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Bisecting k-means based fingerprint indoor localization

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Abstract

This paper presents an indoor localization system based on Bisecting k-means (BKM). BKM is a more robust clustering algorithm compared to k-means. Specifically, BKM based indoor localization consists of two stages: offline stage and online positioning stage. In the offline stage, BKM is used to divide all the reference points into k clusters. A series of experiments have been made to show that our system can greatly improve localization accuracy.

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Acknowledgements

The financial support of the program of Key Industry Innovation Chain of Shaanxi Province, China (2017ZDCXL-GY-04-02), of the program of Xi’an Science and Technology Plan (201805029YD7CG13(5) ), Shaanxi, China, of Key R&D Program—The Industry Project of Shaanxi (Grant No. 2018GY-017), of Key R&D Program—The Industry Project of Shaanxi (Grant No. 2017GY-191) and of Education Department of Shaanxi Province Natural Science Foundation, China (15JK1742) are gratefully acknowledged.

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Authors and Affiliations

  1. State Key Laboratory of Integrated Services Network, Xidian University, Xi’an, 710071, Shaanxi, People’s Republic of China

    Yuxing Chen, Wei Liu, Haojie Zhao, Shulin Cao & Shasha Fu

  2. School of Astronautics and Aeronautic, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China

    Dingde Jiang

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  1. Yuxing Chen
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  2. Wei Liu
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  3. Haojie Zhao
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  4. Shulin Cao
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Correspondence to Yuxing Chen.

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Chen, Y., Liu, W., Zhao, H. et al. Bisecting k-means based fingerprint indoor localization. Wireless Netw 27, 3497–3506 (2021). https://doi.org/10.1007/s11276-019-02222-0

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