用深度学习从混叠信号中分离雷达与通信波形。
Blind Source Separation of Radar Signals in Time Domain Using Deep Learning
- 将时域盲源分离思想引入雷达信号处理，使用监督训练神经网络。
- 单通道接收下成功分离同频重叠的未知波形，包括连续波信号。
- 适用于复杂电磁环境下雷达与通信信号的解耦，适合电子战研究者。
在对抗环境中识别和分析雷达发射源需要检测并分离接收到的信号。当信号来自相同方向且频率相近时，去交织变得极具挑战性。随着发射机能力的提升，解决这一问题日益重要。本文将该问题视为时域盲源分离，采用监督训练的神经网络从混合信号中提取原始信号，可有效处理高度重叠及连续波（CW）信号，涵盖雷达与通信发射源。借鉴语音源分离领域的最新进展，扩展现有最优模型以实现任意射频（RF）信号的去交织。实验结果表明，该方法可在单一通道接收条件下，成功分离给定频段内的两路未知波形。
原文摘要 · Abstract (English)
Identification and further analysis of radar emitters in a contested environment requires detection and separation of incoming signals. If they arrive from the same direction and at similar frequencies, deinterleaving them remains challenging. A solution to overcome this limitation becomes increasingly important with the advancement of emitter capabilities. We propose treating the problem as blind source separation in time domain and apply supervisedly trained neural networks to extract the underlying signals from the received mixture. This allows us to handle highly overlapping and also continuous wave (CW) signals from both radar and communication emitters. We make use of advancements in the field of audio source separation and extend a current state-of-the-art model with the objective of deinterleaving arbitrary radio frequency (RF) signals. Results show, that our approach is capable of separating two unknown waveforms in a given frequency band with a single channel receiver.
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