© 2021, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.High-fidelity aerodynamic dataset generation is one of the most significant components of the aircraft flight simulation, and, it is a time consuming and costly process. Data fusion techniques suggest that, instead of using high fidelity data for entire aerodynamic dataset, an incorporating combination of high-fidelity and low-fidelity data is a more cost-effective one. The objective of data fusion is to obtain high-fidelity dataset accuracy by combining less amount of high-fidelity dataset and more amount of low-fidelity dataset. In this paper, two different data fusion approaches, namely modified Variable-Complexity Modelling and co-Kriging, are applied to F-16 fighter aircraft. Wind tunnel test data is utilized as the high-fidelity dataset while data obtained by a semi-empirical approach (Digital Datcom) is used as the low-fidelity dataset.