WiFi sensing based on channel state information (CSI) collected from commodity WiFi devices has shown great potential across a wide range of applications, including vital sign monitoring and indoor localization. Existing WiFi sensing approaches typically estimate motion information directly from CSI. However, they often overlook the inherent advantages of channel impulse response (CIR), a delay-domain representation that enables more intuitive and principled motion sensing by naturally concentrating motion energy and separating multipath components. Motivated by this, we revisit WiFi sensing and introduce CIRSense, a new framework that enhances the performance and interpretability of WiFi sensing with CIR. CIRSense is built upon a new motion model that characterizes fractional delay effects, a fundamental challenge in CIR-based sensing. This theoretical model underpins technical advances for the three challenges in WiFi sensing: hardware distortion compensation, high-resolution distance estimation, and subcarrier aggregation for extended range sensing. CIRSense, operating with a 160 MHz channel bandwidth, demonstrates versatile sensing capabilities through its dual-mode design, achieving a mean error of approximately 0.25 bpm in respiration monitoring and 0.09 m in distance estimation. Comprehensive evaluations across residential spaces, far-range scenarios, and multi-target settings demonstrate CIRSense's superior performance over state-of-the-art CSI-based baselines. Notably, at a challenging sensing distance of 20 m, CIRSense achieves at least 3x higher average accuracy with more than 4.5x higher computational efficiency.

翻译：基于商用WiFi设备采集的信道状态信息（CSI）的WiFi感知技术，在生命体征监测和室内定位等广泛应用中展现出巨大潜力。现有WiFi感知方法通常直接从CSI估计运动信息，但往往忽视了信道冲激响应（CIR）的固有优势——这种时延域表示能通过自然聚集运动能量和分离多径分量，实现更直观且原理清晰的运动感知。受此启发，我们重新审视WiFi感知并提出了CIRSense，这是一个利用CIR提升WiFi感知性能与可解释性的新框架。CIRSense建立在刻画分数时延效应的新型运动模型之上，该效应是基于CIR感知的根本性挑战。这一理论模型为应对WiFi感知中的三大挑战提供了技术支撑：硬件失真补偿、高分辨率距离估计以及面向远距离感知的子载波聚合。CIRSense在160 MHz信道带宽下运行，通过双模式设计展现出多功能的感知能力，在呼吸监测中实现约0.25 bpm的平均误差，在距离估计中达到0.09 m的平均误差。在居住空间、远距离场景和多目标环境中的综合评估表明，CIRSense的性能优于当前最先进的基于CSI的基线方法。值得注意的是，在20米这一具有挑战性的感知距离上，CIRSense实现了至少3倍的平均精度提升，同时计算效率提高了4.5倍以上。