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Joint access point fuzzy rough set reduction and multisource information fusion for indoor Wi-Fi positioning

  • S.I: Cognitive-inspired Computing and Applications
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Abstract

With the increasing maturity and popularity of wireless network techniques, indoor Wi-Fi positioning will inevitably become a significant application in indoor location-based services. In this circumstance, there is normally no control over the number of access points (APs) and the diversity of the Wi-Fi signal distribution, which may significantly deteriorate the positioning effectiveness as well as the system efficiency. To address this issue, we first adopt the fuzzy information entropy-based fuzzy rough set to conduct redundant APs reduction. Second, we calculate the Wasserstein distance between the signal distribution at the target position and the one at each Reference Point (RP) by the Wasserstein distance method. Third, the multisource information fusion method based on the Dempster–Shafer evidence theory is exerted to construct the matching RPs set. Finally, the abundant experiments and results in a realistic indoor Wi-Fi environment testify that the proposed method is able to preserve satisfactory localization performance as well as reduce the computation overhead of localization.

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Notes

  1. The fingerprint database is recognized as an information system.

  2. The area division is based on the functionality of each sub-area [28].

  3. Because different types of the kernel function have little effect on the result, the Epanechnikov kernel function in [31] is chosen in our paper.

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Acknowledgements

This work was supported in part by the Science and Technology Research Program of Chongqing Municipal Education (KJZD-K202000605, KJQN202000630), the Chongqing Natural Science Foundation Project (cstc2020jcyj-msxmX0842), and the National Natural Science Foundation of China (61901076, 61704015).

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Authors and Affiliations

  1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Wei Nie, Zhu Liu, Mu Zhou, Xiaolong Yang & Wei He

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  1. Wei Nie
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  2. Zhu Liu
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  3. Mu Zhou
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Correspondence to Mu Zhou.

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There was no conflict of interest in the submission of the manuscript, and the author agreed to publish it. I declare that the work described is an original study that has not been published before and is not considered elsewhere, in whole or in part.

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Nie, W., Liu, Z., Zhou, M. et al. Joint access point fuzzy rough set reduction and multisource information fusion for indoor Wi-Fi positioning. Neural Comput & Applic 34, 2677–2689 (2022). https://doi.org/10.1007/s00521-021-05934-7

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  • DOI: https://doi.org/10.1007/s00521-021-05934-7

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