发表机构
National Institute of Technology, Rourkela（鲁尔凯拉国家理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出物理引导的深度度量学习，采用连续到达时间正弦位置编码，结合物理先验验证，用于开放世界雷达脉冲去交织，提升复杂电磁环境下的分离性能。
AI 中文摘要
雷达脉冲去交织是电子支援措施（ESM）中的一项基础任务，旨在未知发射器数量的情况下，在密集、对抗性电磁环境中，将来自多个非合作发射器的按时间顺序交织的脉冲流分离。经典直方图变换和封闭世界深度分类器在严重脉冲丢失、敏捷脉冲重复间隔（PRI）调制和虚假杂波下性能退化。本文系统性地刻画了开放世界雷达去交织中深度度量学习的连续时间表示和物理引导的模型选择。基于Gunn等人[1]引入的用于开放世界去交织的基于Transformer的度量学习框架，我们引入了一种连续到达时间（ToA）正弦位置编码，直接建模物理脉冲间持续时间而非序数令牌索引，这一设计选择与[1]形成对比，后者发现序数位置编码无益并完全省略。神经网络参数仅通过监督对比（SupCon）学习进行优化，而基于PRI一致性和到达角（AoA）连续性的尺度感知物理域先验，作为物理引导的验证和检查点选择标准，作用于无监督HDBSCAN聚类分配。
英文摘要
Radar pulse de-interleaving is a foundational Electronic Support Measures (ESM) task that aims to separate chronologically interleaved pulse streams from multiple non-cooperative transmitters under unknown emitter cardinality in dense, contested electromagnetic environments. Classical histogram transforms and closed-world deep classifiers degrade under severe pulse loss, agile Pulse Repeti tion Interval (PRI) modulation, and spurious clutter. In this paper, we systematically characterise continuous temporal representations and physics-guided model selection in deep metric learning for open-world radar de-interleaving. Building on the transformer-based metric-learning framework for open-world deinterleaving introduced by Gunn et al. [1], we introduce a continuous Time-of-Arrival (ToA) sinusoidal positional encoding that directly models physical inter-pulse durations rather than ordinal token indices, a design choice that contrasts with [1], who found ordinal positional encodings provided no benefit and omitted them entirely. Neural network parameters are optimised solely via Supervised Contrastive (SupCon) learning, while scale-aware physical domain priors based on PRI Consistency and Angle-of-Arrival (AoA) continuity serve as physics-guided validation and checkpoint-selection criteria operating on unsupervised HDBSCAN cluster assignments.
Comments13 pages
