Detecting Airborne Objects with 5G NR Radars

TL;DR

This paper explores using 5G NR signals for detecting airborne objects like UAVs, demonstrating feasibility with a full processing chain and analyzing performance across different urban environments.

Contribution

It introduces a 5G NR radar processing framework for UAV detection and provides simulation results in urban scenarios, including open-source software for reproducibility.

Findings

Performance varies with environment, with higher missed detection in UMi due to clutter.

Position error increases with target distance and altitude.

Achieves 4m accuracy in UMi and 8m in UMa environments.

Abstract

The integration of sensing capabilities into 5G New Radio (5G NR) networks offers an opportunity to enable the detection of airborne objects without the need for dedicated radars. This paper investigates the feasibility of using standardized Positioning Reference Signals (PRS) to detect UAVs in Urban Micro (UMi) and Urban Macro (UMa) propagation environments. A full 5G NR radar processing chain is implemented, including clutter suppression, angle and range estimation, and 3D position reconstruction. Simulation results show that performance strongly depends on the propagation environment. 5G NR radars exhibit the highest missed detection rate, up to 16%, in UMi, due to severe clutter. Positioning error increases with target distance, resulting in larger errors in UMa scenarios and at higher UAV altitudes. In particular, the system achieves a position error within 4m in the UMi environment and within 8m in UMa. The simulation platform has been released as open-source software to support reproducible research in integrated sensing and communication (ISAC) systems.