用低成本无人机实时估算风速，提升复杂气象下监测精度。
Realtime Wind Estimation using Low Cost Quadrotor Uncrewed Aerial Vehicles
- 采用非线性无迹卡尔曼滤波（UKF）替代传统线性滤波，更好处理无人机动力学非线性问题。
- 在高非线性仿真条件下，UKF风速估计误差显著低于扩展卡尔曼滤波（EKF）。
- 适合需要高精度风速感知的野外环境监测与应急响应场景。
在环境监测及火灾等应急响应中，风速测量至关重要。四旋翼无人机因其机动性强、体积小、成本低，成为风速估计的常用平台。现有研究多基于扩展卡尔曼滤波（EKF）进行风速估计，但通常仅适用于悬停或近似直线轨迹，且受限于对非线性无人机动力学的线性化近似，在强风等高度非线性场景下精度不足。本文提出使用无迹卡尔曼滤波（UKF）这一非线性估计算法，在保持四旋翼飞行轨迹的同时实现更精确的风速估计。无人机模型基于特殊欧氏群SE(3)，通过几何控制器维持飞行路径，并在数值仿真中验证方法有效性。结果表明，随着仿真非线性程度增加，UKF始终优于EKF，展现出在复杂风场下的可靠性能，能在最小偏离轨迹的前提下提供准确风速估计。
原文摘要 · Abstract (English)
In environmental monitoring as well as emergency response applications such as wildfires, wind velocity measurement is essential. Quadrotor UAVs have become popular platforms for wind velocity estimation due to their maneuverability, compact size, and cost-effectiveness. Numerous studies use the Extended Kalman Filter (EKF) to estimate the wind velocity based on the quadrotor dynamic model. However, most of them use hovering quadrotors only for wind estimation, others use a near-linear trajectory to estimate near-constant velocities. Furthermore, EKF performance is constrained by its reliance on linearized approximations of the nonlinear quadrotor dynamics around current states, limiting accuracy in highly nonlinear scenarios, including windy conditions. This study proposes the use of an Unscented Kalman Filter (UKF), a nonlinear estimator to provide accurate wind estimations while maintaining the trajectory of the quadrotor UAV. The quadrotor is modeled on the Special Euclidean group SE(3) and the approach is evaluated through numerical simulations using a geometric controller to maintain quadrotor flight paths. The results indicate that as the nonlinearity of the simulation increases, the UKF consistently outperforms the EKF. This demonstrates the potential of the UKF as a reliable estimator for highly nonlinear scenarios, capable of maintaining the trajectory with minimal deviation while providing accurate wind velocity estimations.
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