ICUAS 2020 Paper Abstract


Paper ThC1.3

Wenz, Andreas Wolfgang (Norwegian University of Science and Technology), Johansen, Tor Arne (Norweigian Univ. Of Sci. & Tech.)

Real-Time Moving Horizon Estimation of Air Data Parameters and Wind Velocities for Fixed-Wing UAVs

Scheduled for presentation during the Regular Session "Sensor Fusion" (ThC1), Thursday, September 3, 2020, 17:40−18:00, Macedonia Hall

2020 International Conference on Unmanned Aircraft Systems (ICUAS), September 1-4, 2020 (Postponed from June 9-12, 2020), Athens, Greece

This information is tentative and subject to change. Compiled on September 25, 2020

Keywords Sensor Fusion, Autonomy, Simulation


We present a real-time implementation of an es- timation algorithm for angle of attack, airspeed and wind velocities estimation on a single board computer. The estimator uses only sensor data from a standard fixed-wing UAV autopilot, which consists of a Global Navigation Satellite System receiver, an inertial measurement unit and a pitot-static tube. This sensor data is fused with a combination of kinematic, aerodynamic and stochstic wind models in a nonlinear moving horizon estimator using numerical optimization. An algorithmic differentiation toolbox and automatic code generation is used to create a real- time capable estimator which is able to run within a UAV on an on-board computer. Hardware in the Loop simulation results show that the latency of the estimator is significantly below the expected wind gust period and gives low root-mean-square estimation errors for angle of attack (0.29 degrees) , airspeed (0.21m/s) and wind velocities (0.44 m/s)



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