Aerodynamic parameter estimation of an unmanned aerial vehicle based on extended Kalman filter and its higher order approach

Aerodynamic parameter estimation provides an effective way for aerospace system modelling using measured data from flight test, especially for the purpose of developing elaborate simulation environments and control systems design of Unmanned Aerial Vehicle (UAV) with short design cycles and reduced cost. However, parameter identification of airplane dynamics is complicated because of its nonlinear identification models and the combination of noisy and biased sensor measurements. The combined difficulties mentioned above make the problem of state and parameter estimation a nonlinear filtering problem. Extended Kalman Filter (EKF) is an excellent tool for this matter with the property of recursive parameter identification and excellent filtering. The standard EKF algorithm is based on a first order approximation of system dynamics. More refined linearization techniques such as iterated EKF can be used to reduce the linearization error in the EKF for highly nonlinear systems, which leads to a theoretically better result. In this paper we concentrate on the application and comparison of EKF and iterated EKF for aerodynamic parameter estimation of a fixed wing UAV. The result shows that the two methods have been able to provide accurate estimations.