通过物理启发的结构锚定与捕获感知原型校准，提升跨环境无线指纹识别性能。
Physics-Informed Structure Anchoring With Capture-Aware Prototype Calibration for Cross-Environment RF Fingerprinting
- 利用天线拓扑和频率偏移动态构建物理引导的图结构表示
- 在无标签目标域上实现0.9257的平均宏F1，保持源模型不变
- 适合部署于多天线、跨环境的物联网设备身份识别场景
射频指纹识别（RFFI）利用发射端硬件固有差异作为物联网设备的物理层身份标识，但深度模型在不同采集环境中性能下降。多天线接收时，天线拓扑与载波频率偏移（CFO）动态影响接收观测，而捕获相关变异会扭曲目标特征嵌入并错位源训练决策边界。本文提出物理信息结构锚定与捕获感知原型校准（PISA-CAPC）方法，分两阶段处理表示与决策不匹配问题：源训练阶段，PISA通过拓扑引导图组织天线标记，基于CFO导出的采集动态条件传播，并应用受限上下文残差抑制以保留身份证据；部署阶段，无标签捕获感知原型校准（U-CAPC）估计局部原型并重校目标决策分数，无需目标标签或主干网络更新。在含四根接收天线、十台发射机的实测WiFi基准上，PISA-CAPC在平衡归纳设置下达到0.9257的平均目标域宏F1。组件消融实验表明拓扑引导锚定、CFO条件调制、可靠性感知标记聚合、上下文抑制及捕获感知校准具有互补作用。结果表明，在不更换部署主干的前提下，物理驱动表示学习与无标签决策校准可有效提升跨环境RFFI性能。
原文摘要 · Abstract (English)
Radio frequency fingerprint identification (RFFI) exploits transmitter-specific hardware imperfections as physicallayer identity cues for Internet of Things (IoT) devices, but deep models often degrade across acquisition environments. In multi-antenna reception, antenna topology and frequencyoffset dynamics structure receiver observations, while capturedependent variation distorts target embeddings and misaligns source-trained decision boundaries. This article proposes physicsinformed structure anchoring with capture-aware prototype calibration (PISA-CAPC) to address both representation and decision mismatches. The two stages separate source representation construction from target decision correction. During source training, PISA organizes antenna tokens through a topology-guided graph, conditions propagation on CFO-derived acquisition dynamics, and applies bounded contextual residual suppression to preserve identity evidence. At deployment, unlabeled capture-aware prototype calibration (U-CAPC) estimates capture-local prototypes and recalibrates target decision scores while keeping the representation and source classifier fixed. Thus, calibration uses neither target labels nor target-domain backbone updates. On a measured WiFi benchmark with four receive antennas and ten transmitters, PISA-CAPC achieves a mean target-domain Macro-F1 of 0.9257 under a balanced transductive setting. Component ablations support complementary roles for topology-guided anchoring, CFO-conditioned modulation, reliability-aware token aggregation, contextual suppression, and capture-aware calibration. These results indicate that physically motivated representation learning can be combined with labelfree decision calibration to improve cross-environment RFFI under the evaluated protocol without changing the deployed backbone.
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