  Radio tomographic imaging (RTI) is an emerging technology for localization of
physical objects in a geographical area covered by wireless networks. With
attenuation measurements collected at spatially distributed sensors, RTI
capitalizes on spatial loss fields (SLFs) measuring the absorption of radio
frequency waves at spatial locations along the propagation path. These SLFs can
be utilized for interference management in wireless communication networks,
environmental monitoring, and survivor localization after natural disasters
such as earthquakes. Key to the success of RTI is to accurately model shadowing
as the weighted line integral of the SLF. To learn the SLF exhibiting
statistical heterogeneity induced by spatially diverse environments, the
present work develops a Bayesian framework entailing a piecewise homogeneous
SLF with an underlying hidden Markov random field model. Utilizing variational
Bayes techniques, the novel approach yields efficient field estimators at
affordable complexity. A data-adaptive sensor selection strategy is also
introduced to collect informative measurements for effective reconstruction of
the SLF. Numerical tests using synthetic and real datasets demonstrate the
capabilities of the proposed approach to radio tomography and channel-gain
estimation.
