The success of drone missions is incumbent on an accurate determination of the drone pose and velocity, which are collectively estimated by fusing iner- tial measurement unit and global navigation satellite system (GNSS) mea- surements. 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.