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

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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.

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Notes

  1. The distance interval between each pair of tags is 1.2 m, which is sparse compared to the communication range of RFID systems (tens of meters).

  2. The multipath profile denotes all the distinguishable paths of signals from the RFID’s reader to each tag.

  3. An affected tag stands for the tag whose multipath profile is distorted due to the target appearing.

  4. The selective fingerprints mean that we don’t need compare the real-time measured fingerprints with all the reference fingerprints. It is efficient to get the optimal value by comparing the real-time fingerprints with a few of reference fingerprints in our method.

  5. All the tags in Fig. 5(c), (d) are contentious tags.

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Acknowledgments

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

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Authors and Affiliations

  1. School of Information Science and Technology, Northwest University, Xi’an, China

    Binbin Xie, Dingyi Fang, Tianzhang Xing, Lichao Zhang, Xiaojiang Chen, Zhanyong Tang & Anwen Wang

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  1. Binbin Xie
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  2. Dingyi Fang
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  3. Tianzhang Xing
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  4. Lichao Zhang
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Correspondence to Xiaojiang Chen.

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Xie, B., Fang, D., Xing, T. et al. FISCP: fine-grained device-free positioning system for multiple targets working in sparse deployments. Wireless Netw 22, 1751–1766 (2016). https://doi.org/10.1007/s11276-016-1253-8

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