  We propose the use of basic inertial measurement units (IMU) which contain
sensors such as accelerometers and gyroscopes already on-board drones to detect
the speed and direction of wind gusts. The ability to quickly sense wind gusts
has many applications, the most notable of which is in flight assistance of the
drone, where it may adjust motor power parameter to compensate for such
external factors or steer the drone toward a safer direction. To illustrate the
feasibility of the approach, we conducted studies to assess how reliably wind
speed and wind direction can be detected while a quad-copter drone is hovering
and then in-motion, using off-the-shelf classifiers. Empirical results with
real-life data, collected on a micro aerial vehicle (MAV) in a physical room
with a consumer-grade fan, show that (1.1) wind speed can be detected with high
accuracy after training, not only on the same drone, but also across different
drones of the same class, while (1.2) wind direction can be detected with high
accuracy after training on the same drone, but with limited generalizability to
other drones. (1.3) We demonstrate how real-time detection of wind speed, using
offline trained models, is feasible and can be done with high accuracy. (2.1)
Finally, we find the reason behind the lower accuracy for wind direction
detection during the analysis of drones in-motion.
