Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies

This article presents a radar cross-section (RCS)-based statistical recognition system for identifying/classifying unmanned aerial vehicles (UAVs) at microwave frequencies. First, the results of vertical (VV) and horizontal (HH) polarization RCS measurement six commercial UAVs 15 25 GHz in compact range anechoic chamber. The show that average depends on shape, size, material composition target UAV as well azimuth angle, frequency, illuminating radar. Afterward, characterization is achieved by fitting data to 11 different models. From model selection analysis, we observe lognormal, generalized extreme value, gamma distributions are most suitable modeling while Gaussian distribution performed relatively poorly. best statistics forms class conditional probability densities proposed system. performance evaluated signal noise ratio (SNR) with aid Monte Carlo analysis. At an SNR 10 dB, classification accuracy 97.60% or better achievable.