Analyzing radar signals is an important task in operating electronic support measure systems. The received signals in the real electromagnetic environment often originate from multiple emitters and must be separated for further processing. Pulses from important target emitters with known parameters should be picked out first. To solve the problem, time-of-arrival (TOA) deinterleaving may be performed to extract signals from a certain emitter by learning the pulse repetition interval (PRI) modulation that makes up the signal. However, conventional deinterleaving methods only work with simple PRI modulations; their performance degrades in noisy environments. A novel approach based on denoising autoencoders for TOA deinterleaving was developed in this article. The inner patterns of pulse-of-interest sequences were learned by the proposed denoising autoencoders to generate output sequences from well-trained autoencoders. Simulation results show that the proposed method outperforms conventional methods, especially in environments with high lost and spurious pulse ratios.

中文翻译：

---

使用去噪自动编码器对脉冲流进行去交织

分析雷达信号是操作电子支援测量系统的一项重要任务。真实电磁环境中接收到的信号通常来自多个发射器，必须将其分离以进行进一步处理。应该首先挑选来自具有已知参数的重要目标发射器的脉冲。为了解决该问题，可以通过学习构成信号的脉冲重复间隔（PRI）调制来执行到达时间（TOA）解交织以从某个发射器提取信号。然而，传统的解交织方法只适用于简单的 PRI 调制；它们的性能在嘈杂的环境中会下降。本文开发了一种基于去噪自编码器进行 TOA 解交织的新方法。所提出的去噪自动编码器学习感兴趣脉冲序列的内部模式，以从训练有素的自动编码器生成输出序列。仿真结果表明，所提出的方法优于传统方法，尤其是在高损耗和杂散脉冲比的环境中。