Research Article
Reducing Calibration Effort for Indoor WLAN Localization Using Hybrid Fingerprint Database
@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
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.