UAV MOTION MODEL AND ESTIMATION OF ITS UNCERTAINTIES WITH FLIGHT TEST DATA

摘要

In this paper, nominal longitudinal linear model of the UAV is identified with the use of combination of two different methods called Estimation before Modeling (EBM) and Estimation after Modeling (EAM), using Kalman filter and available measurements. Tuning of filter is one of the difficult stages of the estimation using Kalman filter. It can be made easier to tune Kalman filter by using the EBM method because in this method estimation and modeling of aerodynamic forces and moments are done in two stages. In the EBM method at the first-stage aerodynamic forces and moments are estimated without a priori structure and in the second stage estimated forces and moments are modeled versus suitable state variables. Dimension of augmented state vector for Kalman filter is reduced by using the EBM method. Here we suggested a combination of the EBM and EAM methods to achieve observability and simplicity of filter tuning. We used linear accelerometer out of the center of gravity purposely to measure the linear and angular accelerations to achieve better observability. Bounds of uncertainties for estimated aerodynamic coefficients are calculated using diagonal elements of covariance matrix.