Estimation architecture for future autonomous vehicles

An architecture for the development of on-line models to support future uninhabited aerial vehicles is developed. The architecture is based on a new filter called the Unscented Kalman Filter that approximates the state and noise stochastic distributions, rather than the dynamics. A square root version of the Unscented Kalman Filter is shown to have better characteristics for on-line implementation than traditional methods, such as less sensitivity to tuning, initial conditions, and sample frequency. The estimation methodology is shown to be able to estimate the nonlinear state and model parameters for an aircraft during failure, and to generate aerodynamic models with potential application to on-line control reconfiguration.