The success of drone missions is incumbent on an accurate determination of the drone pose and velocity, which are collectively estimated by fusing inertial measurement unit and global navigation satellite system (GNSS) measurements. However, during a GNSS outage, the long-term accuracy of these estimations are far from allowing practical use. In contrast, vehicle dynamic model (VDM)-based navigation has demonstrated significant improvement in autonomous positioning during GNSS outages. This improvement is achieved by incorporating mathematical models of aerodynamic forces/moments in the sensor fusion architecture. Such an approach, however, relies on a knowledge of aerodynamic model parameters, specific to the operating vehicle. We present a novel calibration algorithm to identify these parameters from the flight data of two geometrically different drones. The identified parameters, when used in the VDM framework, show a significant reduction in navigation drift during GNSS outages. Moreover, the obtained results show that the proposed algorithm is independent of the choice of fixed-wing platform and prior knowledge of aerodynamics.

The full paper is available for free here: https://navi.ion.org/content/70/4/nav...