用低成本传感器实现小型无人机实时风速估测
Onboard Wind Estimation for Small UAVs Equipped with Low-Cost Sensors: An Aerodynamic Model-Integrated Filtering Approach
- 融合气动模型与扩展卡尔曼滤波，仅靠基础传感器估算风况
- 仿真验证可准确估计稳态及变化的三维风矢量
- 适合需节能飞行的微型无人机，尤其适合资源受限场景
为实现小型无人飞行器（UAV）在能源高效飞行中的自主风场感知，本文提出一种仅依赖自主飞行所必需的低成本基础传感器、无需额外风速测量设备的风速估测方法。核心包括结合气动模型的扩展卡尔曼滤波（EKF）与自适应移动平均估测（AMAE）技术，显著提升风速估测精度与平滑性。仿真结果表明，该方法可在不依赖流角测量的情况下，有效估计稳态和时变的三维风矢量。同时分析了气动模型精度对风速估测误差的影响，评估其实际应用可行性。飞行试验验证了方法的有效性及其在机载实时计算中的可行性。此外，系统梳理了测试中遇到的不确定性与误差源，为后续优化提供依据。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。