Attitude Estimation for UAV Using Extended Kalman Filter

A novel attitude estimation algorithm is proposed for unmanned aerial vehicles(UAV) in this paper. It uses attitude quaternion to represent the attitude of UAV, and uses extended Kalman filter(EKF) to fuse the merits of magnetic, angular rate, and gravity(MARG) sensors. First, attitude quaternion and drift bias of gyroscope are selected to construct the state vector, and the state equation is established based on the kinematics model of gyroscope. Then, an orthogonalization method is utilized to obtain the unit attitude quaternion from the outputs of accelerometer and magnetometer, it makes the magnetic field vector perpendicular to the measured gravity vector, which avoids the geomagnetic disturbance. And the unit attitude quaternion is used for the measurements for the EKF. Finally, the EKF update equation is used to determine the attitude of UAV. Experiments are provided on a real-world data set and the results show that the algorithm can precisely represents the orientation of UAV in both static and dynamic situation.