LAUSR

This study investigates a means through which commercially available computational electromagnetic modeling software can be used to predict radar cross sections (RCSs) of airborne organisms of interest as a preliminary step toward enabling detection and tracking of these organisms. This work aims to analyze this framework for the specialized case of the honey bee (Apis mellifera), given its critical role in food security as a major pollinator of agricultural crops. A Method-of-Moment (MoM) solver made available by Altair’s FEKO is used to conduct the analysis over varying frequencies, illumination angles, and polarizations. A high degree of correlation between measured and modeled cross sections is noted. Maximum RCS root-mean-square errors (RMSEs) between the two are approximately 4 and 5 dB relative to 1 $\text{m}^{2}$ (dBsm) for Horizontal polarization (H-pol) and Vertical polarization (V-pol) X-band measurements, respectively. Findings of this study also highlight the sensitivity of both modeled and measured RCS estimates to the dielectric properties of honey bees and the corrupting effects that this may have if not accounted for accurately, where errors are shown to increase from 2 to 5 dBsm, but without significantly corrupting the overall RCS azimuth profile.