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Link Selection in Radio Tomographic Imaging with Backprojection Transformation

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Book cover Proceedings of 2018 Chinese Intelligent Systems Conference

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 529))

Abstract

Multi-path interference in Radio Tomographic Imaging(RTI), often brings unpredictable degeneration to the reconstructed image and degrades the accuracy of Device-Free Localization(DFL). By analyzing the reconstruction process of RTI, this paper certifies that the shadow fading can be transformed as a linear combination of the contribution of RF links. This transformation named backprojection indicates that the selection of informative RF links is helpful to resist the multi-path noise. Then a method based on Bayesian Compressive Sensing(BCS) and backprojection is proposed to figure out the contributive RF links and reconstruct the image. Besides, by transforming the reconstruction issue of high-dimensional image into the analysis problem of low-dimensional measured data, the proposed method also decreases the time complexity of BCS without reducing the accuracy. The experimental results show the effectiveness and practicability of the method in RTI and DFL.

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References

  1. J. Wilson, N. Patwari, Radio tomographic imaging with wireless networks. IEEE Trans. Mobile Comput. 9(5), 621–632 (2010)

    Article  Google Scholar 

  2. C. Alippi, M. Bocca, G. Boracchi, N. Patwari, M. Roveri, RTI goes wild: radio Ttomographic imaging for outdoor people detection and localization. IEEE Trans. Mobile Comput. 15, 2585–2598

    Google Scholar 

  3. N. Patwari, J. Wilson, RF sensor networks for device-free localization: measurements, models, and algorithms. Proc. IEEE 98(11), 1961–1973 (2010)

    Article  Google Scholar 

  4. C. Anderson, R. Martin, T. Walker, R. Thomas, Radio tomography for roadside surveillance. IEEE J. Selected top. Sig. Proc. 8(1), 66–79 (2014)

    Article  Google Scholar 

  5. M. Bocca, O. Kaltiokallio, N. Patwari, Radio Tomographic Imaging for Ambient Assisted Living, vol. 362 (Springer, Berlin Heidelberg, 2012), pp. 108–130

    Google Scholar 

  6. Y. Guo, K. Huang, N. Jiang, X. Guo, Y. Li, G. Wang, An exponential-rayleigh model for RSS-based device-free localization and tracking. IEEE Trans. Mobile Comput. 14(3), 484–494 (2015)

    Article  Google Scholar 

  7. Z. Yang, K. Huang, X. Guo, G. Wang, A real-time device-free localization system using correlated RSS measurements. Eurasip J. Wireless Commun. Networking, pp. 1–12 (2013)

    Google Scholar 

  8. Y. Luo, K. Huang, X. Guo, G. Wang, A hierarchical RSS model for RF-based device-free localization. Pervasive Mobile Comput. 31, 124–136 (2016)

    Article  Google Scholar 

  9. S. Ji, Y. Xue, L. Carin, Bayesian compressive sensing. IEEE Trans. Signal Proc. 56(6), 2346–2356 (2008)

    Article  MathSciNet  Google Scholar 

  10. M. Tipping, A. Smola, Sparse bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1(3), 211–244 (2001)

    MathSciNet  MATH  Google Scholar 

  11. K. Huang, Y. Guo, L. Yang, X. Guo, G. Wang, Optimal information based adaptive compressed radio tomographic imaging, in Proceedings of the 32th Chinese Intelligent Systems Conference, vol. 12, no. 7, pp. 7438–7444 (2013)

    Google Scholar 

  12. K. Huang, Y. Guo, X. Guo, G. Wang, Heterogeneous Bayesian compressive sensing for sparse signal recovery. IET Proc. Iet 8(9), 1009–1017 (2014)

    Google Scholar 

  13. K. Huang, S. Tan, Y. Luo, X. Guo, G. Wang, Enhanced radio tomographic imaging with heterogeneous Bayesian compressive sensing. Pervasive Mobile Comput. 40(9), 450–463 (2017)

    Article  Google Scholar 

  14. A. Smola, B. Scolkopf, A Tutorial on Support Vector Regression. Kluwer Academic Publishers, 14(3), pp. 199–222 (2004)

    Article  MathSciNet  Google Scholar 

  15. J. Wilson, N. Patwari, A fade-level skew-laplace signal strength model for device-free localization with wireless networks. IEEE Trans. Mobile Comput. 11(6), 947–958 (2012)

    Article  Google Scholar 

  16. O. Kaltiokallio, M. Bocca, N. Patwari, A fade level-based spatial model for radio tomographic imaging. IEEE Trans. Mobile Comput. 13(6), 1159–1172 (2014)

    Google Scholar 

  17. K. Huang, Y. Luo, X. Guo, G. Wang, Data-efficient radio tomographic imaging with adaptive Bayesian compressive sensing, in IEEE International Conference on Information and Automation, pp. 1859–1864 (2015)

    Google Scholar 

  18. M. Khaledi, SK. Kasera, N. Patwari, M. Bocca, Energy efficient radio tomographic imaging, In Eleventh IEEE International Conference on Sensing, pp. 609–617 (2014)

    Google Scholar 

  19. D. Schuhmacher, B.T. Vo, B.N. Vo, A consistent metric for performance evaluation of multi-object filters. IEEE Trans. Signal Process 56, 3447–3457 (2008)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of P.R. China under Grant Nos. 61772574 and 61375080, the Key Program of Natural Science Foundation of Guangdong, China under Grant No. 2015A030311049.

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Authors and Affiliations

  1. Institute of Robotics and Automatic Information System, Nankai University, Tianjin, China

    Jiaju Tan

  2. Tianjin Key Laboratory of Intelligent Robotics, Tianjin, China

    Jiaju Tan

  3. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China

    Xuemei Guo & Guoli Wang

  4. Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Guangzhou, China

    Xuemei Guo & Guoli Wang

Authors
  1. Jiaju Tan
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  2. Xuemei Guo
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  3. Guoli Wang
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Corresponding author

Correspondence to Guoli Wang .

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Editors and Affiliations

  1. Beihang University, Beijing, China

    Yingmin Jia

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Junping Du

  3. University of Science and Technology Beijing, Beijing, China

    Weicun Zhang

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Tan, J., Guo, X., Wang, G. (2019). Link Selection in Radio Tomographic Imaging with Backprojection Transformation. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2018 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 529. Springer, Singapore. https://doi.org/10.1007/978-981-13-2291-4_48

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