Model-Aided Inertial Navigation Applied to Wind Estimation for a Fixed Wing UAV

The flight of unmanned aerial vehicles is often associated with model uncertainties, measurement noises, and environmental disturbances such as wind gust. To mitigate these challenges, the accurate estimation of states is vital. Moreover, the wind model and its parameters should also be estimated and compensated during the flight. In this paper, a model-aided inertial navigation is implemented for this purpose. To investigate the performance of the model-aided inertial navigation, two different models including constant wind and “1-cosine” model are considered. The model-aided inertial navigation integrates the output from a dynamics model of unmanned aerial vehicle in the navigation system to simultaneously estimate the model of wind as well as the current states. The results show that the model-aided inertial navigation provides good performance and the wind model is properly estimated. Moreover, small estimation errors, obtained from the simulations, prove the good performance of this approach in estimation of states and wind model.