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 the 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 paper introduces a piecewise homogeneous SLF governed by a hidden Markov random field model. Efficient and tractable SLF estimators are developed by leveraging Markov chain Monte Carlo 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.
Bibliographical noteFunding Information:
This work was supported by NSF under Grants 1442686, 1508993, and 1509040
Manuscript received April 5, 2018; revised October 23, 2018 and December 4, 2018; accepted February 1, 2019. Date of current version March 5, 2019. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Mathini Sellathurai. This work was supported by NSF under Grants 1442686, 1508993, and 1509040. This paper was presented in part at the IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, Alberta, April 15-20, 2018. (Corresponding author: Georgios B. 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:, firstname.lastname@example.org; email@example.com; georgios@ umn.edu). Digital Object Identifier 10.1109/TSP.2019.2899806
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- Bayesian inference
- Markov chain Monte Carlo
- Radio tomography
- active learning
- channel-gain cartography