{\mu}DopplerTag: CNN-Based Drone Recognition via Cooperative Micro-Doppler Tagging
O.Yerushalimov, D.Vovchuk, A.Glam, and P.Ginzburg

TL;DR
This paper introduces a CNN-based drone recognition system utilizing micro-Doppler signatures generated by electromagnetic tags on drone blades, enabling reliable classification even at low signal-to-noise ratios and long ranges.
Contribution
It presents a novel electromagnetic tagging method combined with deep learning for robust drone identification under challenging environmental conditions.
Findings
High classification accuracy at SNR as low as 7 dB
Effective differentiation of 43 tag configurations in experiments
Potential operational detection range of several kilometers
Abstract
The rapid deployment of drones poses significant challenges for airspace management, security, and surveillance. Current detection and classification technologies, including cameras, LiDAR, and conventional radar systems, often struggle to reliably identify and differentiate drones, especially those of similar models, under diverse environmental conditions and at extended ranges. Moreover, low radar cross sections and clutter further complicate accurate drone identification. To address these limitations, we propose a novel drone classification method based on artificial micro-Doppler signatures encoded by resonant electromagnetic stickers attached to drone blades. These tags generate distinctive, configuration-specific radar returns, enabling robust identification. We develop a tailored convolutional neural network (CNN) capable of processing raw radar signals, achieving high…
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Taxonomy
TopicsUAV Applications and Optimization · Advanced SAR Imaging Techniques · Air Traffic Management and Optimization
