Open AccessCSELM‐QE: A Composite Semi‐supervised Extreme Learning Machine with Unlabeled RSS Quality Estimation for Radio Map Construction
Author(s) -
Zhao Jianli,
Wang Wei,
Sun Qiuxia,
Huo Huan,
Sun Guoqiang,
Gao Xiang,
Zhu Chendi
Publication year - 2020
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2020.09.002
Subject(s) - rss , computer science , extreme learning machine , graph , construct (python library) , artificial intelligence , pattern recognition (psychology) , calibration , data mining , machine learning , artificial neural network , mathematics , statistics , theoretical computer science , programming language , operating system
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.