Realtime Wind Estimation using Low Cost Quadrotor Uncrewed Aerial Vehicles
2026-06-29 • Robotics
Robotics
AI summaryⓘ
The authors explain that measuring wind speed is important for things like monitoring the environment and handling emergencies like wildfires. They focus on using small flying drones called quadrotors to estimate wind velocity, but note that common methods like the Extended Kalman Filter (EKF) have trouble with complex, nonlinear situations. To fix this, the authors propose using a different method called the Unscented Kalman Filter (UKF), which better handles nonlinear drone movements and keeps the flight path stable. Through simulations, they show that the UKF performs better than the EKF when conditions get complicated, making it a promising tool for accurate wind measurements.
Quadrotor UAVWind velocity estimationExtended Kalman Filter (EKF)Unscented Kalman Filter (UKF)Nonlinear dynamicsSE(3) groupGeometric controllerEnvironmental monitoringTrajectory tracking
Authors
Hiranya Udagedara, Mahdis Bisheban
Abstract
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.