Michele D'Amico

Federica Fieramosca, Anastasia Maiolli, Alexander H. Paulus et al.

Human localization is gaining momentum in security, healthcare, logistics, and smart spaces applications. While global navigation systems are unreliable indoor, device-free (a.k.a. passive) localization methods that exploit human-induced perturbations of radio propagation can be effectively used. This paper investigates the use of a compact full-wave electromagnetic (EM) setup as a fast and reliable tool to simulate indoor Wi-Fi propagation for human sensing. The goal is to provide a practical baseline for validating simplified propagation models, such as diffraction-based descriptions, and to reduce the need for costly measurement campaigns. Two-dimensional attenuation maps from received signal strength are generated and compared in controlled environments, focusing on attenuation statistics and interference patterns. The simulations reproduce the main spatial features, though discrepancies remain due to simplified material characterization. Diffraction-aware refinements are proposed to mitigate these effects. Overall, the approach provides an efficient pre-measurement reference to support device-free system design and to guide experimental planning.

Vittorio Rampa, Federica Fieramosca, Stefano Savazzi et al.

Device-Free Localization (DFL) employs passive radio techniques capable to detect and locate people without imposing them to wear any electronic device. By exploiting the Integrated Sensing and Communication paradigm, DFL networks employ Radio Frequency (RF) nodes to measure the excess attenuation introduced by the subjects (i.e., human bodies) moving inside the monitored area, and to estimate their positions and movements. Physical, statistical, and ElectroMagnetic (EM) models have been proposed in the literature to estimate the body positions according to the RF signals collected by the nodes. These body models usually employ a single-antenna processing for localization purposes. However, the availability of low-cost multi-antenna devices such as those used for WLAN (Wireless Local Area Network) applications and the timely development of array-based body models, allow us to employ array-based processing techniques in DFL networks. By exploiting a suitable array-capable EM body model, this paper proposes an array-based framework to improve people sensing and localization. In particular, some simulations are proposed and discussed to compare the model results in both single- and multi-antenna scenarios. The proposed framework paves the way for a wider use of multi-antenna devices (e.g., those employed in current IEEE 802.11ac/ax/be and forthcoming IEEE 802.11be networks) and novel beamforming algorithms for DFL scenarios.

Federica Fieramosca, Vittorio Rampa, Michele D'Amico et al.

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 body localization and sensing. Physics-informed Generative Neural Network (GNN) models have been recently proposed to reproduce EM effects, namely to simulate or reconstruct missing data or samples by incorporating relevant EM principles and constraints. The paper discusses a Variational Auto-Encoder (VAE) model which is trained to reproduce the effects of human motions on the EM field and incorporate EM body diffraction principles. Proposed physics-informed generative neural network models are verified against both classical diffraction-based EM tools and full-wave EM body simulations.