Vehicle motion improves confidence in drone wind sensing measurements
Vehicle-State-Aware Confidence Estimation for Direct Wind Sensing on a Multirotor
Robotics
Summary
Measuring wind directly from drones can be tricky because the drone's own movement affects the readings. The authors developed a way to estimate how reliable each wind measurement is by looking at the drone’s horizontal and vertical speeds. This confidence score helps adjust the wind calculations to be more accurate during drone movements like flying forward or hovering. Their tests showed this method can successfully identify unreliable data and improve wind estimates.
What this means in practice
- •For drone operators: Improve wind measurement accuracy during various flight modes by adjusting sensor confidence based on drone motion.
- •For weather monitoring teams: Obtain more reliable localized atmospheric wind data from drones by using vehicle-state-aware confidence to filter unreliable readings.
Authors
Sri Arjun Senthil Kumar, April Zi Qian Chan, Chen-Hua Yeow
Abstract
Direct wind sensing from a multi-rotor UAV enables localized atmospheric measurements, but the reliability of the anemometer depends on vehicle motion. This work presents a vehicle-state-aware confidence method that uses horizontal and vertical vehicle speed to quantify the acquisition of each wind observation. The resulting confidence score is used to adapt measurement influence to a two-dimensional vector estimator. Experiments were conducted during horizontal translation, vertical motion, and held-out autonomous waypoint flight. The proposed two-feature formulation achieved an ROC-AUC of 0.995, with 98.84% of translation observations identified as unreliable. The confidence-aware adaptation reduced median estimator gain during translation from 0.207 to 0.064 while retaining median gain of 0.151 during hover. The results show that vehicle state provides useful proprioceptive context for regulating the influence of direct airborne wind observations.