LUMEN：一种用于边缘设备上基于PSD的射频指纹识别的轻量级无人机多增强网络
LUMEN: A Lightweight UAV Multi-Enhanced Network for PSD-Based RF Fingerprinting on Edge Devices.
摘要(中文译文)
无人机（UAV）在边缘环境中的识别既需要高分类精度，也需要在轻量级硬件上实现高效的实时部署。本研究提出了LUMEN，一种面向资源受限单板计算机平台设计的轻量级无人机多增强网络。为实现高效的边缘部署，所提方法采用基于功率谱密度（PSD）的信号表示以及轻量级神经网络架构。LUMEN将多通道PSD堆叠与多尺度特征提取相结合，以捕获短期频谱变化和多分辨率射频模式。所提出的流程涵盖无人机射频数据采集、数据集构建、预处理、模型设计、对比评估以及在基于RK3582的边缘平台上的部署。在分类实验中，LUMEN在四种评估模型中取得了最佳性能，准确率达到0.975，而基线模型分别为0.775、0.876和0.942。在边缘部署测试中，该模型在30分钟连续运行期间保持了1.73 ms的平均推理延迟和578.95 FPS的吞吐量，同时表现出低CPU利用率、低内存占用和稳定的热行为。这些结果表明，LUMEN在边缘设备上实现了识别精度与运行效率之间的实用平衡，适用于实时无人机识别。
Abstract
Unmanned aerial vehicle (UAV) identification in edge environments requires both high classification accuracy and efficient real-time deployment on lightweight hardware. This study presents LUMEN, a Lightweight UAV Multi-Enhanced Network designed for resource-constrained single-board computer platforms. To enable efficient edge deployment, the proposed method adopts a power spectral density (PSD)-based signal representation together with a lightweight neural network architecture. LUMEN combines multi-channel PSD stacking with multi-scale feature extraction to capture both short-term spectral variations and multi-resolution RF patterns. The proposed pipeline covers UAV RF data collection, including UAV RF data collection, dataset construction, preprocessing, model design, comparative evaluation, and deployment on an RK3582-based edge platform. In the classification experiments, LUMEN achieved the best performance among the four evaluated models, reaching an accuracy of 0.975, compared with 0.775, 0.876, and 0.942 for the baseline models. In the edge deployment test, the model maintained an average inference latency of 1.73 ms and a throughput of 578.95 FPS during a 30-min continuous run, while showing low CPU utilization, low memory usage, and stable thermal behavior. These results demonstrate that LUMEN achieves a practical balance between identification accuracy and runtime efficiency for real-time UAV identification on edge devices.
如何引用
AMA
Min-Joo Yoon, Ki-Woong Park. LUMEN: A Lightweight UAV Multi-Enhanced Network for PSD-Based RF Fingerprinting on Edge Devices.. Sensors (Basel, Switzerland). 2026; doi:10.3390/s26103208.
APA
Min-Joo Yoon, Ki-Woong Park (2026). LUMEN: A Lightweight UAV Multi-Enhanced Network for PSD-Based RF Fingerprinting on Edge Devices.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s26103208
GB/T 7714
Min-Joo Yoon, Ki-Woong Park. LUMEN: A Lightweight UAV Multi-Enhanced Network for PSD-Based RF Fingerprinting on Edge Devices.[J]. Sensors (Basel, Switzerland), 2026 doi:10.3390/s26103208.
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