Analyzing and recognizing radar signals are important tasks for effective Electronic Support Measurement (ESM) system operation. The electromagnetic environment is highly complex nowadays, however, resulting in non-uniformed distributed pulse streams. The high-dimensional features of the radar emitters are also overly complicated. Isolating useful information of the pulse streams and removing noise can assist in the emitter classification process. This letter proposes an attention-based approach for radar emitter classification using recurrent neural networks (RNNs). Several RNNs assigned to individual features exploit the intrinsic patterns of the radar pulse streams via supervised learning; the learned patterns are then used to identify patterns of interest in the test pulse streams and place them into different categories. The attention mechanism demonstrates effective treatment of high missing and spurious pulse ratios, especially in cases of multiple consecutive missing pulses and multifunctional radar pulses. Simulation results also show that the proposed model outperforms other state-of-the-art neural network structures.

中文翻译：

---

使用基于注意力的多 RNN 的雷达发射器分类

分析和识别雷达信号是电子支持测量 (ESM) 系统有效运行的重要任务。然而，如今的电磁环境非常复杂，导致脉冲流分布不均匀。雷达发射器的高维特征也过于复杂。隔离脉冲流的有用信息并去除噪声有助于发射器分类过程。这封信提出了一种使用循环神经网络 (RNN) 的基于注意力的雷达发射器分类方法。分配给各个特征的几个 RNN 通过监督学习利用雷达脉冲流的内在模式；然后使用学习到的模式来识别测试脉冲流中感兴趣的模式，并将它们归入不同的类别。注意机制证明了对高丢失和虚假脉冲比的有效处理，特别是在多个连续丢失脉冲和多功能雷达脉冲的情况下。仿真结果还表明，所提出的模型优于其他最先进的神经网络结构。