Exploring the Dynamic RCS of Small Drones Using FMCW Radar

In low-altitude airspace, developing suitable techniques to distinguish between objects is essential for ensuring efficient and reliable surveillance. When using radar systems for surveillance, one of the most critical parameters characterizing a target is its Radar Cross Section (RCS). In fact, potentially hostile small drones typically exhibit a very small RCS (on the order of 0.01 square meters), posing challenges in distinguishing them from other non-hostile flying objects, such as birds. Therefore, gaining further insights into possible signatures in the RCS of drones is pivotal for developing bespoke detection strategies and suitable classification techniques. Therefore, the aim of this paper is to characterize the dynamic RCS of small flying drones by leveraging measurements collected via a Frequency Modulated Continuous Wave (FMCW) radar operating in the X-band, with a carrier frequency of 9.55 GHz. Several tests and measurements have been conducted using this system while two different sized drones, a DJI Matrice 300 and a DJI Mavic 2 Dual Enterprise, were flying within the radar surveillance area. At the analysis stage, The RCS fluctuations of each drone are analyzed by fitting the RCS data to various one- and two-parameter statistical models. The results reveal that different drones exhibit unique RCS fluctuation patterns, with each drone aligning with different statistical models that reflect their variations in size and structural complexity. These findings are crucial for developing tailored radar signal processing algorithms aimed at improving drone detection and tracking performance.