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Aerodynamic Parameter Estimation for High-Performance Aircraft Using Extended Kalman Filtering

The estimation of aerodynamic coefficients in aircraft dynamic models from flight-test data is addressed in this paper. An extended Kalman filter (EKF), implementing the full nonlinear kinematics of the aircraft equations of motion, was used for this purpose. Flight-test data from NASA's X-31 Drop Model and High Angle-of-attack Research Vehicle (HARV) were analyzed. The EKF parameter estimates for the X-31 compared well with wind-tunnel data and flight-data results using other identification techniques. For the HARV, the assumption of pseudonoise in the parameter dynamic model substantially improved the state and parameter estimates. A residual correlation method was used to estimate the process noise intensity matrix for this aircraft's flight data.