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RFID-Based WIMEC-LANDMARC Indoor Location Algorithm

He Xu,Peng Li

Published 2020 in 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)

ABSTRACT

In view of the low positioning accuracy caused by the multi-path effect of Location Identification Based on Dynamic Active RFID Calibration(LANDMARC) system and the existing factors that affects the stability of the system, an RFID-Based LANDMARC indoor positioning optimization algorithm, namely Weigth Improverment and Mean Error Correction(WIMEC-LANDMARC), is proposed. The accuracy of the positioning system is improved by means of the average error correction, that is, the LANDMARC algorithm is adopted to locate the neighbor reference tags, and the average error is obtained to correct the initial position of the target to be measured and reduce the error of the positioning result. By improving the formula of reference tags' weight, the system outliers are eliminated and the stability of the positioning system is improved when Euclidean distance is zero. The experimental results show that the WIMEC-LAND MARC algorithm can effectively reduce the error of the target to be measured and eliminate the outliers, thus achieving the effect of improving the accuracy and stability of LANDMARC indoor positioning algorithm.

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)

  • Publication date

    2020-11-01

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00084
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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