μDopplerTag: 基于协作微多普勒标记的CNN无人机识别
μDopplerTag: CNN-Based Drone Recognition via Cooperative Micro-Doppler Tagging
AI总结：
本文提出基于电磁标签和CNN的无人机识别方法，利用微多普勒签名实现远距离高精度分类，适用于空域监控等关键应用。
摘要：
无人机的快速部署对空域管理、安全和监视带来了重大挑战。当前的检测和分类技术，包括摄像头、LiDAR和传统雷达系统，往往难以可靠地识别和区分无人机，尤其是型号相似的无人机，在多样的环境条件和远距离下尤为困难。此外，低雷达截面和杂波进一步加剧了准确识别无人机的难度。为了解决这些限制，我们提出了一种基于人工微多普勒签名的新型无人机分类方法，该方法通过附着在无人机旋翼上的谐振电磁标签编码。这些标签生成独特的、配置特定的雷达回波，能够实现稳健的识别。我们开发了一种定制的卷积神经网络（CNN），能够处理原始雷达信号，实现高分类精度。在消音室中进行了大量实验，使用了43种标签配置，并在户外真实飞行轨迹和噪声条件下进行了测试。降维技术，包括主成分分析（PCA）和统一流形近似和投影（UMAP），提供了对代码分离性和稳健性的见解。我们的结果表明，在信号噪声比仅为7 dB的情况下，能够实现可靠的无人机分类性能，表明了使用先进监视雷达系统进行远距离检测的可行性。初步的范围估计表明，潜在的操作距离可达数公里，适用于机场空域监控等关键应用。电磁标记与机器学习的结合实现了可扩展且高效的无人机识别，为日益拥堵的空域管理与安全提升铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
