Analyzing radar signals is a critical task in modern Electronic Warfare (EW) environments. However, the pulse streams emitted by radars have flexible features and complex patterns which are difficult to be identified from a statistical perspective. To solve this problem, pulse repetition interval (PRI) is used as a distinguishing parameter of emitters to be identified. However, traditional PRI modulation recognition methods can only deal with simple PRI modulations and their performance will further degrade with the increasing number of emitters or noisy environments. In this paper, we introduce an attention-based recognition framework based on recurrent neural network (RNN) to categorize pulse streams with complex PRI modulations and in environments with high ratios of missing and spurious pulses. Simulation results show that our model is robust to noisy environments and has a better performance than conventional methods.

中文翻译：

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具有循环神经网络的基于注意力的雷达 PRI 调制识别

分析雷达信号是现代电子战 (EW) 环境中的一项关键任务。然而，雷达发射的脉冲流具有灵活的特征和复杂的模式，从统计角度难以识别。为了解决这个问题，脉冲重复间隔（PRI）被用作要识别的发射器的区分参数。然而，传统的 PRI 调制识别方法只能处理简单的 PRI 调制，并且随着发射器数量的增加或嘈杂环境的增加，其性能将进一步下降。在本文中，我们介绍了一种基于循环神经网络 (RNN) 的基于注意力的识别框架，以对具有复杂 PRI 调制的脉冲流以及在丢失和虚假脉冲比例高的环境中进行分类。