用变换器与度量学习实现未知数量雷达信号源的分离。
Radar Pulse Deinterleaving with Transformer Based Deep Metric Learning
- 基于变换器的度量学习模型，通过三元组损失在合成数据上训练。
- 调整互信息得分达0.882，优于其他深度学习模型。
- 适合雷达信号处理、电子战等需要信号源分离的场景。
接收雷达脉冲时，记录的脉冲序列通常包含来自多个不同发射源的脉冲。雷达脉冲去交织任务是将这些脉冲按其来源的发射源分离开。值得注意的是，任意记录脉冲序列中的发射源数量是未知的。本文定义了该问题，并提出可用于衡量模型性能的指标。我们提出一种基于变换器的度量学习方法，使用三元组损失在合成数据上进行训练。该模型在与其他深度学习模型的对比中表现优异，调整互信息得分为0.882。
原文摘要 · Abstract (English)
When receiving radar pulses it is common for a recorded pulse train to contain pulses from many different emitters. The radar pulse deinterleaving problem is the task of separating out these pulses by the emitter from which they originated. Notably, the number of emitters in any particular recorded pulse train is considered unknown. In this paper, we define the problem and present metrics that can be used to measure model performance. We propose a metric learning approach to this problem using a transformer trained with the triplet loss on synthetic data. This model achieves strong results in comparison with other deep learning models with an adjusted mutual information score of 0.882.
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