  Due to limitations in available sensor technology, unmanned aerial vehicles
(UAVs) lack an active sensing capability to measure turbulence, gusts, or other
unsteady aerodynamic phenomena. Conventional in situ anemometry techniques fail
to deliver in the harsh and dynamic multirotor environment due to form factor,
resolution, or robustness requirements. To address this capability gap, a
novel, fast-response sensor system to measure a wind vector in two dimensions
is introduced and evaluated. This system, known as `MAST' (for MEMS Anemometry
Sensing Tower), leverages advances in microelectromechanical (MEMS) hot-wire
devices to produce a solid-state, lightweight, and robust flow sensor suitable
for real-time wind estimation onboard a UAV. The MAST uses five
pentagonally-arranged microscale hot-wires to determine the wind vector's
direction and magnitude. The MAST's performance was evaluated in a wind tunnel
at speeds up to 5~m/s and orientations of 0 - 360 degrees. A neural network
sensor model was trained from the wind tunnel data to estimate the wind vector
from sensor signals. The average error of the sensor is 0.14 m/s for speed and
1.6 degrees for direction. Furthermore, 95% of measurements are within 0.36 m/s
error for speed and 5.0 degree error for direction. With a bandwidth of 570 Hz
determined from square-wave testing, the MAST stands to greatly enhance UAV
wind estimation capabilities and enable capturing relevant high-frequency
phenomena in flow conditions.
