用多天线信道状态信息优化手势识别，提升实际场景泛化能力
Doppler Radiance Field-Guided Antenna Selection for Improved Generalization in Multi-Antenna Wi-Fi-based Human Activity Recognition
- 基于多天线信号构建多普勒辐射场，通过拟合误差筛选最优天线
- 在小规模手势数据集上显著提升跨设备、跨环境识别准确率
- 适合需要高鲁棒性的无线感知系统研发人员参考
随着IEEE 802.11bf任务组推进无线局域网标准的传感增强，利用Wi-Fi信道状态信息（CSI）进行远程感知的兴趣大增。最新研究表明，通过从CSI中提取的多普勒辐射场（DoRFs）学习统一的三维运动表征，可显著提升基于Wi-Fi的人体活动识别（HAR）的泛化能力。然而，由于接入点（AP）时钟异步及环境与硬件带来的加性噪声，即使经过现有预处理，CSI数据和用于生成DoRFs的多普勒速度投影仍受噪声和异常值影响，制约了识别性能。为此，本文提出一种针对多天线接入点的新框架，通过分析多普勒速度投影间的不一致性，利用DoRF拟合误差抑制噪声并识别最有效天线。在具有挑战性的小型手势识别数据集上的实验表明，所提方法显著增强了基于多天线的DoRF引导式Wi-Fi HAR系统的泛化能力，为实际部署中的鲁棒感知提供了可行路径。
原文摘要 · Abstract (English)
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.
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