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.
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Manuscript received September 4, 2019; revised May 5, 2020; accepted June 9, 2020. Date of publication June 19, 2020; date of current version July 11, 2020. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Abd-Krim Seghouane. This work was supported in part by NSF under Grants 1508993, 1711471, and 1901134. This article was presented in part at the IEEE International Conference on Acoustics, Speech and Signal Processing, held in Brighton, U.K., during May 12-17, 2019 . (Corresponding author: Georgios Giannakis.) The authors are with the Department of Electrical and Computer Engineering and the Digital Technology Center, University of Minnesota, Minneapolis, MN 55455 USA (e-mail: email@example.com; firstname.lastname@example.org). Digital Object Identifier 10.1109/TSP.2020.3003130
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- Bayesian inference
- Radio tomography
- active learning
- channel-gain estimation
- variational Bayes