🤖 AI Summary
This work addresses the challenge of autonomous three-dimensional wind field estimation for small unmanned aerial vehicles (UAVs), which is hindered by the limited accuracy of low-cost sensors and critical for energy-efficient flight. The authors propose a method that fuses an aerodynamic model with an extended Kalman filter (EKF) and incorporates an adaptive moving-average estimator (AMAE). Relying solely on standard onboard IMU and GNSS measurements—without requiring additional anemometers or angle-of-attack/sideslip sensors—the approach enables real-time, accurate estimation of both steady-state and time-varying 3D wind fields. Designed to balance computational efficiency and estimation smoothness, the method has been validated through comprehensive simulations and real-flight experiments, demonstrating its suitability for resource-constrained embedded platforms. The study also systematically analyzes the impact of model inaccuracies and real-world uncertainties on estimation performance.
📝 Abstract
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.