基于领域泛化的变化环境下雷达目标识别
Towards Radar Emitter Recognition in Changing Environments with Domain Generalization
摘要：
分析复杂电子战环境下的雷达信号是一项非常重要的任务。然而，在现实世界中，不断变化的电子战环境会导致信号分布不一致，例如不同检测场景之间的脉冲重复间隔（PRI）不匹配。本文提出了一种新的领域泛化框架，以提高信号识别在变化环境中的适应性。具体而言，我们首先设计了几个噪声发生器来模拟不同的场景。与传统的增强方法不同，我们引入的生成器仔细地增强了检测信号的多样性，同时保持了信号的语义特征。此外，我们提出了一个信号场景域分类器，工作在对抗学习的方式。所提出的分类器保证信号预测器推广到不同的场景。大量的对比实验证明了该方法的优越性。
英文摘要：
Analyzing radar signals from complex Electronic Warfare (EW) environment is a non-trivial task.However, in the real world, the changing EW environment results in inconsistent signal distribution, such as the pulse repetition interval (PRI) mismatch between different detected scenes.In this paper, we propose a novel domain generalization framework to improve the adaptability of signal recognition in changing environments.Specifically, we first design several noise generators to simulate varied scenes. Different from conventional augmentation methods, our introduced generators carefully enhance the diversity of the detected signals and meanwhile maintain the semantic features of the signals. Moreover, we propose a signal scene domain classifier that works in the manner of adversarial learning. The proposed classifier guarantees the signal predictor to generalize to different scenes. Extensive comparative experiments prove the proposed method's superiority.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
