Identification of Aerodynamic Parameters of Unmanned Aerial Vehicles under Non-Gaussian Measurement Noise

Regarding the dependence of the traditional extended Kalman filter (EKF) algorithm on Gaussian measurement noise for identifying aerodynamic parameters in unmanned aerial vehicles (UAVs), an interacting noise model–based extended Kalman filter (INM-EKF) is proposed. Firstly, the longitudinal aerodynamic parameters of the unmanned aerial vehicle are selected as the identification object, and the identification system is modeled accordingly. The non-Gaussian measurement noise is approximated using a Gaussian mixture model. Subsequently, the expectation-maximization algorithm is employed to estimate the parameters of this model. Filtering calculations are then performed for multiple identification systems that incorporate Gaussian noise. The estimated values of the aerodynamic parameters are obtained by fusing the calculated results from each model. Finally, simulation experiments were conducted to compare the performance of the proposed method with three existing filtering techniques in environments characterized by Gaussian mixture noise and α-stable noise. The results indicate that the INM-EKF method outperforms the other techniques in terms of identification accuracy and convergence speed, significantly enhancing the accuracy of aerodynamic parameter identification for unmanned aerial vehicles.