Simulation Tools and Techniques. 11th International Conference, SIMUtools 2019, Chengdu, China, July 8–10, 2019, Proceedings

Research Article

Bisecting K-Means Based Fingerprint Indoor Localization

  • @INPROCEEDINGS{10.1007/978-3-030-32216-8_1,
        author={Yuxing Chen and Wei Liu and Haojie Zhao and Shuling Cao and Shasha Fu and Dingde Jiang},
        title={Bisecting K-Means Based Fingerprint Indoor Localization},
        proceedings={Simulation Tools and Techniques. 11th International Conference, SIMUtools 2019, Chengdu, China, July 8--10, 2019, Proceedings},
        proceedings_a={SIMUTOOLS},
        year={2019},
        month={10},
        keywords={Fingerprint Bisecting K-means WiFi Indoor localization},
        doi={10.1007/978-3-030-32216-8_1}
    }
    
  • Yuxing Chen
    Wei Liu
    Haojie Zhao
    Shuling Cao
    Shasha Fu
    Dingde Jiang
    Year: 2019
    Bisecting K-Means Based Fingerprint Indoor Localization
    SIMUTOOLS
    Springer
    DOI: 10.1007/978-3-030-32216-8_1
Yuxing Chen1,*, Wei Liu1,*, Haojie Zhao1,*, Shuling Cao1,*, Shasha Fu1,*, Dingde Jiang2,*
  • 1: Xidian University
  • 2: University of Electronic Science and Technology of China
*Contact email: yxchen_2@stu.xidian.edu.cn, liuweixd@mail.xidian.edu.cn, hjzhao@stu.xidian.edu.cn, slcao_cn@stu.xidian.edu.cn, ssfu@stu.xidian.edu.cn, jiangdd99@sina.com

Abstract

This paper presents a fingerprint indoor localization system based on Bisecting k-means (BKM). Compared to k-means, BKM is a more robust clustering algorithm. 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 (RPs) into clusters. A series of experiments have been made to show that our system can greatly improve localization accuracy.