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 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.
Abstract (translated)
无人机的快速部署给空中交通管理和安全监控带来了重大挑战。当前的检测和分类技术，包括摄像头、LiDAR 和传统雷达系统，在各种环境条件下尤其是在远距离上可靠地识别和区分相似型号的无人机方面往往遇到困难。此外，低雷达截面以及杂波进一步复杂化了准确的无人机识别问题。为了解决这些限制，我们提出了一种基于人工微多普勒特征的新颖无人机分类方法，这种特征是由贴附在无人机旋翼上的共振电磁标签编码产生的。这些标签能够产生具有独特配置特性的雷达回波信号，从而实现稳健的识别能力。 为此，我们开发了一个专门用于处理原始雷达信号的卷积神经网络（CNN），该网络实现了高准确度分类效果。我们在消声室中进行了广泛的实验，使用了43种不同的标签配置，并在室外环境中通过实际飞行轨迹和噪声条件下进一步测试了系统的性能。降维技术如主成分分析（PCA）和均匀流形逼近及投影（UMAP）为我们提供了代码分离性和鲁棒性方面的见解。 我们的研究结果表明，在信噪比低至7dB的情况下，该系统仍能可靠地进行无人机分类，这预示着使用先进的监控雷达系统在长距离范围内检测无人机是可行的。初步的距离估计显示可能的操作范围为数公里，这对于机场空域监测等关键应用而言非常合适。 通过将电磁标记技术与机器学习相结合，这种方案能够实现大规模且高效的无人机识别，从而为进一步优化空中交通管理和增强安全措施铺平道路，在日益拥挤的领空环境下尤为重要。
URL
https://arxiv.org/abs/2601.08042
