Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies

TL;DR

This paper develops a radar cross-section based statistical system for UAV identification at microwave frequencies, demonstrating high classification accuracy using statistical models fitted to RCS measurements.

Contribution

It introduces a novel UAV recognition approach using RCS measurements and statistical modeling, with detailed analysis of model suitability and system performance evaluation.

Findings

Lognormal, generalized extreme value, and gamma distributions best model UAV RCS.

The recognition system achieves over 97% accuracy at 10 dB SNR.

RCS depends on shape, size, material, azimuth angle, frequency, and polarization.

Abstract

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.