Detecting Airborne Objects with 5G NR Radars

2025-05-30

AI Summary

This work addresses the challenge of radar-free UAV detection in urban microcell (UMi) and macrocell (UMa) environments using standardized 5G NR Positioning Reference Signals (PRS). We propose an end-to-end native radar processing chain integrating time-frequency joint clutter suppression, MUSIC-based angle estimation, matched-filter ranging, and geometric triangulation. To our knowledge, this is the first systematic validation—under standardized 3GPP channel models—of 5G NR native radar capability for aerial target detection. A fully open-source simulation platform is released to enable reproducible ISAC research. Experimental results show positioning errors ≤4 m (with ≤16% missed-detection rate) in UMi scenarios and ≤8 m in UMa scenarios; errors increase with target range and altitude. The core contribution lies in the first demonstration of standardized PRS waveforms as high-accuracy aerial sensing enablers, advancing practical integration of communication and sensing (ISAC).

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Intelligent Robots: State Estimation
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Detecting UAVs using 5G NR signals in urban environments
• Evaluating performance of 5G NR radar in UMi and UMa scenarios
• Assessing positioning accuracy and clutter impact on detection

Innovation

• Uses 5G NR signals for airborne object detection
• Implements PRS-based radar processing chain
• Achieves 4m-8m positioning accuracy in urban environments