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Spatiotemporal Radio Tomographic Imaging with Bayesian Compressive Sensing for RSS-Based Indoor Target Localization

  • Conference paper
  • First Online:
Cloud Computing and Security (ICCCS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10603))

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Abstract

Wireless sensor network based device-free localization (DFL) is now widely used in security and monitoring systems for indoor and outdoor areas. Multipath fading induced noises often degrade the performance of the DFL security system. To address this problem, the paper firstly presents a spatiotemporal radio tomographic imaging (RTI) approach for the enhancement of localization. Specifically, the task of RTI can be formulated into a sparse Bayesian learning problem. In addition, two robust sparse Byesian learning algorithms are developed to handle with the low signal-to-noise-ratio (SNR) with heterogeneous noise. The proposed spatiotemporal RTI approach performs much better than traditional RTI with lower average errors in our four diverse cluttered indoor scenes. The localization results also highlight advantages of applying proposed robust sparse Bayesian learning algorithms in addressing missing estimations and outlier errors, and finally improving indoor target DFL performance.

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Acknowledgement

This work was supported by the National Natural Science Foundation of P.R. China under Grant No. 61375080, and the Key Program of Natural Science Foundation of Guangdong, China under Grant No. 2015A030311049. The Guangzhou science and technology project under Grant Nos. 201510010017 and 201604010101. The Special Project of Sharing Large Scientific Instruments and Equipments with the Public under Grant No. 2015B030304001.

Author information

Authors and Affiliations

  1. Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China

    Baolin Shang, Xiaobing Hong, Xuemei Guo & Guoli Wang

  2. Tianjin Key Laboratory of Intelligent Robotics, Institute of Robotics and Automatic Information System, Nankai University, Tianjin, China

    Jiaju Tan

  3. Vkan Certification and Testing Co. Ltd, Guangdong, China

    Gonggui Liu & Shouren Xue

Authors
  1. Baolin Shang
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  2. Jiaju Tan
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  3. Xiaobing Hong
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  4. Xuemei Guo
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  5. Guoli Wang
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  6. Gonggui Liu
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  7. Shouren Xue
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Corresponding author

Correspondence to Guoli Wang .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. National Dong Hwa University, Shoufeng, Taiwan

    Han-Chieh Chao

  3. China Information Technology Security Evaluation Center, Nanjing, China

    Xingang You

  4. Department of Computer Science, Purdue University, West Lafayette, Indiana, USA

    Elisa Bertino

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Shang, B. et al. (2017). Spatiotemporal Radio Tomographic Imaging with Bayesian Compressive Sensing for RSS-Based Indoor Target Localization. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10603. Springer, Cham. https://doi.org/10.1007/978-3-319-68542-7_45

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  • DOI: https://doi.org/10.1007/978-3-319-68542-7_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68541-0

  • Online ISBN: 978-3-319-68542-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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