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Metasurface-Enabled Electromagnetic License Plate for Drone Identification and Motion Tracking
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AI
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DOI：10.1002/lpor.71714.png)
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Abstract
En 中文
The explosive growth of the low-altitude economy has positioned drones as essential components of modern airspace. However, the reliable identification and tracking of “low-altitude, small, and slow” objects remain challenging. Existing monitoring approaches often rely on active cooperation with high energy consumption, or degrade under adverse environmental conditions, constraining their applicability in complex urban environments. Here we propose MetaLicPlate, a low-cost, zero-power, and multifunctional metasurface license plate for joint drone identification and motion tracking. Mounted passively on a drone, MetaLicPlate simultaneously encodes identity and motion information into its electromagnetic time–frequency response. A time–frequency multiplexing framework allows a ground station to extract drone identity from frequency-domain echoes using a convolutional neural network, while estimating distance and velocity from time-domain signals in real time. Indoor and outdoor experiments demonstrate high identification accuracy and precise motion estimation, highlighting MetaLicPlate as a potential passive platform for integrated drone monitoring in dense low-altitude airspace.
Keywords:
convolutional neural network
drone identification
drone motion recognition
Low-altitude economy
metasurface
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