The aim of this thesis is to describe the process of tuning and implementing an algorithm that estimates wind speed and airspeed parameters on an UAV (Unmanned Air Vehicle). This involves the use of an EKF (Extended Kalman Filter) which is a commonly used state estimation method. Furthermore, this also calculates other state variables of the state space model, which then can be fed to the autopilot, therefore enhancing the functioning of the system by providing filtered data instead of raw data.
A MATLAB code of the above EKF filter was already existing, but I did some additional debugging and adjustments because it had some flaws and was not working as expected on real life flight data. After all the changes, I tested it offline with real flight data and in a SIL environment. Unfortunately, HIL simulation was not available, so SIL validation was used instead. Moreover, I ran the MATLAB compiler to translate it into C code in order to make it implementable on the UAV hardware. Apart from some flaws, the results can be considered satisfactory.