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.

翻译：在对抗环境中识别与分析雷达辐射源，需对接收信号进行检测与分离。当信号来自相同方向且频率相近时，去交织处理仍具挑战性。随着辐射源性能的不断提升，克服这一局限的解决方案日益重要。本文将问题建模为时域盲源分离任务，应用监督训练的神经网络从接收混合信号中提取底层源信号。该方法能够处理雷达与通信辐射源发出的高度重叠信号及连续波信号。我们借鉴音频源分离领域的最新进展，对当前先进模型进行扩展，以实现对任意射频信号的去交织。实验结果表明，所提方法能够利用单通道接收机在给定频段内分离两种未知波形。