Abstract
With the IEEE 802.11bf Task Group introducing amendments to the WLAN standard for advanced sensing, interest in using Wi-Fi Channel State Information (CSI) for remote sensing has surged. Recent findings indicate that learning a unified three-dimensional motion representation through Doppler Radiance Fields (DoRFs) derived from CSI significantly improves the generalization capabilities of Wi-Fi-based human activity recognition (HAR). Despite this progress, CSI signals remain affected by asynchronous access point (AP) clocks and additive noise from environmental and hardware sources. Consequently, even with existing preprocessing techniques, both the CSI data and Doppler velocity projections used in DoRFs are still susceptible to noise and outliers, limiting HAR performance. To address this challenge, we propose a novel framework for multi-antenna APs to suppress noise and identify the most informative antennas based on DoRF fitting errors, which capture inconsistencies among Doppler velocity projections. Experimental results on a challenging small-scale hand gesture recognition dataset demonstrate that the proposed DoRF-guided Wi-Fi-based HAR approach significantly improves generalization capability, paving the way for robust real-world sensing deployments.
Abstract (translated)
随着IEEE 802.11bf工作组引入针对高级传感的无线局域网（WLAN）标准修正案，人们对利用Wi-Fi信道状态信息（CSI）进行远程传感的兴趣激增。近期的研究发现表明，通过从CSI中导出多普勒辐射场（DoRFs），学习统一的三维运动表示可以显著提升基于Wi-Fi的人体活动识别（HAR）的一般化能力。尽管取得了这一进展，但CSI信号仍然受到接入点（AP）时钟异步和环境及硬件源产生的加性噪声的影响。因此，即使采用了现有的预处理技术，用于DoRFs的CSI数据和多普勒速度投影依然容易受噪声和异常值的影响，从而限制了HAR性能。 为了解决这一挑战，我们提出了一种新的框架，利用多天线AP来抑制噪声，并根据DoRF拟合误差识别最具有信息量的天线，这些误差捕捉到了多普勒速度投影之间的不一致性。在一项针对小型手部手势识别数据集的实验中，我们的DoRF引导的基于Wi-Fi的人体活动识别方法显示出显著提升了泛化能力，为稳健的实际传感部署铺平了道路。
URL
https://arxiv.org/abs/2509.15129
