Multitarget Human Scattering Modeling Using 2.5-D FEM for RF Sensing

This letter proposes an approximate model based on electromagnetic scattering superposition for multitarget indoor passive radio frequency (RF) sensing scenarios, aiming to predict the influence of multiple human bodies on wireless network link signal strengths. A line-of-sight condition is presented to simplify the compensation of shadowing effects. The scattering superposition model is validated numerically with a full-wave EM solver under different numbers of targets, separation distances, and occlusion conditions. The overall approximate framework is further validated against indoor measurements, showing good agreement in both received signal strength indicator and spatial variation trends across different link configurations. The proposed model provides a lightweight and computationally efficient solution for multitarget RF sensing, enabling low-cost yet accurate generation of large-scale synthetic databases for the training of machine-learning-based RF sensing networks with specific applications to people detection, people trajectory tracking, and RF tomography.