Two-Stage Kalman Filter for Estimation of Wind Speed and UAV Flight Parameters based on GPS/INS and Pitot Tube Measurements

Two-stage Kalman filter based estimation algorithm was developed for wind speed and UAV motion parameters. In the first stage., wind speed estimation algorithm is used based on GPS measurements and dynamic pressure measurements. The wind speed is estimated by Kalman filter using GPS and Air Data System (ADS)measurements. For this purpose., Extended Kalman Filter (EKF)was designed., and as state variables., the wind velocity components and ADS pitot scale factor are considered. In the second stage., the state parameters of the UAV dynamic model are estimated using GPS and IMU measurements. The second stage filter uses GPS position measurements., IMU orientation angles and speed measurements as well as the wind speed value estimated by the first stage filter.