Airspeed is a term used to describe how fast an airplane is flying. In flight, airspeed has to be measured very accurately because the safe and efficient operation of an aircraft depends on it. The system (sensors and software) used to measure airspeed is called an air data system. After installation and before its first use, an air data system must be calibrated. Conventionally, airspeed calibrations are done by mounting another calibrated air data system with sensors mounted on long poles jutting out of the leading edge of the airplane wing or the nose. This calibration procedure is a complex and time-consuming process, which requires expensive and cumbersome equipment. It is not suited for small airplanes such as unmanned aerial vehicles (UAVs). The aim of this research is to explore ways of using GPS to calibrate air data systems. The main goal of this project was to produce a MATLAB algorithm that relates the accurate GPS velocities to the airspeed measurements on board the UAV in order to calibrate the airspeed sensors on the UAV. It was shown that the MATLAB algorithm produced could determine the errors of the airspeed sensors on the UAV, as well as the wind conditions when the UAV flight test was conducted. The airspeed errors were verified to be correct. The estimates of the wind speeds and directions generated by the MATLAB algorithm generally matched the average wind conditions by the National Weather Service on that day. The creation of this airspeed calibration MATLAB algorithm is definitely a step forward in the airspeed calibration field, because it not only simplifies the whole airspeed calibration process, but also is able to determine the wind conditions encountered by the UAV.