In electronic warfare, radar signal deinterleaving is a critical task. While many researchers have applied deep learning and utilised known radar classes to construct interleaved pulse sequences training sets for deinterleaving models, these models face challenges in distinguishing between known and unknown radar classes in open-set scenarios. To address this challenge, the authors propose a novel model, the Reconstruction Bidirectional Recurrent Neural Network (RBi-RNN). RBi-RNN utilises input reconstruction and employs a joint training strategy incorporating cross-entropy loss, reconstruction loss, and centre loss. These strategies aim to maximise inter-class latent representation distances while minimising intra-class disparities. By incorporating an open-set recognition method based on extreme value theory, RBi-RNN adapts to open-set scenarios. Simulation results demonstrate the superiority of RBi-RNN over conventional models in both closed-set and open-set scenarios. In open-set scenarios, it successfully discriminates between known and unknown radar signals within interleaved pulse sequences, deinterleaving known radar classes with high stability. The authors lay the foundation for future unsupervised deinterleaving methods designed specifically for unknown radar pulses.

中文翻译：

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开放集场景下基于双向循环神经网络的已知雷达信号重构解交织方法

在电子战中，雷达信号解交织是一项关键任务。虽然许多研究人员已经应用深度学习并利用已知的雷达类别来构建用于解交错模型的交错脉冲序列训练集，但这些模型在开放集场景中区分已知和未知的雷达类别方面面临着挑战。为了应对这一挑战，作者提出了一种新颖的模型：重建双向循环神经网络（RBi-RNN）。 RBi-RNN 利用输入重建并采用结合交叉熵损失、重建损失和中心损失的联合训练策略。这些策略旨在最大化类间潜在表示距离，同时最小化类内差异。 RBi-RNN 通过结合基于极值理论的开集识别方法，适应开集场景。仿真结果证明了 RBi-RNN 在闭集和开放集场景中均优于传统模型。在开放场景中，它成功区分交错脉冲序列中的已知和未知雷达信号，以高稳定性解交错已知雷达类别。作者为未来专为未知雷达脉冲设计的无监督解交织方法奠定了基础。