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

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Chinese Journal of Electronics 2020年第6期29卷 1016-1024页

作者： ZHAO Jianli WANG Wei SUN Qiuxia HUO Huan SUN Guoqiang GAO Xiang ZHU Chendi College of Computer Science and Engineering Shandong University of Science and Technology College of Mathematics and Systems Science Shandong University of Science and Technology Faculty of Engineering and Information Technology University of Technology Sydney

Wireless local area network(WLAN)fingerprint-based localization has become the most attractive and popular approach for indoor ***,the primary concern for its practical implementation is the laborious manual effort of calibrating sufficient location-labeled *** Semi-supervised extreme learning machine(selm) performs well in reducing calibration *** 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 *** propose Composite selm(Cselm) method by using both RSS signals and location information to construct composite ***,the issue of unlabeled RSS data quality has not been *** 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 *** results show that the Cselm-QE could construct a precise localization model,reduce the calibration effort for radio map construction and improve localization *** quality estimation method can be applied to other methods that need to retain high quality unlabeled Received signal strength data to improve model accuracy.

关键词： localization accuracy location-labeled fingerprints calibration effort wireless local area network fingerprint-based localization indoor localization signal strength data Radio links and equipment Combinatorial mathematics calibration Composite Semisupervised extreme learning machine Radionavigation and direction finding unlabeled RSS data quality traditional selm methods RSS signals precise localization model graph theory Composite selm method location information radio map construction composite graph received signal strength information composite neighbor graph Cselm-QE wireless LAN indoor radio RSSI learning (artificial intelligence) quality estimation method unlabeled RSS Quality estimation

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