Device-free Localization using Received Signal Strength Measurements in Radio Frequency Network

TL;DR

This paper introduces a diffraction-based model for device-free localization using RSS measurements, improving accuracy by 36% through particle filtering and experimental validation.

Contribution

A novel diffraction theory-based model for RSS variation in RF networks that better explains experimental phenomena and enhances localization accuracy.

Findings

Improved localization accuracy by at least 36% with the new model.

Effective tracking of multiple targets with 25% error reduction.

Validated model performance through real-world experiments with 8 sensors.

Abstract

Device-free localization (DFL) based on the received signal strength (RSS) measurements of radio frequency (RF)links is the method using RSS variation due to the presence of the target to localize the target without attaching any device. The majority of DFL methods utilize the fact the link will experience great attenuation when obstructed. Thus that localization accuracy depends on the model which describes the relationship between RSS loss caused by obstruction and the position of the target. The existing models is too rough to explain some phenomenon observed in the experiment measurements. In this paper, we propose a new model based on diffraction theory in which the target is modeled as a cylinder instead of a point mass. The proposed model can will greatly fits the experiment measurements and well explain the cases like link crossing and walking along the link line. Because the measurement model is nonlinear, particle filtering tracing is used to recursively give the approximate Bayesian estimation of the position. The posterior Cramer-Rao lower bound (PCRLB) of proposed tracking method is also derived. The results of field experiments with 8 radio sensors and a monitored area of 3.5m 3.5m show that the tracking error of proposed model is improved by at least 36 percent in the single target case and 25 percent in the two targets case compared to other models.