This study aims to identify an optimal, as well as practical, parametric structure for a delta-wing UAV aerodynamic model for the purpose of model-based navigation. We present a comprehensive procedure for characterizing the aerodynamics of this platform, utilizing a hybrid approach that combines open-air wind-tunnel experiments with the processing of real flight data using filter error method. The experimental design employs Latin Hypercube Sampling to maximize the observability of aerodynamic coefficients while adhering to time constraints. Candidate aerodynamic models are selected through step-wise regression. Numerical values for model coefficients are determined experimentally and subsequently calibrated through a two-phase procedure using real flight data. We then compare these models by assessing their effectiveness in improving navigation in the absence of GNSS signal in four different test flights, with respect to conventional inertial coasting using the autopilot IMU. The experimental evidence demonstrates that the model-based navigation, utilizing the proposed aerodynamic model structures, significantly reduces positioning errors compared to traditional navigation methods during GNSS outages.