Trajectory Reconstruction of Launch Vehicle in Atmospheric Flight using the Unscented Kalman Filter

In this paper the performance of a smoothed Unscented Kalman filter is analyzed for the determination of the best-estimated trajectory of a launch vehicle in atmospheric flight. A kinematic formulation of the filter is considered, that makes use of vehicle and ground-based measurements, where acceleration and angular velocity measured by an Inertial Measurement Unit are treated as inputs in the filter model, and the states include uncertainty parameters such as IMU biases. Results for state estimation and related uncertainty are presented and discussed for a case study involving the first-stage flight of the ARES I launch vehicle, where synthetic measurement data are generated by a nonlinear high-fidelity dynamic model of the vehicle, and the estimates obtained by the proposed method are compared to those evaluated using the Extended Kalman filter algorithm.