Technology for Multi-rotor Unmanned Aerial Vehicle Based on AFKF

摘要:

目前旋翼无人机组合导航系统大都使用扩展卡尔曼滤波算法，然而由于导航系统建模误差和传感器测量精度的影响，导航信息解算误差较大。为了改善旋翼无人机的飞行控制效果，应用自适应渐消卡尔曼滤波（Adaptive fading Kalman filter，AFKF）进行旋翼无人机组合导航解算，算法通过实时计算遗忘因子，对过去的数据权重进行削减，以提高扩展卡尔曼滤波算法的自适应能力。应用旋翼无人机真实飞行数据进行仿真，仿真结果表明，自适应渐消卡尔曼滤波算法能够有效抑制建模误差，弥补传感器测量精度不足，改善旋翼无人机组合导航解算结果。

Abstract:

At present, most of the rotary-wing UAV integrated navigation systems use the extended Kalman filter algorithm. However, due to the influence of the navigation system modeling error and the sensor measurement accuracy, the navigation information solution error is large. In order to improve the flight control effect of the rotorless drone, this paper applies the adaptive fading Kalman filter (AFKF) for the combined navigation of the rotorcraft. The algorithm calculates the forgetting factor in real time and weights the past data. Cuts are made to improve the adaptive ability of the extended Kalman filter algorithm. The simulation results of the real flight data of the rotorless UAV are carried out. The simulation results show that the adaptive fading Kalman filter algorithm can effectively suppress the modeling error, make up for the insufficient measurement accuracy of the sensor, and improve the solution result of the integrated navigation of the rotorcraft.