Device-Free Localization (DFL) employs passive radio techniques capable to detect and locate people without imposing them wear any electronic device. By exploiting the Integrated Sensing Communication paradigm, DFL networks employ Radio Frequency (RF) nodes measure excess attenuation introduced by subjects (i.e., human bodies) moving inside monitored area, estimate their positions movements. Physical, statistical, ElectroMagnetic (EM) models have been proposed in literature body according RF...

Electromagnetic (EM) body models predict the impact of human presence and motions on Radio-Frequency (RF) stray radiation received by wireless devices nearby. These may be co-located members a Wireless Local Area Network (WLAN) or even cellular connected with Wide (WAN). Despite their accuracy, EM are time-consuming methods which prevent adoption in strict real-time computational imaging problems Bayesian estimation, such as passive localization, RF tomography, holography. Physics-informed...

Device-free localization (DFL) systems exploit the human-induced perturbations of electromagnetic (EM) fields as a privacy-preserving sensing tool for passive detection, recognition, localization, and tracking. Without wearing any electronic device, monitored subjects (targets) modify EM field (e.g., Received Signal Strength - RSS) in way that depends on their location relative to wireless devices. Thus, DFL specific radio maps reconstruct body-induced alterations enable motion These can be...

Electromagnetic (EM) body models based on the scalar diffraction theory allow to predict impact of subject motions radio propagation channel without requiring a time-consuming full-wave approach. On other hand, they are less effective in complex environments characterized by significant multipath effects. Recently, emerging sensing applications have proposed adoption smart antennas with non-isotropic radiation characteristics improve coverage. This letter investigates antenna patterns...

Electromagnetic (EM) body models predict the impact of human presence and motions on Radio-Frequency (RF) field originated from wireless devices nearby. Despite their accuracy, EM are time-consuming methods which prevent adoption in strict real-time computational imaging problems Bayesian estimation, such as passive localization, RF tomography, holography. Physics-informed Generative Neural Network (GNN) have recently attracted a lot attention thanks to potential reproduce process by...

Electromagnetic (EM) body models designed to predict Radio-Frequency (RF) propagation are time-consuming methods which prevent their adoption in strict real-time computational imaging problems, such as human localization and sensing. Physics-informed Generative Neural Network (GNN) have been recently proposed reproduce EM effects, namely simulate or reconstruct missing data samples by incorporating relevant principles constraints. The paper discusses a Variational Auto-Encoder (VAE) model is...

Electromagnetic (EM) body models predict the impact of human presence and motions on Radio-Frequency (RF) field originated from wireless devices nearby. Despite their accuracy, EM are time-consuming methods which prevent adoption in strict real-time computational imaging problems Bayesian estimation, such as passive localization, RF tomography, holography. Physics-informed Generative Neural Network (GNN) have recently attracted a lot attention thanks to potential reproduce process by...

Recently, proposals of human-sensing-based services for cellular and local area networks have brought indoor localization to the attention several research groups. In response these stimuli, various Device-Free Localization (DFL) techniques, also known as Passive methods, emerged by exploiting ambient signals locate track individuals that do not carry any electronic device. This study delves into human passive through full-wave electromagnetic simulations. For scope, we exploit simulations...

Electromagnetic (EM) body models predict the impact of human presence and motions on Radio-Frequency (RF) field originated from wireless devices nearby. Despite their accuracy, EM are time-consuming methods which prevent adoption in strict real-time computational imaging estimation problems, such as passive localization, RF tomography, holography. Physics-informed Generative Neural Network (GNN) have recently attracted a lot attention thanks to potential reproduce process by incorporating...

Electromagnetic (EM) body models predict the impact of human presence and motions on Radio-Frequency (RF) stray radiation received by wireless devices nearby. These may be co-located members a Wireless Local Area Network (WLAN) or even cellular connected with Wide (WAN). Despite their accuracy, EM are time-consuming methods which prevent adoption in strict real-time computational imaging problems Bayesian estimation, such as passive localization, RF tomography, holography. Physics-informed...