Near-Field Coupling Operator Model for RIS-Based Human Sensing
DOI:
https://doi.org/10.31838/NJAP/07.03.21Keywords:
Reconfigurable intelligent surfaces, near-field coupling, human sensing, operator learning, dielectric perturbation, localization, mutual impedanceAbstract
Reconfigurable Intelligible Surfaces (RIS) have become a potentially effective device-free human sensing application because they can control the wavefronts using low-power density and high spatial resolution. Nevertheless, the current models of RIS sensing are largely based on far-field considerations as well as do not take into account the highly reactive-field interactions that prevail when the human subjects are in close proximity to the RIS. These near-field distortions have a serious impact on mutual coupling, dielectric perturbation, and scattering behaviour resulting in poor localization and sensing resolution. To overcome these drawbacks, a Near-Field Coupling Operator (NFCO) model is presented in this paper that analytically represents the interaction between RIS and humans through mutual impedance, evanescent-field behaviour and dielectric discontinuities in a single operator model. The suggested NFCO is a computationally and physically interpretable model of near-field EM behaviour that does not involve full-wave simulation which can be costly. A MATLAB-HFSS co-simulation model is constructed to analyse the NFCO model in 4000 RIS-human configurations. Findings indicate that NFCO enhances the performance of human localization by 39 percent, perturbation estimation error by 37 percent and requires less time to converge as compared to the traditional far-field RIS sensing models. Sensitivity analysis establishes the robustness in positional variation and dielectric variations whereas near-field maps visually validate perturbation modeling. The suggested NFCO framework creates a powerful base towards the next-generation RIS-enabled human sensing in smart environments, indoor monitoring, and healthcare applications.
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