Radio signal recognition is a crucial task in both civilian and military applications, as accurate and timely identification of unknown signals is an essential part of spectrum management and electronic warfare. The majority of research in this field has focused on applying deep learning for modulation classification, leaving the task of signal characterisation as an understudied area. This paper addresses this gap by presenting an approach for tackling radar signal classification and characterisation as a multi-task learning (MTL) problem. We propose the IQ Signal Transformer (IQST) among several reference architectures that allow for simultaneous optimisation of multiple regression and classification tasks. We demonstrate the performance of our proposed MTL model on a synthetic radar dataset, while also providing a first-of-its-kind benchmark for radar signal characterisation.

翻译：无线电信号识别在民用和军事领域均是一项关键任务，因为准确及时地识别未知信号是频谱管理与电子战的重要组成部分。该领域的大多数研究集中于应用深度学习进行调制分类，而信号特征表征任务仍是一个研究不足的领域。本文通过提出一种将雷达信号分类与特征表征作为多任务学习问题处理的方法，填补了这一研究空白。我们在若干参考架构中提出了IQ信号变换器，该架构能够同时优化多个回归与分类任务。我们基于合成雷达数据集验证了所提多任务学习模型的性能，同时首次建立了雷达信号特征表征的基准测试体系。