Machine Learning and Intelligent Communications. First International Conference, MLICOM 2016, Shanghai, China, August 27-28, 2016, Revised Selected Papers

Research Article

Reducing Calibration Effort for Indoor WLAN Localization Using Hybrid Fingerprint Database

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  • @INPROCEEDINGS{10.1007/978-3-319-52730-7_16,
        author={Mu Zhou and Yunxia Tang and Zengshan Tian and Feng Qiu},
        title={Reducing Calibration Effort for Indoor WLAN Localization Using Hybrid Fingerprint Database},
        proceedings={Machine Learning and Intelligent Communications. First International Conference, MLICOM 2016, Shanghai, China, August 27-28, 2016, Revised Selected Papers},
        proceedings_a={MLICOM},
        year={2017},
        month={2},
        keywords={WLAN Location fingerprint Interpolation Semi-supervised learning Manifold alignment},
        doi={10.1007/978-3-319-52730-7_16}
    }
    
  • Mu Zhou
    Yunxia Tang
    Zengshan Tian
    Feng Qiu
    Year: 2017
    Reducing Calibration Effort for Indoor WLAN Localization Using Hybrid Fingerprint Database
    MLICOM
    Springer
    DOI: 10.1007/978-3-319-52730-7_16
Mu Zhou1,*, Yunxia Tang1,*, Zengshan Tian1,*, Feng Qiu1,*
  • 1: Chongqing University of Posts and Telecommunications
*Contact email: zhoumu@cqupt.edu.cn, 13629735505@139.com, tianzs@cqupt.edu.cn, qiufeng245@outlook.com

Abstract

Due to the implementation ease and cost-efficiency, the indoor Wireless Local Area Network (WLAN) fingerprint based localization approach is preferred compared with the conventional trilateration localization approaches. In this paper, we propose a new semi-supervised learning algorithm based on manifold alignment with cubic spline interpolation to reduce the offline calibration effort for indoor WLAN localization using hybrid fingerprint database. The proposed approach significantly reduces the number of labeled training samples collected at each survey location by constructing the hybrid database via interpolation and semi-supervised manifold learning. We carry out extensive experiments in a ground-truth indoor environment to examine the localization accuracy of the proposed approach. The experimental results demonstrate that our approach can effectively reduce the calibration effort, as well as achieve high localization accuracy.