Untangling the Geometry and Speed for RF Sensing Spectrograms

2026-09-22

AI Summary

本文解决了RF感知中目标运动与感知几何纠缠的问题，通过物理可解释的参数表示和基于物理信息的自编码器方法，实现了速度和几何因素的解耦。

Abstract

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.

Problem

• RF sensing
• Doppler signatures
• sensing geometry
• real-world settings

Innovation

• RF Sensing
• Doppler Signature Disentanglement
• Physics-Informed Autoencoder
• Synthetic-to-Real Training Framework