通过联合建模特征、空间与时间相关性，提升无线感知跨设备泛化能力。
FSTC-Encoder: Feature--Spatial--Temporal Correlation Learning for Generalizable RF Sensing
- 分层建模信号结构、观测位置与时间变化，统一异构无线感知表示
- 跨域测试中平均准确率达92.15%，在三类任务中排名第一
- 适用于WiFi、毫米波雷达和RFID，显著缩小跨模态性能差距
异构无线感知在特征结构、空间布局和时间尺度上差异显著，导致现有模型难以跨设备、环境与感知模态复用。本文提出FSTC-Encoder，通过特征、空间与时间相关性联合建模，实现异构无线感知表示的统一学习。结构感知的特征编码适应不同信号结构，基于集合的空间编码聚合可变观测，分层时间编码同时捕捉局部变化与长程依赖。在多个传感任务与模态下，FSTC-Encoder保持相同的时空主干架构，仅调整特征配置与任务头。在Widar3.0、CSI-Bench和XRF55数据集上，其在多因子跨域协议下平均准确率达到92.15%，在四项附加传感任务中排名首位，对WiFi、毫米波雷达和RFID均表现稳定，并将跨模态性能差距从18.85%降低至12.93%。结果表明，FSTC-Encoder具备高领域鲁棒性、任务通用性与模态可扩展性。
原文摘要 · Abstract (English)
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.
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