  This paper presents a radar cross-section (RCS)-based statistical recognition
system for identifying/ classifying unmanned aerial vehicles (UAVs) at
microwave frequencies. First, the paper presents the results of the vertical
(VV) and horizontal (HH) polarization RCS measurement of six commercial UAVs at
15 GHz and 25 GHz in a compact range anechoic chamber. The measurement results
show that the average RCS of the UAVs depends on shape, size, material
composition of the target UAV as well as the azimuth angle, frequency, and
polarization of the illuminating radar. Afterward, radar characterization of
the target UAVs is achieved by fitting the RCS measurement data to 11 different
statistical models. From the model selection analysis, we observe that the
lognormal, generalized extreme value, and gamma distributions are most suitable
for modeling the RCS of the commercial UAVs while the Gaussian distribution
performed relatively poorly. The best UAV radar statistics forms the class
conditional probability densities for the proposed UAV statistical recognition
system. The performance of the UAV statistical recognition system is evaluated
at different signal noise ratio (SNR) with the aid of Monte Carlo analysis. At
an SNR of 10 dB, the average classification accuracy of 97.43% or better is
achievable.
