用深度学习自动分类160类短波信号，1秒内准确率达90%。
Large-Scale Classification of Shortwave Communication Signals with Machine Learning
- 用卷积神经网络直接识别短波信号，无需人工设计特征。
- 在真实全球接收数据上，1秒观测达90%分类准确率。
- 适用于无先验知识的自动化无线电监测场景。
本文提出一种深度学习方法，用于对160类短波无线电信号进行分类。针对短波频谱中信号类型繁多、模拟调制方式多样及电离层传播特性复杂等挑战，采用深度卷积神经网络作为分类器，在大量合成信号与高质量实录数据上训练，实现端到端的盲分类。该方法无需预知识或特殊预处理，也无需为每类信号手动设计判别特征。最终在由全球部署接收设备获取的真实无线电信号上评估，仅需1秒观测时间，最高分类准确率达90%。
原文摘要 · Abstract (English)
This paper presents a deep learning approach to the classification of 160 shortwave radio signals. It addresses the typical challenges of the shortwave spectrum, which are the large number of different signal types, the presence of various analog modulations and ionospheric propagation. As a classifier a deep convolutional neural network is used, that is trained to recognize 160 typical shortwave signal classes. The approach is blind and therefore does not require preknowledge or special preprocessing of the signal and no manual design of discriminative features for each signal class. The network is trained on a large number of synthetically generated signals and high quality recordings. Finally, the network is evaluated on real-world radio signals obtained from globally deployed receiver hardware and achieves up to 90% accuracy for an observation time of only 1 second.
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