For several decades, aircraft have been extensively used in both civil and military applications. Currently, we are witnessing a proliferation of Unmanned Aerial Vehicles (UAVs) with various shapes in both civil and military domains for different purposes. For example, drones can be used in the production of cinema movies as well as for precise offensive strikes on the battlefield. However, these UAVs are usually smaller than modern fighter aircraft and have a very low Radar Cross Section (RCS), which prevents radars from reliably detecting them. This implies significant security issues, as inexpensive drones can be used for area surveillance or offensive tactics. Such a threat has led governments, like those in Europe in 2019, to enact new laws to curb the increasing utilization of drones.

Nevertheless, it could be possible to enhance radar capability to detect and identify drones using the micro-Doppler effect, as many UAVs use propellers to move. Therefore, our problem lies in how to improve radars' capability to detect and identify drones in various environments (sea, urban area, forests, etc.) using the micro-Doppler signatures of targets. In this perspective, we focus on the detailed modeling and simulation of the micro-Doppler effect produced by drone-type targets and the characterization of the simulated radar signal based on time–frequency representations.

A few simulations have been carried out using physical optics methods and with the software FEKO in various simplified configurations. Preliminary results show that characteristic patterns appear in time–frequency representations depending on the parameters of the drone system (tilt, relative speed, blade rotation speed, blade dimensions, etc.) that could help in the detection and identification of drone-type targets.