Wind Estimation for UAV Based on Multi-sensor Information Fusion

Aiming at the requirements of accurate target positioning and autonomous capability for adapting to the environmental changes of unmanned aerial vehicle（UAV）,a new method for wind estimation and airspeed calibration is proposed.The method is implemented to obtain both wind speed and wind direction based on the information from a GPS receiver,an air data computer and a magnetic compass,combining with the velocity vector triangle relationships among ground speed,wind speed and air speed.Considering the installation error of Pitot tube,cubature Kalman filter（CKF）is applied to determine proportionality calibration coefficient of true airspeed,thus improving the accuracy of wind field information further.The entire autonomous flight simulation is performed in a constant 2-D wind using a digital simulation platform for UAV.Simulation results show that the wind speed and wind direction can be accurately estimated both in straight line and in turning segment during the path tracking by using the proposed method.The measurement accuracies of the wind speed and wind direction are 0.62 m/s and2.57°,respectively.