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Localization in Inconsistent WiFi Environments

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Robotics Research

Part of the book series: Springer Proceedings in Advanced Robotics ((SPAR,volume 10))

Abstract

As WiFi becomes more and more popular, indoor environments are often covered with access points (APs) many of which are temporarily generated by mobile devices. On the other hand, more and more infrastructural APs are equipped with beamforming capabilities which adjust radiation patterns according to client locations. These APs have large variations of signal fields. The inconsistent WiFi environments present a challenge for localization tasks when the client cannot communicate with APs. Here we report a new algorithm targeted at handling inconsistent APs. We develop a windowed majority voting and statistical hypothesis testing-based approach to remove APs with large displacements between reference and query data sets. We then refine the localization by applying maximum likelihood estimation method to the closed-form posterior location distribution over the filtered signal strength and AP sets in the time window. We determine the time window length by minimizing Shannon entropy of the posterior location distribution. We have implemented our algorithm and our method outperforms its counterparts in physical experiments. Our method achieves a mean localization error of less than 3.7 meters even when \(70\%\) of APs are inconsistent.

This work was supported in part by National Science Foundation under IIS-1318638, NRI-1426752, NRI-1526200, and NRI-1748161.

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Acknowledgements

We would like to thank C. Chou, B. Li, S. Yeh, A. Kingery, A. Angert, Y. Sun, M. Jin, D. Wang, Y. You, M. Momin, T. Sun, and H. Li for their input and contributions to the NetBot Lab at Texas A&M University.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Texas A&M University, College Station, 77843, TX, USA

    Hsin-Min Cheng & Dezhen Song

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  1. Hsin-Min Cheng
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  2. Dezhen Song
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Correspondence to Dezhen Song .

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Editors and Affiliations

  1. Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Nancy M. Amato

  2. Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA

    Greg Hager

  3. Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA

    Shawna Thomas

  4. Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

    Miguel Torres-Torriti

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Cheng, HM., Song, D. (2020). Localization in Inconsistent WiFi Environments. In: Amato, N., Hager, G., Thomas, S., Torres-Torriti, M. (eds) Robotics Research. Springer Proceedings in Advanced Robotics, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-28619-4_47

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