发表机构
Ludovika University of Public Service; Eötvös Loránd University（路德维卡公共服务大学; 厄特沃什·罗兰大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对现代冲突中无人机检测需求，利用轻量级卷积神经网络，将样本转换为光栅化时域图像，设计自定义模型架构并测试评估，结果显示紧凑模型能保持高精度且计算要求低，相比现有方法降低成本。
AI 中文摘要
现代冲突中第一人称视角无人机的使用增加，催生了对能在复杂电磁环境中运行的紧凑可靠检测系统的需求。这些无人机通过机载视频发射器持续传输视频信号，产生可用于早期检测的射频辐射。本研究探讨使用轻量级卷积神经网络，通过基于软件定义无线电的电子战框架自动检测无人机信号。样本被转换为光栅化时域图像，为嵌入式系统提供计算高效的输入表示。设计了几种自定义模型架构，并使用包含约40000个标记图像的数据集，在准确性、模型大小和推理性能方面进行了基准测试。除离线测试外，模型还集成到GNU Radio信号处理链中进行实时评估。结果表明，紧凑模型能在保持低计算要求的同时实现高检测精度，适用于嵌入式射频监测应用。与现有的基于频谱图的射频检测方法相比，该方法消除了频域预处理，以显著降低的计算成本实现了相当的准确性。
英文摘要
The increasing use of first-person-view drones in modern conflicts has created a demand for compact and reliable detection systems capable of operating in complex electromagnetic environments. These drones continuously transmit video signals through onboard video transmitters, generating radio-frequency emissions that can be exploited for early detection. This study investigates the use of lightweight convolutional neural networks for automated detection of drone signals captured by a software-defined radio-based electronic warfare framework. Samples are converted into rasterized time-domain images, providing a computationally efficient input representation suitable for embedded systems. Several custom model architectures were designed and benchmarked in terms of accuracy, model size, and inference performance using a dataset containing approximately 40,000 labeled images. In addition to offline testing, the models were integrated into a GNU Radio signal processing chain for real-time evaluation. The results show that compact models can achieve high detection accuracy while maintaining low computational requirements, making them suitable for embedded radio-frequency monitoring applications. Compared with existing spectrogram-based RF detection methods, the proposed approach eliminates frequency-domain preprocessing and achieves comparable accuracy with significantly reduced computational cost.
