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Wireless Networks

, Volume 22, Issue 5, pp 1751–1766 | Cite as

FISCP: fine-grained device-free positioning system for multiple targets working in sparse deployments

  • Binbin Xie
  • Dingyi Fang
  • Tianzhang Xing
  • Lichao Zhang
  • Xiaojiang ChenEmail author
  • Zhanyong Tang
  • Anwen Wang
Article

Abstract

Device-free localization has long been playing a key role in the anti-intrusion applications. However, current multi-target localization solutions mainly use uneconomical equipment, which are not cost-efficient for large-scale scenarios . Moreover, they only consider the line-of-sight (LoS) path signals distorted by the targets, hence can’t exactly pinpoint the locations when faced with multipath effects and non-line-of-sight (NLoS), which are typical in real-world deployments. In this paper, we propose FISCP, a fine-grained device-free positioning system for multiple targets working in sparse deployments. The RFID passive tags are employed, which are much cheaper than other devices for localization. Meanwhile, unlike past approaches, which ignore the multipath effects or even take multipath as detrimental, FISCP exploits the dynamic distortion in multipath caused by the targets and considers the distortion as fingerprints for localization. We make a prototype system for FISCP using the commercial off-the-shelf products, including RFID systems and omnidirectional antennas, and develop a software program for the RFID systems. All the experiments are conducted in the deployments where the distance interval between each pair of tags is 1.2 m, and the deployments are sparse with respect to the short communication range of passive RFID systems (from a few meters up to tens of meters). The results of our experiments demonstrate that FISCP is effective in multi-target localization with low localization errors of 0.33 m in average.

Keywords

Fine-grained Device-free localization Multiple targets Multipath 

Notes

Acknowledgments

This work is supported by Project National Key Technology R&D Program 2013BAK01B02 and Project NSFC (61170218, 61272461, 61373177).

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Binbin Xie
    • 1
  • Dingyi Fang
    • 1
  • Tianzhang Xing
    • 1
  • Lichao Zhang
    • 1
  • Xiaojiang Chen
    • 1
    Email author
  • Zhanyong Tang
    • 1
  • Anwen Wang
    • 1
  1. 1.School of Information Science and TechnologyNorthwest UniversityXi’anChina

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