State Estimation of a Nonlinear Unmanned Aerial Vehicle Model using an Extended Kalman Filter

An Extended Kalman Filter is designed in order to estimate both state variables and wind velocity vector at the same time for a non conventional unmanned aircraft. The proposed observer uses few measurements, obtained by means of either conventional simple air data sensors or a low cost GPS. To cope with the low rate of the GPS with respect to the other sensors, the EKF algorithm has been modified to allow for a dual rate measurement model. State propagation is obtained by means of an accurate six degrees of freedom nonlinear model of the aircraft dynamics. To obtain joint estimation of state and disturbance, wind velocity components are included in the set of the state variables. Both stochastic and deterministic turbulence models have been applied to the studied aircraft flying in unsymmetrical flight. The obtained results have shown the effectiveness of the designed observer. Besides, the accurate state reconstruction allows to use the estimated ariables for control purposes. Copyright