Doppler Radiance Field-Guided Antenna Selection for Improved Generalization in Multi-Antenna Wi-Fi-based Human Activity Recognition

• URL: http://arxiv.org/abs/2509.15129v1
• Date: Thu, 18 Sep 2025 16:40:14 GMT
• Title: Doppler Radiance Field-Guided Antenna Selection for Improved Generalization in Multi-Antenna Wi-Fi-based Human Activity Recognition
• Authors: Navid Hasanzadeh, Shahrokh Valaee,
• Abstract summary: We propose a novel framework for multi-antenna APs to suppress noise and identify the most informative antennas based on DoRF fitting errors.<n> 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.
• Score: 20.859205920322676
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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