Device-Free Localization with Multiple Antenna Receivers: Simulations and Results

Device-Free Localization (DFL) is a passive radio method able to detect, estimate, and localize targets (e.g., human or other obstacles) that do not need to carry any electronic device. According to the Integrated Sensing And Communication (ISAC) paradigm, DFL networks exploit Radio Frequency (RF) devices, used for communication purposes, to evaluate also the excess attenuation due to targets moving in the monitored area, to estimate the target positions and movements. Several target models have been discussed in the literature to evaluate the target positions by exploiting the RF signals received by networked devices. Among these models, Electromagnetic (EM) body models emerged as an interesting research field for excess attenuation prediction using commercial RF devices. While these RF devices are usually single-antenna boards, the availability of low-cost multi-antenna devices e.g. those used in WLAN (Wireless Local Area Network) scenarios, allow us to exploit array-based signal processing techniques for DFL applications as well. Using an array-capable EM body model, this paper shows how to employ array-based processing to improve angular detection of targets. Unlike single-antenna devices that can provide only attenuation information, multi-antenna devices can provide both angular and attenuation estimates about the target location. To this end, simulations are presented and preliminary results are discussed. The proposed framework paves the way for a wider use of multi-antenna devices based, for instance, on WiFi6 and WiFi7 standards.

On the impact of the antenna radiation patterns in passive radio sensing

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

Full-wave EM simulation analysis of human body blockage by dense 2D antenna arrays

Recently, proposals of human-sensing-based services for cellular and local area networks have brought indoor localization to the attention of several research groups. In response to these stimuli, various Device-Free Localization (DFL) techniques, also known as Passive Localization methods, have emerged by exploiting ambient signals to locate and track individuals that do not carry any electronic device. This study delves into human passive indoor localization through full-wave electromagnetic simulations. For the scope, we exploit simulations from the commercial tool FEKO software that employs the Method of Moments (MoM). In particular, we collect and analyze the electric field values in a scenario constituted by a dense 2D/3D deployment of receivers in the presence of an anthropomorphic mobile target. The paper describes in detail the collected dataset and provides a first analysis based on a statistical approach. Possible use cases are also investigated through examples in the context of passive localization, sensing, and imaging.

Physics-informed generative neural networks for RF propagation prediction with application to indoor body perception

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.

An EM Body Model for Device-Free Localization with Multiple Antenna Receivers: A First Study

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.

Electromagnetic Models for Passive Detection and Localization of Multiple Bodies

The paper proposes a multi-body electromagnetic (EM) model for the quantitative evaluation of the influence of multiple human bodies in the surroundings of a radio link. Modeling of human-induced fading is the key element for the development of real-time Device-Free (or passive) Localization (DFL) and body occupancy tracking systems based on the processing of the Received Signal Strength (RSS) data recorded by radio-frequency devices. The proposed physical-statistical model, is able to relate the RSS measurements to the position, size, orientation, and random movements of people located in the link area. This novel EM model is thus instrumental for crowd sensing, occupancy estimation and people counting applications for indoor and outdoor scenarios. The paper presents the complete framework for the generic N-body scenario where the proposed EM model is based on the knife-edge approach that is generalized here for multiple targets. The EM-equivalent size of each target is then optimized to reproduce the body-induced alterations of the free space radio propagation. The predicted results are then compared against the full EM simulations obtained with a commercially available simulator. Finally, experiments are carried out to confirm the validity the proposed model using IEEE 802.15.4-compliant industrial radio devices.