基于深度学习的大规模电磁信号识别

Large-scale electromagnetic signal recognition based on deep learning

近年来,很多高质量的数据集支撑了深度学习在计算机视觉、语音和自然语言处理领域的快速发展。但在电磁信号识别领域仍缺乏高质量的数据集,为促进深度学习在电磁信号识别中的应用,本文基于广播式自动相关监视(ADS-B)建立了一个大规模的真实电磁信号数据集。首先设计了一个自动数据收集和标注系统,在开放和真实的场景中自动捕获ADS-B电磁信号。通过对ADS-B信号进行数据清理和排序,建立高质量的ADS-B信号数据集;其次,对使用数据集的深度学习模型的性能进行深入研究,在不同信噪比、采样率、样本数目下对模型进行综合评估。该数据集给相关研究者提供了有价值的研究基准。

In recent years, many high-quality datasets have supported the rapid development of deep learning in the field of computer vision, speech and natural language processing. Nevertheless, there is still a lack of high-quality datasets in the field of electromagnetic signal recognition. In order to promote in-depth learning in the application of electromagnetic signal recognition, a large-scale real electromagnetic signal dataset is established based on Automatic Dependent Surveillance-Broadcast(ADS-B). An automatic data collection and labeling system is designed to automatically capture ADS-B electromagnetic signals in open and real scenes. A high quality ADS-B signal dataset is established by data cleaning and sorting of ADS-B signals. The performance of in-depth learning models using datasets is studied, and the models are evaluated comprehensively under different signal-to-noise ratios, sampling rates and number of samples. The data set provides a valuable benchmark for relevant researchers.