CSELM‐QE: A Composite Semi‐supervised Extreme Learning Machine with Unlabeled RSS Quality Estimation for Radio Map Construction. Issue 6 (1st November 2020)

• Record Type: Journal Article
• Authors: Zhao, Jianli; Wang, Wei; Sun, Qiuxia; Huo, Huan; Sun, Guoqiang; Gao, Xiang; Zhu, Chendi
• Is Part Of: Chinese journal of electronics. Volume 29:Issue 6(2020)
• Page Start: 1016
• Page End: 1024
• Publication Date: 2020-11-01
• Subjects: calibration -- graph theory -- indoor radio -- learning (artificial intelligence) -- RSSI -- wireless LAN; signal strength data -- quality estimation method -- CSELM‐QE -- Composite Semisupervised extreme learning machine -- unlabeled RSS Quality estimation -- radio map construction -- wireless local area network fingerprint‐based localization -- indoor localization -- location‐labeled fingerprints -- calibration effort -- traditional SELM methods -- received signal strength information -- location information -- Composite SELM method -- RSS signals -- composite graph -- unlabeled RSS data quality -- composite neighbor graph -- precise localization model -- localization accuracy; Wireless local area network (WLAN) -- WiFi fingerprints -- Radio map construction -- Semisupervised extreme learning machine (SELM) -- Received signal strength (RSS) data quality estimation -- Location based services; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2020.09.002
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : 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.