[Paper Review] Electromagnetic Models for Passive Detection and Localization of Multiple Bodies

Vittorio Rampa, Gian Guido Gentili+2 more|arXiv (Cornell University)|Apr 15, 2021

Indoor and Outdoor Localization Technologies63 references20 citations

TL;DR

This paper presents a novel physical-statistical electromagnetic (EM) model for passive, device-free localization (DFL) of multiple human bodies using received signal strength (RSS) measurements. Based on scalar diffraction theory and generalized knife-edge diffraction, the model predicts RSS fluctuations due to multiple bodies' positions, sizes, orientations, and small movements, outperforming linear superposition models with improved accuracy in both simulations and real-world experiments using IEEE 802.15.4 devices.

ABSTRACT

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.

Motivation & Objective

• To develop a physically grounded, scalable EM model for predicting RSS variations caused by multiple human bodies in RF links.
• To overcome limitations of linear superposition models that neglect mutual interactions between multiple targets.
• To enable accurate, real-time device-free localization (DFL) and occupancy tracking in indoor and outdoor environments.
• To provide a validated framework for crowd sensing, people counting, and human presence monitoring using off-the-shelf RF devices.
• To generalize the single- and dual-body models to an arbitrary N-body scenario with analytical rigor.

Proposed method

• Uses scalar diffraction theory to model EM field perturbations from multiple bodies as diffraction and multipath components.
• Applies the knife-edge approximation to represent each 3D human body as a 2D absorbing surface, enabling analytical treatment.
• Derives a full analytical expression for global extra attenuation in N-body scenarios using recursive inclusion of diffraction terms.
• Incorporates target-specific parameters: position, size, orientation, and small random movements around nominal positions.
• Optimizes the EM-equivalent size of each body to match free-space propagation losses observed in full-wave simulations.
• Validates the model against full-wave EM simulations (FEKO) and real RSS measurements from IEEE 802.15.4-compliant devices.

Experimental results

Research questions

• RQ1 How can the electromagnetic field perturbations caused by multiple human bodies be modeled analytically in a radio link?
• RQ2 To what extent do mutual diffraction effects between multiple bodies degrade the accuracy of linear superposition models?
• RQ3 Can a physical-statistical model based on scalar diffraction accurately predict RSS variations due to multiple moving bodies?
• RQ4 How does the model’s prediction accuracy compare to full-wave simulations and real-world measurements?
• RQ5 What are the dominant error sources affecting RSS prediction in multi-body scenarios?

Key findings

• The proposed multi-body model (MBM) achieves an average RSS prediction error of about 4 dB when compared to real-world measurements using IEEE 802.15.4 devices.
• The probabilistic multi-body model (PMBM) shows a higher average error of about 6 dB, primarily due to stochastic fluctuations from body movements.
• The model successfully captures the combined effects of body size, position, orientation, and small random movements on RSS variations.
• Discrepancies between model and measurements are largest when one or both targets are near the transmitter or receiver, due to increased shadowing and signal loss.
• The model outperforms linear superposition models, especially in configurations where mutual diffraction terms between targets are non-negligible.
• The model's validity is confirmed through comparison with full-wave EM simulations (FEKO), showing strong agreement in both static and dynamic scenarios.