Method of Wind Estimation for UAV Based on Hybrid Derivative-Free Extended Kalman Filter

Providing accurate path following is a key challenge in achieving full autonomy for UAVs. These issues are particularly important for small unmanned aerial vehicle (UAV) applications due to their smaller size and lighter weight, which make them more susceptible to the effects of wind. To mitigate these challenges, a new method of wind field estimation and airspeed calibration is proposed. The method is based on the UAV's own GPS receiver, magnetic compass and atmospheric data computer sensor information through fusion algorithm. A Hybrid derivative-free extended Kalman filter algorithm (HDEKF) is used to estimate the proportional calibration coefficients of wind field information and true airspeed. Using a digital simulation platform for the flight control system of a UAV, the entire process of autonomous flight simulation is conducted under the condition of 2D constant wind. The simulation results demonstrate that the proposed method can accurately estimate the wind field information in both the straight flight and the turning section of the UAV route.