Attitude Estimation of Four-Rotor UAV Based on Extended Kalman Filter

Abstract

Quadrotor unmanned aerial vehicle (UAV) is a typical multi-input multi-output (MIMO), nonlinear and strong coupling underactuated system. In the working process of the system, it is necessary to perform information fusion on the attitude detected by the sensor to achieve accurate measurement of attitude angle and angular velocity. Accurate and efficient measurement of UAV attitude angle is the basis of UAV flight control. In this paper, the extended Kalman filter (EKF) algorithm is used to estimate the attitude information of the four-rotor UAV. Firstly, a four-rotor UAV simulation model is established on Simulink in Matlab, and then the attitude information of the UAV is measured and estimated. The results show that the extended Kalman filter algorithm can effectively estimate the attitude information of UAV.