Recognition of multi-function radar (MFR) work mode in an input pulse sequence is a fundamental task to interpret the functions and behaviour of an MFR. There are three major challenges that must be addressed: (i) The received radar pulses stream may contain an unknown number of multiple work mode class segments. (ii) The intra-mode and inter-mode knowledge of a modern MFR may be too flexible and complicated to be represented and learned through traditional hand-crafted features and learning models. (iii) The variable duration of each enclosed work mode makes the identification of the transition boundaries of adjacent modes difficult. To address these challenges and implement automatic recognition of MFR work mode sequences at a pulse-level, this study develops a novel processing framework based on a time series representation of MFR work mode sequence and sequence-to-sequence (seq2seq) long short-term memory network. The proposed method can not only automatically recognise multiple complexes modulated work mode classes in a pulse sequence. Still, it can also accurately identify the transition boundaries between each class by labelling the class information for each pulse. The experimental results showed the extended capabilities and improved performance of the proposed method over the state-of-the-art work mode classification methods.

中文翻译：

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使用分层seq2seq LSTM的MFR脉冲序列的工作模式识别和边界识别

在输入脉冲序列中识别多功能雷达（MFR）工作模式是解释MFR的功能和行为的一项基本任务。必须解决三个主要挑战：（i）接收到的雷达脉冲流可能包含未知数量的多个工作模式类别片段。（ii）现代MFR的模式内和模式间知识可能过于灵活和复杂，无法通过传统的手工功能和学习模型来表示和学习。（iii）每个封闭工作模式的持续时间各不相同，因此很难确定相邻模式的过渡边界。为了解决这些挑战，并实现脉冲级的MFR工作模式序列自动识别，这项研究开发了一种基于MFR工作模式序列和序列到序列（seq2seq）长短期记忆网络的时间序列表示的新颖处理框架。所提出的方法不仅可以自动识别脉冲序列中的多个复杂调制的工作模式类别。而且，它还可以通过标记每个脉冲的类别信息来准确识别每个类别之间的转换边界。实验结果表明，与最新的工作模式分类方法相比，该方法具有扩展的功能和改进的性能。它也可以通过标记每个脉冲的类别信息来准确地识别每个类别之间的转换边界。实验结果表明，与最新的工作模式分类方法相比，该方法具有扩展的功能和改进的性能。它也可以通过标记每个脉冲的类别信息来准确地识别每个类别之间的转换边界。实验结果表明，与最新的工作模式分类方法相比，该方法具有扩展的功能和改进的性能。