A Heuristic Link Selection for Radio Frequency Tomography Based on BCS: Principle and Method

Abstract:

Radio tomographic imaging as a coarse computational imaging method has attracted much attentions due to its potential applications in human surveillance. It utilizes the shadow fading characteristics of radio signals among wireless sensor network nodes to infer the targets' localization. The recovery process involves a large number of scanning links, which brings heavy burdens on node energy, communication routes and data storage. In general, there is a small number of targets and could be seen as a sparse signal compared with the original high-dimensional space. Hence, the key of scene recovery is the effective links selected. This part of work presents a heuristic link selection for radio tomographic localization system, which introduces a Bayesian compressive sensing (BCS) to heuristic link selection method for scene imaging reconstruction.

• Periodical: Advanced Materials Research (Volumes 1049-1050)
• Pages: 520-525
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1049-1050.520
• Online since: October 2014
• Authors: Xiao Xi Hao, Xue Mei Guo, Guo Li Wang
• Keywords: Bayesian Compressive Sensing, Heuristic Link Selection, Radio Tomographic Imaging

References

[1] Wilson J, Patwari N. Radio tomographic imaging with wireless networks[J]. IEEE Transactions on Mobile Computing, 2010, 9(5): 621-632.

DOI: 10.1109/tmc.2009.174

[2] Patwari N, Wilson J. RF sensor networks for device-free localization: Measurements, Models and Algorithms[J]. Proceedings of the IEEE, 2010, 98(11): 1961-(1973).

DOI: 10.1109/jproc.2010.2052010

[3] Pahlavan K, Ye Y, Khan U, et al. RF localization inside human body: enabling micro-robotic navigation for medical applications[C]. Tampere : International Conference on Localization and GNSS (ICL-GNSS), 2011. 133-139.

DOI: 10.1109/icl-gnss.2011.5955284

[4] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.

DOI: 10.1109/tit.2006.871582

[5] Kanso M A, Rabbat M G. Compressed RF tomography for wireless sensor networks: centralized and decentralized approaches[C]. Marina del Rey: Proceedings of the 5th IEEE International Conference on Distributed Computing in Sensor Systems. 2009. 173-186.

DOI: 10.1007/978-3-642-02085-8_13

[6] Vogel C R. Computational Methods for Inverse Problems[M]. SIAM: Society for Industrial and Applied Mathematics, 2002. 142-145.

[7] Tibshirani R. Regression shrinkage and selection via the lasso[J]. Journal of the Royal Statistical Society, 1996. 267-288.

DOI: 10.1111/j.2517-6161.1996.tb02080.x

[8] Ji S, Xue Y, Carin L. Bayesian compressive sensing[J]. IEEE Transactions on Signal Processing, 2008, 5(6): 621-632.

[9] Patwari N, Agrawal P. Effects of correlated shadowing: Connectivity, localization, and RF tomography[C]. Missouri: International Conference on Information Processing in Sensor Networks, 2008. 82-93.

DOI: 10.1109/ipsn.2008.7

[10] Tipping M. Sparse Bayesian learning and the relevance vector machine[J]. Machine Learning Research, 2001. 1: 211-244.

[11] Cover T M, Thomas J A. Elements of Information Theory(Second edition)[M]. SIAM: Wiley-interscience, 2012. 247-250.