The Impact of Payload on Radar Detection: A Comprehensive RCS Analysis of Drones Operating In 2.4 Ghz and 5.2 Ghz Bands

Abstract

The rise of unmanned aerial vehicles (UAVs) poses significant airspace security challenges, particularly concerning radar detection capabilities. This study examines the radar cross-section (RCS) of drones operating in LTE frequency bands (2.4 GHz and 5.2 GHz), using the DJI Phantom 3 as a case study. Simulations conducted in CST Studio Technology reveal that payload weight substantially affects RCS, with increased loads resulting in higher radar visibility. Additionally, angle-dependent analyses demonstrate significant variations in RCS based on the angle of incidence, identifying critical angles for enhanced detectability. These findings underscore the necessity for advanced radar detection strategies that consider payload impacts and angle-specific characteristics of RCS. The study's implications extend to the optimization of UAV designs for reduced radar signatures, informing both manufacturers and regulatory frameworks. This research contributes to the development of effective airspace monitoring systems, emphasizing the need for adaptive detection methodologies as UAV usage continues to expand.