Computer Science ›› 2018, Vol. 45 ›› Issue (10): 69-77.doi: 10.11896/j.issn.1002-137X.2018.10.014

• Network & Communication • Previous Articles     Next Articles

Incremental Indoor Localization for Device Diversity Issues

XIA Jun, LIU Jun-fa, JIANG Xin-long, CHEN Yi-qiang   

  1. University of Chinese Academy of Sciences,Beijing 100049,China
    Research Center for Ubiquitous Computing Systems,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China
    Beijing Key Laboratory of Mobile Computing and Pervasive Device,Beijing 100190,China
  • Received:2018-03-03 Online:2018-11-05 Published:2018-11-05

Abstract: With the rapid development of Wireless Local Area Network(WLAN),the Received Signal Strength(RSS) based indoor localization becomes a hot area in research and application fields.Among various kinds of up-to-date indoor localization methods,fingerprint based methods are most widely used because of its good performance,and one feature of those methods is that the accuracy is determined by the identification of the training and the testing dataset.Howe-ver,in practical applications,there are three problems in existing fingerprint based methods or system.Firstly,the loca-lization error caused by device variance is a severe problem.Secondly,the wireless data are changing as the time passed,leading to the reduction of the prediction accuracy.Thirdly,traditional fingerprint based methods or system cannot avoid the dependency on a large amount of labeled data to keep effective positioning performance,which usually involves high cost in labor and time.To solve these problems,this paper proposed a incremental indoor localization method for device diversity issues,which keeps real-time update by training uncalibrated data that collected in localization.Experimental results show that the proposed method can increase the precision of overall indoor localization system,especially when error distance is between 3 and 5 meters.What’s more,this method possesses good advantage of timeliness compared with traditional indoor localization method on real BLE dataset.

Key words: Indoor localization, Device diversity, Incremental learning, Extreme learning machine, Internet of things

CLC Number: 

  • TP311
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