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Electromagnetic (EM) body models predict the impact of human presence andmotions on the Radio-Frequency (RF) stray radiation received by wirelessdevices nearby. These wireless devices may be co-located members of a WirelessLocal Area Network (WLAN) or even cellular devices connected with a Wide AreaNetwork (WAN). Despite their accuracy, EM models are time-consuming methodswhich prevent their adoption in strict real-time computational imaging problemsand Bayesian estimation, such as passive localization, RF tomography, andholography. Physics-informed Generative Neural Network (GNN) models haverecently attracted a lot of attention thanks to their potential to reproduce aprocess by incorporating relevant physical laws and constraints. Thus, GNNs canbe used to simulate/reconstruct missing samples, or learn physics-informed datadistributions. The paper discusses a Variational Auto-Encoder (VAE) techniqueand its adaptations to incorporate a relevant EM body diffraction method withapplications to passive RF sensing and localization/tracking. The proposedEM-informed generative model is verified against classical diffraction-based EMbody tools and validated on real RF measurements. Applications are alsointroduced and discussed.

A physics-informed generative model for passive radio-frequency sensing