Radar Detection Performance Prediction Using Measured UAVs RCS Data

This is the authors' abstract. We don't add key points for this paper.

This article presents measurements of radar cross section (RCS) of five unmanned aerial vehicles (UAVs), comprising both consumer grade and professional small drones, collected in a semicontrolled environment as a function of azimuth aspect angle, polarization, and frequency in the range 8.2–18 GHz. The experimental setup and the data preprocessing, which include coherent background subtraction and range gating procedures, are illustrated in detail. Furthermore, a thorough description of the calibration process, which is based on the substitution method, is discussed. Then, a first-order statistical analysis of the measured RCSs is provided by means of the Cramér-von Mises (CVM) distance and the Kolmogorov–Smirnov (KS) test. Finally, radar detection performance is assessed on both measured and bespoke simulated data (leveraging the results of the developed statistical analysis), including, as benchmark terms, the curves for nonfluctuating, and Rayleigh fluctuating targets.