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Human-Computer Interaction and Pervasive Computing     
Fusion feature based semi-supervised manifold localization method
HUANG Zheng-yu, JIANG Xin-long, LIU Jun-fa, CHEN Yi-qiang, GU Yang
1. College of Information Engineering, Xiangtan University, Xiangtan 411105, China;
2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
3. Beijing Key Laboratory of Mobile Computing and Pervasive Device, Beijing 100190, China;
4. University of Chinese Academy of Sciences, Beijing 100190, China
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Wi-Fi and Bluetooth Low Energy based single mode localization methods cannot get satisfactory performance on localization accuracy, robustness and universality. In the training phase, a large amount of calibrated data is required to train a model. A semi-supervised localization method was proposed based on fusing features of Wi-Fi and Bluetooth low energy signals in order to solve these problems. Wi-Fi and Bluetooth Low Energy based localization methods were effectively used, and semi-supervised manifold was employed to import a vast amount of uncalibrated data for model training. Experimental results show that the proposed fusion feature can increase the indoor localization accuracy by more than 20% as well as improving robustness compared with single feature. The semisupervised manifold localization method can dramatically reduce labeled calibration samples by 90%.

Published: 25 April 2017
CLC:  TP 391  
Cite this article:

HUANG Zheng-yu, JIANG Xin-long, LIU Jun-fa, CHEN Yi-qiang, GU Yang. Fusion feature based semi-supervised manifold localization method. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(4): 655-662.



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