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 classification accuracy. Extensive experiments were conducted both in anechoic chambers with 43 tag configurations and outdoors under realistic flight trajectories and noise conditions. Dimensionality reduction techniques, including Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP), provided insight into code separability and robustness. Our results demonstrate reliable drone classification performance at signal-to-noise ratios as low as 7 dB, indicating the feasibility of long-range detection with advanced surveillance radar systems. Preliminary range estimations indicate potential operational distances of several kilometers, suitable for critical applications such as airport airspace monitoring. The integration of electromagnetic tagging with machine learning enables scalable and efficient drone identification, paving the way for enhanced aerial traffic management and security in increasingly congested airspaces.

翻译：无人机的快速部署对空域管理、安全与监控提出了重大挑战。当前基于摄像头、激光雷达和传统雷达系统的检测与分类技术，常难以在不同环境条件及远距离下可靠识别与区分无人机，特别是相似型号的无人机。此外，较低的雷达散射截面和杂波干扰进一步增加了准确识别无人机的难度。为应对这些局限，本文提出一种基于人工微多普勒特征的无人机分类新方法，该特征由附着在无人机旋翼上的谐振电磁贴片编码产生。这些标签能生成独特且与配置相关的雷达回波，从而实现鲁棒识别。我们开发了一种专用的卷积神经网络（CNN），能够处理原始雷达信号，并实现高分类准确率。我们在无回波室中采用43种标签配置，并在室外真实飞行轨迹与噪声条件下进行了大量实验。通过主成分分析（PCA）和均匀流形逼近与投影（UMAP）等降维技术，深入探究了编码的可分离性与鲁棒性。实验结果表明，在信噪比低至7 dB时仍能实现可靠的无人机分类性能，这证明了利用先进监视雷达系统进行远距离检测的可行性。初步距离估计显示潜在工作距离可达数公里，适用于机场空域监控等关键应用。电磁标签与机器学习的结合，实现了可扩展且高效的无人机识别，为日益拥挤的空域中提升空中交通管理与安全水平开辟了新途径。