Local wind velocities, angle of attack and lift coefficients of a fixed-wing unmanned aerial vehicle (UAV) are estimated by fusing kinematic, aerodynamic and stochastic wind models with data from an inertial measurement unit, a global navigation satellite system receiver and a pitot-static tube in a Moving Horizon Estimator. Experimental validation with two different UAVs and two sensor sets of different quality, show promising results for both wind velocity and angle of attack estimation.

We propose to estimate steady and turbulent wind velocities and aerodynamic coefficients of a fixed-wing Unmanned Aerial Vehicle (UAV) by using frequency separation as well as kinematic, aerodynamic and wind models combined in an Extended Kalman Filter (EKF). With these estimates it is possible to calculate the angle of attack and the magnitude of the airspeed. Avoiding the need for prior knowledge of UAV parameters, the proposed method utilizes only sensor information that is part of a standard sensor suite, which consists of a Global Navigation Satellite System (GNSS), an Inertial Measurement Unit (IMU) and a pitot-static tube, and attitude information obtained from these sensors. An observability analysis shows that attitude changes are necessary during the initialization phase and from time to time during the flight. Simulation results indicate that, with typical sensor accuracy, the estimates are close to the reference values of the aerodynamic coefficients and wind velocities and is capable of estimating the Angle of Attack with an Root Mean Square Error (RMSE) of 0.33°, the Sideslip Angle with an RMSE of 3.21° and the airspeed with an RMSE of 0.23 m/s

We propose a method to detect icing of the airfoil of a fixed-wing Unmanned Aerial Vehicle by using an aerodynamic coefficient estimator and ambient temperature and humidity sensors. The estimator uses the information provided by a standard autopilot sensor suite consisting of an IMU, GNSS and a pitot-static tube to estimate lift coefficients as well as steady and turbulent wind velocities. These sensor inputs are fused within an Extended Kalman Filter using frequency separation and kinematic, aerodynamic and wind models while avoiding the need for prior knowledge about the aircraft. Ambient temperature and humidity sensors are used to assess environmental conditions and if icing is suspected, a trigger signal to the estimator and the autopilot is generated. This signal is used to adjust the anticipated uncertainties of the estimated coefficients and, if permitted by the flight control system, to initialize a small altitude change in order to excite the estimator. Simulation results show that this method is able to separate clearly between iced and non-iced cases and can be used to significantly enhance the detection performance compared to only using temperature and humidity based information.

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Abstract-This paper presents a first assessment on the impact of atmospheric icing on the aerodynamic performance of fixed-wing UAVs. Numerical simulations were performed in order to evaluate the impact on lift and drag on a 2D airfoil for UAVs. The results show clear evidence that icing increases drag while decreasing lift and the maximum angle of attack. All these effects have negative impact on the maneuverability, stall behavior, range and general operational capabilities of UAVs. Additionally, these results were used in a flight simulator in order to allow the simulation of UAV flights in icing conditions and to study the impact of icing on energy consumption and autopilot responses. Results from the flight simulator show higher angles of attack and higher energy consumption when flying in icing conditions. This flight simulator provides a testbed for further research into in-flight ice detection for fixed-wing UAVs.