用于跨环境射频指纹识别的基于捕获感知原型校准的物理信息结构锚定
Physics-Informed Structure Anchoring With Capture-Aware Prototype Calibration for Cross-Environment RF Fingerprinting
- School of Information Systems Engineering, Information Engineering University（信息工程大学信息系统工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究针对跨环境射频指纹识别中模型性能受环境变化影响的问题，提出PISA-CAPC框架，通过分离源表示锚定与目标校准，结合拓扑引导、残差抑制和捕获感知校准，提升跨环境识别性能。
AI中文摘要：
射频指纹识别（RFFI）利用特定发射机的硬件缺陷作为物联网设备的物理层身份线索，但深度RFFI模型在采集环境变化时往往会退化。在多天线接收中，这种退化不仅是一般的分布变化，还受接收机阵列拓扑、频率偏移动态和捕获相关目标结构影响。本文提出了基于捕获感知原型校准的物理信息结构锚定（PISA-CAPC）框架，将源表示锚定与固定骨干目标校准分离。表示阶段用拓扑图组织天线令牌并调制，应用有界上下文残差抑制。部署时，无标签捕获感知原型校准（U-CAPC）在校准目标决策分数。在多天线WiFi基准测试中，PISA-CAPC取得了0.9257的目标域平均宏F1。消融实验证实了拓扑引导结构锚定、上下文残差抑制和捕获感知校准的互补作用。这些结果确立了PISA-CAPC作为跨环境RFFI的固定骨干路径，将物理驱动的表示学习与无标签、捕获感知决策校准相结合。
英文摘要：
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.
