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<p>This study introduces the design and implementation of WiField, a commercial off-the-shelf (COTS)-device-deployed WiFi-based sensing system that can simultaneously identify multiple wavelength-level targets placed flexibly. Dissimilar to traditional radio frequency (RF)-based sensing schemes that focus on specific targets and RF links, WiField focuses on all media in the sensing area of the entire electric field. Consequently, WiField provides a unified framework for accurately characterizing diffraction, scattering, and other effects of targets across various positions, materials, and shapes. Notably, this combination of targets for different positions, numbers, and sizes only represents a special case. Furthermore, WiField provides a scheme that utilizes phaseless data to complete inverse mapping from an electric field to material distribution, thereby ensuring the simultaneous identification of multiple wavelength-level targets at any position and having the potential for deployment on a wide range of low-cost COTS devices. Our evaluation results reveal that WiField achieves an average identification accuracy of over 97% for 1–3 targets (size: 5 cm × 10 cm) with different materials randomly placed within an area of 1.05 m × 1.05 m. And the identification accuracy is about 83% for eight liquids placed in a 700 mL container.</p>

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The field-based model: a new perspective on RF-based material sensing

  • 期刊论文

  • 发表日期：

    2026年1月4日

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  • Fei Shang,
  • Haocheng Jiang,
  • Panlong Yang,
  • Dawei Yan,
  • Haohua Du,
  • Xiang-Yang Li

摘要

This study introduces the design and implementation of WiField, a commercial off-the-shelf (COTS)-device-deployed WiFi-based sensing system that can simultaneously identify multiple wavelength-level targets placed flexibly. Dissimilar to traditional radio frequency (RF)-based sensing schemes that focus on specific targets and RF links, WiField focuses on all media in the sensing area of the entire electric field. Consequently, WiField provides a unified framework for accurately characterizing diffraction, scattering, and other effects of targets across various positions, materials, and shapes. Notably, this combination of targets for different positions, numbers, and sizes only represents a special case. Furthermore, WiField provides a scheme that utilizes phaseless data to complete inverse mapping from an electric field to material distribution, thereby ensuring the simultaneous identification of multiple wavelength-level targets at any position and having the potential for deployment on a wide range of low-cost COTS devices. Our evaluation results reveal that WiField achieves an average identification accuracy of over 97% for 1–3 targets (size: 5 cm × 10 cm) with different materials randomly placed within an area of 1.05 m × 1.05 m. And the identification accuracy is about 83% for eight liquids placed in a 700 mL container.