Radar Detection Performance via Frequency Agility Using Measured UAVs RCS Data

This article addresses radar detection performance prediction (via measured data) for drone targets using a frequency agility-based incoherent (square-law) detector. To this end, a preliminary statistical analysis of the integrated radar cross section (RCS) resulting from frequency agile pulses is carried out for drones of different sizes and characteristics, using data acquired in a semi-controlled environment for distinct frequencies, angles, and polarizations. The analysis involves fitting the integrated RCS measurements with commonly used one-parametric and two-parametric probability distributions and leverages the Cramer-von Mises (CVM) distance and the Kolmogorov Smirnov test. Results show that the Gamma distribution appears to accurately model the resulting fluctuations. Hence, the impact of integration and frequency agility on the RCS fluctuation dispersion is studied. Finally, the detection performance of the incoherent square-law detector is assessed for different target and radar parameters, using both measured and simulated data drawn from a Gamma distribution whose parameters follow the preliminary RCS statistical analysis. The results highlight a good agreement between simulated and measurement-based curves.