Shear wind estimation with quadrotor UAVs using Kalman filtering regressing method

This paper presents an approach for shear wind estimation with quadrotor UAVs based on Kalman filter and regressive model. Shear wind estimation and identification are crucial for tasks such as aircraft movement and wind energy monitoring applications. In order to characterize the shear wind conveniently, an economical solution is conducted to retrieve shear wind at low altitude only utilizing the data detected by the quadrotor's onboard inertial measurement unit (IMU) and GNSS module. This method consists of two main steps: 1) the wind vector is estimated at certain position of different altitudes; 2) the shear wind model is retrieved through the wind vector fusion. Firstly, a Kalman filter is designed to extract prevailing wind vector information and eliminate the disturbance of turbulence flow from a mixed wind field. Then, a Kalman filtering regression algorithm is employed to identify the shear wind model factors. Therefore, the shear wind model can be reconstructed. Simulation results demonstrate the feasibility of the approach.