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This paper introduces an Extended Kalman Filter (EKF) integrated with an aerodynamic model and Adaptive Moving Average Estimation (AMAE) to estimate 3D wind vectors for small UAVs using only low-cost onboard sensors. The method avoids the need for specialized wind measurement devices or flow angle sensors, relying instead on fusing sensor data with aerodynamic modeling. Both simulations and flight tests demonstrate the approach's ability to accurately estimate steady and time-varying wind conditions in real-time.
Ditching expensive anemometers, this work shows how to estimate 3D wind conditions for UAVs using only the cheap sensors already onboard.
To enable autonomous wind estimation for energy-efficient flight in small unmanned aerial vehicles (UAVs), this study proposes a method that estimates flight states and wind using only the low-cost essential onboard sensors required for autonomous flight, without relying on additional wind measurement devices. The core of the method includes an Extended Kalman Filter (EKF) integrated with the aerodynamic model and an Adaptive Moving Average Estimation (AMAE) technique, which improves the accuracy and smoothness of the wind estimation. Simulation results show that the approach efficiently estimates both steady and time-varying 3D wind vectors without requiring flow angle measurements. The impact of aerodynamic model accuracy on wind estimation errors is also analyzed to assess practical applicability. Flight tests validate the effectiveness of the method and its feasibility for real-time onboard computation. Additionally, uncertainties and error sources encountered during testing are systematically examined, providing a foundation for further refinement.