Micro-Doppler Signature Classification of Unmanned Aerial Vehicles Using FMCW Radar

Unmanned aerial vehicles are seeing greater use as civil disruption and as military equipment. Strong classification techniques are necessary to effectively counter them. It is possible to classify targets that are detected by radar systems, including UAVs, using the micro-Doppler signature produced by the different moving structural components of the target. This paper demonstrates the strong performance of UAV classification using machine learning in conditions with high signal-to-noise ratios and explores the issues related to this approach.