Adaptive Bayesian Radio Tomography

TL;DR

This paper introduces an adaptive Bayesian radio tomography method that models heterogeneous environments with a hidden Markov random field and uses MCMC for efficient estimation, improving localization accuracy.

Contribution

It proposes a novel piecewise homogeneous SLF model with an adaptive measurement collection strategy, enhancing radio tomography in complex environments.

Findings

Effective SLF estimation in heterogeneous environments

Improved localization accuracy demonstrated on real data

Adaptive measurement strategy reduces data collection needs

Abstract

Radio tomographic imaging (RTI) is an emerging technology to locate physical objects in a geographical area covered by wireless networks. From the attenuation measurements collected at spatially distributed sensors, radio tomography capitalizes on spatial loss fields (SLFs) measuring the absorption of radio frequency waves at each location along the propagation path. These SLFs can be utilized for interference management in wireless communication networks, environmental monitoring, and survivor localization after natural disaster such as earthquakes. Key to success of RTI is to model accurately the shadowing effects as the bi-dimensional integral of the SLF scaled by a weight function, which is estimated using regularized regression. However, the existing approaches are less effective when the propagation environment is heterogeneous. To cope with this, the present work introduces a piecewise homogeneous SLF governed by a hidden Markov random field (MRF) model. Efficient and tractable SLF estimators are developed by leveraging Markov chain Monte Carlo (MCMC) techniques. Furthermore, an uncertainty sampling method is developed to adaptively collect informative measurements in estimating the SLF. Numerical tests using synthetic and real datasets demonstrate capabilities of the proposed algorithm for radio tomography and channel-gain estimation.