CubeDN: Real-time Drone Detection in 3D Space from Dual mmWave Radar Cubes

Advanced search
Browse by:

Department | Year

UCL Theses | Latest

Deposit your research

CubeDN: Real-time Drone Detection in 3D Space from Dual mmWave Radar Cubes

Fang, Yuan; Shi, fangzhan; Wei, xijia; Chen, Qingchao; Julier, Simon; Chetty, Kevin; (2025) CubeDN: Real-time Drone Detection in 3D Space from Dual mmWave Radar Cubes. In: Proceedings of the 2025 IEEE International Conference on Robotics and Automation (ICRA). Institute of Electrical and Electronics Engineers (IEEE) (In press). Green open access

[thumbnail of ICRA25_1026_Submitted.pdf]

Preview

Text
ICRA25_1026_Submitted.pdf - Accepted Version
Download (590kB) | Preview

Abstract

As drone use has become more widespread, there is a critical need to ensure safety and security. A key element of this is robust and accurate drone detection and localization. While cameras and other optical sensors like LiDAR are commonly used for object detection, their performance degrades under adverse lighting and environmental conditions. Therefore, this has generated interest in finding more reliable alternatives, such as millimeter-wave (mmWave) radar. Recent research on mmWave radar object detection has predominantly focused on 2D detection of road users. Although these systems demonstrate excellent performance for 2D problems, they lack the sensing capability to measure elevation, which is essential for 3D drone detection. To address this gap, we propose CubeDN, a single-stage end-to-end radar object detection network specifically designed for flying drones. CubeDN overcomes challenges such as poor elevation resolution by utilizing a dual radar configuration and a novel deep learning pipeline. It simultaneously detects, localizes, and classifies drones of two sizes, achieving decimeter-level tracking accuracy at closer ranges with overall 95% average precision (AP) and 85% average recall (AR). Furthermore, CubeDN completes data processing and inference at 10Hz, making it highly suitable for practical applications.

Type:	Proceedings paper
Title:	CubeDN: Real-time Drone Detection in 3D Space from Dual mmWave Radar Cubes
Event:	IEEE International Conference on Robotics and Automation (ICRA)
Location:	Atlanta, GA, USA
Dates:	19th-23rd May 2025
Open access status:	An open access version is available from UCL Discovery
Publisher version:	https://ieeexplore.ieee.org/Xplore/home.jsp
Language:	English
Additional information:	This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science
URI:	https://discovery.ucl.ac.uk/id/eprint/10204066