Efficient 2.5-D FEM-Based Scattering Analysis of the Human Body for RF Sensing

TL;DR

This paper introduces a fast 2.5-D FEM method to efficiently simulate RF scattering from human models, enabling large-scale dataset generation for device-free localization and RF sensing applications.

Contribution

The paper presents a novel 2.5-D FEM approach for rapid RF scattering analysis of human bodies, improving dataset creation for RF sensing without extensive measurements.

Findings

The method achieves high accuracy compared to full-wave simulations.

It effectively captures micro-movement effects on RSSI.

Validated with indoor RF sensing experiments.

Abstract

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.