解开射频传感频谱图的几何与速度之谜
Untangling the Geometry and Speed for RF Sensing Spectrograms
- University of California, Santa Barbara（加州大学圣巴巴拉分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出物理信息自编码器与合成到真实训练框架，解耦WiFi频谱图中的反射体速度与几何，联合恢复多普勒脊参数，并在合成与真实实验中显著超越基线。
AI中文摘要：
射频感知中的一个基本挑战是，链路观测到的多普勒特征将目标运动与感知几何纠缠在一起，导致其难以适用于不受约束的真实世界场景。本文为物理可解释的射频感知建立了新基础，将反射体速度与几何解耦，联合恢复每个主导多普勒脊的速度、几何因子、相对幅度和宽度。具体而言，我们首先开发了WiFi频谱图的紧凑参数化表示，并通过对大型多样化人体活动数据集的系统性计算机视觉分析，确立其低维结构，从而为学习提供了易处理的基矗基于该表示，我们设计了一个物理信息自编码器，其结构化瓶颈和可微射频前向模型强制实现对反射体速度和几何的物理意义估计。我们进一步引入合成到真实的训练框架，消除了对真实WiFi训练数据的需求。我们在已知和时变几何下，使用独立生成的合成测试集和31次真实WiFi实验，广泛验证了所提框架。结果表明，在速度和几何提取方面性能优越，能在所有设置下稳健恢复底层几何、速度、多普勒脊幅度和脊宽度，同时大幅超越最强基线。
英文摘要：
A fundamental challenge in RF sensing is that Doppler signatures observed by a link entangle the target's motion with the sensing geometry, resulting in limited applicability to unconstrained real-world settings. In this paper, we establish a new foundation for physically interpretable RF sensing that disentangles reflector speed from geometry, jointly recovering the speed, geometry factor, relative amplitude, and width of each dominant Doppler ridge. More specifically, we first develop a compact parametric representation of WiFi spectrograms and establish its low-dimensional structure through a systematic computer-vision analysis of a large and diverse human-activity dataset, thereby providing a tractable foundation for learning. Building on this representation, we then design a physics-informed autoencoder whose structured bottleneck and differentiable RF forward model enforce physically meaningful estimates of reflector speed and geometry. We further introduce a synthetic-to-real training framework, eliminating the need for real WiFi training data. We extensively validate the proposed framework under both known and time-varying geometries, using both independently generated synthetic test sets and 31 real WiFi experiments. The results demonstrate the superior performance in speed and geometry extraction, robustly recovering the underlying geometry, speeds, Doppler-ridge amplitudes, and ridge widths across all settings, while substantially outperforming the strongest baselines.
