A Correction Algorithm for Propeller-Induced Airflow and Flight Attitude Changes during Three-Dimensional Wind Speed Measurements Made from A Rotary Unmanned Aerial Vehicle

Abstract. A hexacopter unmanned aerial vehicle (UAV) was fitted with a three-dimensional sonic anemometer to measure three-dimensional wind speed, air temperature, relative humidity, and pressure. To obtain accurate results for three-dimensional wind speeds, we developed an algorithm to correct biases caused by the propeller-induced airflow disturbance, UVA movement, and changes in flight attitude in the three-dimensional wind measurements. The wind measurement platform was built based on a custom-designed integration kit that couples seamlessly to the UAV, equipped with a payload and the sonic anemometer. Based on an accurate digital model of the integrated UAV-payload-anemometer platform, computational fluid dynamics (CFD) simulations were performed to quantify the wind speed disturbances caused by the rotation of the UAV's rotor on the anemometer during the UAV's steady flight under headwind, tailwind, and crosswind conditions. Through analysis of the simulated data, regression equations were developed to predict the wind speed disturbance, and the correction algorithm for rotor disturbances, motions, and attitude changes was developed. To validate the correction algorithm, we conducted a comparison study in which the integrated UAV system flew around a meteorological tower on which three-dimensional wind measurements were made at multiple altitudes. The comparison between the corrected UAV wind data and those from the meteorological tower demonstrated an excellent agreement. The corrections result in significant reductions in wind speed bias caused mostly by the propellers, along with notable changes in the dominant wind direction and wind speed in the original data. The algorithm enables reliable and accurate wind speed measurements in the atmospheric boundary layer made from rotorcraft UAVs.