Efficient 2.5-D FEM-Based Scattering Analysis of the Human Body for RF Sensing
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866912970820288512 |
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| author | Wen, Haoqing D'Amico, Michele Oldoni, Matteo Fieramosca, Federica Rampa, Vittorio Savazzi, Stefano Wu, Qi Gentili, Gian Guido |
| author_facet | Wen, Haoqing D'Amico, Michele Oldoni, Matteo Fieramosca, Federica Rampa, Vittorio Savazzi, Stefano Wu, Qi Gentili, Gian Guido |
| contents | Model training for Device-Free Localization (DFL) and Radio-Frequency (RF) sensing heavily relies on large-scale datasets, which are difficult, expensive, and time-consuming to obtain through measurements. This paper proposes a fast 2.5-dimensional Finite Element Method (2.5-D FEM) for computing the scattering fields of a Body of Revolution (BoR) human model under the excitation of a z-directed dipole. The proposed method can evaluate the effect of human micro-movements through the statistical characteristics of the Received Signal Strength Indicator (RSSI). The numerical accuracy and the practical applicability of the proposed method are validated through comparisons with full-wave simulations and indoor RF sensing experiments. The simulation results show agreement with the experimental measurements, demonstrating that the method is a reliable tool for evaluating micro-movement-induced statistical variations. The proposed method provides a practical and efficient means for generating large-scale, labeled RF training datasets, thereby accelerating the development of indoor localization tools as well as the calibration and tuning of tomographic reconstruction methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_16255 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Efficient 2.5-D FEM-Based Scattering Analysis of the Human Body for RF Sensing Wen, Haoqing D'Amico, Michele Oldoni, Matteo Fieramosca, Federica Rampa, Vittorio Savazzi, Stefano Wu, Qi Gentili, Gian Guido Signal Processing Model training for Device-Free Localization (DFL) and Radio-Frequency (RF) sensing heavily relies on large-scale datasets, which are difficult, expensive, and time-consuming to obtain through measurements. This paper proposes a fast 2.5-dimensional Finite Element Method (2.5-D FEM) for computing the scattering fields of a Body of Revolution (BoR) human model under the excitation of a z-directed dipole. The proposed method can evaluate the effect of human micro-movements through the statistical characteristics of the Received Signal Strength Indicator (RSSI). The numerical accuracy and the practical applicability of the proposed method are validated through comparisons with full-wave simulations and indoor RF sensing experiments. The simulation results show agreement with the experimental measurements, demonstrating that the method is a reliable tool for evaluating micro-movement-induced statistical variations. The proposed method provides a practical and efficient means for generating large-scale, labeled RF training datasets, thereby accelerating the development of indoor localization tools as well as the calibration and tuning of tomographic reconstruction methods. |
| title | Efficient 2.5-D FEM-Based Scattering Analysis of the Human Body for RF Sensing |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2603.16255 |