Adaptive Bayesian Radio Tomography

• Published in: IEEE transactions on signal processing Vol. 67; no. 8; pp. 1964 - 1977
• Main Authors: Donghoon Lee, Berberidis, Dimitris, Giannakis, Georgios B.
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.04.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: active learning; Adaptation models; Algorithms; Bayes methods; Bayesian analysis; Bayesian inference; channel-gain cartography; Communication networks; Computer simulation; Environmental monitoring; Hidden Markov models; Markov analysis; Markov chain Monte Carlo; Markov chains; Monte Carlo simulation; New technology; Radio attenuation; Radio tomography; Sensors; Shadow mapping; Tomography; Wave propagation; Weighting functions; Wireless communications; Wireless networks
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2019.2899806

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.