Volume 29 Issue 6
Dec.  2020
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ZHAO Jianli, WANG Wei, SUN Qiuxia, HUO Huan, SUN Guoqiang, GAO Xiang, ZHU Chendi. CSELM-QE: A Composite Semi-supervised Extreme Learning Machine with Unlabeled RSS Quality Estimation for Radio Map Construction[J]. Chinese Journal of Electronics, 2020, 29(6): 1016-1024. doi: 10.1049/cje.2020.09.002
Citation: ZHAO Jianli, WANG Wei, SUN Qiuxia, HUO Huan, SUN Guoqiang, GAO Xiang, ZHU Chendi. CSELM-QE: A Composite Semi-supervised Extreme Learning Machine with Unlabeled RSS Quality Estimation for Radio Map Construction[J]. Chinese Journal of Electronics, 2020, 29(6): 1016-1024. doi: 10.1049/cje.2020.09.002

CSELM-QE: A Composite Semi-supervised Extreme Learning Machine with Unlabeled RSS Quality Estimation for Radio Map Construction

doi: 10.1049/cje.2020.09.002
Funds:  This paper is supported by the Key R&D Plan of Shandong Province (No.2018GGX101045), the National Key R&D Plan (No.2018YFC0831002, No.2017YFC0804406), the Key Project of Industrial Transformation and Upgrading (Made in China 2025) (No.TC170A5SW), Humanity and Social Science Fund of the Ministry of Education (No.18YJAZH136, No.17YJCZH262), and the National Natural Science Foundation of China (No.61433012, No.U1435215).
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  • Corresponding author: SUN Qiuxia (corresponding author) received the Ph.D. degree in 2011 from Qingdao University, China. In 2014, she served as associate professor in College of Mathematics and System Science, Shandong University of Science and Technology. Her major research is big data analysis and complex system Modeling. (Email:qiuxiasun@sdust.edu.cn)
  • Received Date: 2019-09-17
  • Publish Date: 2020-12-25
  • Wireless local area network (WLAN) fingerprint-based localization has become the most attractive and popular approach for indoor localization. However, the primary concern for its practical implementation is the laborious manual effort of calibrating sufficient location-labeled fingerprints. The Semi-supervised extreme learning machine (SELM) performs well in reducing calibration effort. Traditional SELM methods only use Received signal strength (RSS) information to construct the neighbor graph and ignores location information, which helps recognizing prior information for manifold alignments. We propose Composite SELM (CSELM) method by using both RSS signals and location information to construct composite graph. Besides, the issue of unlabeled RSS data quality has not been solved. We propose a novel approach called Composite semisupervised extreme learning machine with unlabeled RSS Quality estimation (CSELM-QE) that takes into account the quality of unlabeled RSS data and combines the composite neighbor graph, which considers location information in the semi-supervised extreme learning machine. Experimental results show that the CSELM-QE could construct a precise localization model, reduce the calibration effort for radio map construction and improve localization accuracy. Our quality estimation method can be applied to other methods that need to retain high quality unlabeled Received signal strength data to improve model accuracy.
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