提出新嵌入方法，实现复杂环境下雷达脉冲的高精度排序。
WVEmbs with its Masking: A Method For Radar Signal Sorting
- 用宽值嵌入技术处理脉冲描述字，自适应信号分布。
- 引入值维度掩码生成挑战样本，提升模型鲁棒性。
- 端到端实现高密度脉冲序列的逐脉冲精确排序。
本研究提出一种新型嵌入方法——宽值嵌入（Wide-Value-Embeddings, WVEmbs），用于将脉冲描述字（PDWs）作为归一化输入送入神经网络。该方法能适应交织雷达信号的分布特性，对原始信号特征按重要性从弱到强排序，稳定学习过程。为应对雷达信号交织中的不平衡问题，我们在WVEmbs上引入值维度掩码机制，可自动高效生成具有挑战性的样本，并构建多样化的交织场景，促使模型学习更鲁棒的特征。实验表明，该方法是一种高效的端到端方案，在复杂非理想环境中实现了对高密度交织雷达脉冲序列的高粒度、样本级脉冲排序。
原文摘要 · Abstract (English)
Our study proposes a novel embedding method, Wide-Value-Embeddings (WVEmbs), for processing Pulse Descriptor Words (PDWs) as normalized inputs to neural networks. This method adapts to the distribution of interleaved radar signals, ranking original signal features from trivial to useful and stabilizing the learning process. To address the imbalance in radar signal interleaving, we introduce a value dimension masking method on WVEmbs, which automatically and efficiently generates challenging samples, and constructs interleaving scenarios, thereby compelling the model to learn robust features. Experimental results demonstrate that our method is an efficient end-to-end approach, achieving high-granularity, sample-level pulse sorting for high-density interleaved radar pulse sequences in complex and non-ideal environments.
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