FSTC-Encoder：用于通用射频感知的特征-空间-时间关联学习
FSTC-Encoder: Feature--Spatial--Temporal Correlation Learning for Generalizable RF Sensing
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中文总结 AI 辅助
本文针对异构射频感知跨域复用难的问题，提出FSTC-Encoder模型，通过特征-空间-时间关联建模统一异构射频表示学习，在多数据集多模态任务中实现高准确率与跨域性能提升。
中文摘要 AI 辅助
异构射频感知在特征结构、空间布局和时间尺度上存在显著差异，导致现有模型难以在不同设备、环境和射频模态间复用。本文提出FSTC-Encoder，该模型通过特征、空间和时间关联建模实现异构射频表示学习的统一：结构感知特征编码适配不同信号结构，基于集合的空间编码聚合可变观测值，分层时间编码联合捕捉局部变化与长程依赖。在感知任务和模态中，FSTC-Encoder保留相同的空间-时间骨干架构，仅调整特征配置和任务头。在Widar3.0、CSI-Bench和XRF55数据集上，FSTC-Encoder在多因子跨域协议下达到92.15%的平均准确率，在其余四项感知任务中的三项排名第一，在WiFi、毫米波雷达和RFID模态上始终表现强劲，且通过跨射频学习将跨模态性能差距从18.85%缩小至12.93%。这些结果表明，FSTC-Encoder具备高域鲁棒性、任务通用性和模态扩展性。
英文摘要
Heterogeneous RF sensing differs substantially in feature structure, spatial layout, and temporal scale, making existing models difficult to reuse across devices, environments, and RF modalities. We propose FSTC-Encoder, which unifies heterogeneous RF representation learning through feature, spatial, and temporal correlation modeling. Structure-aware feature encoding accommodates different signal structures, set-based spatial encoding aggregates variable observations, and hierarchical temporal encoding jointly captures local variations and long-range dependencies. Across sensing tasks and modalities, FSTC-Encoder retains the same spatial--temporal backbone architecture while varying only the feature configuration and task head. Across Widar3.0, CSI-Bench, and XRF55, FSTC-Encoder achieves 92.15% mean Accuracy under multi-factor cross-domain protocols, ranks first on three of four additional sensing tasks, remains consistently strong across WiFi, millimeter-wave radar, and RFID, and reduces the cross-modality performance gap from 18.85% to 12.93% through cross-RF learning. These results demonstrate that FSTC-Encoder achieves high domain robustness, task generality, and modality extensibility.
