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RSSI indoor localization through a Bayesian strategy

  • Fu Zhou
    ,
  • Kaixian Lin
    ,
  • Aifeng Ren
    ,
  • Dongjian Cao
    ,
  • Zhiya Zhang
    ,
  • Masood Ur-Rehman
  • Xidian University
    ,
  • Queen Mary University of London
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Abstract

A method to locate the position of the user in an indoor environment employing Bayesian theory is presented in this paper. A detailed analysis of the positional accuracy is carried out evaluating effects of two major degradation factors namely the measurement and calculation errors. The proposed technique makes use of the Gaussian distribution of random data in indoor Zigbee propagation model based on received signal strength indicator (RSSI) and the triangular positioning algorithm, maximum likelihood estimation (MLE) and Bayesian theory. It identifies the user's location calculating the maximum probability point. The proposed method offers high accuracy levels with a mean error of 0.1363m as compared to the mean error values of 1.4059m and 0.4291m for the triangular localization and triangular localization with MLE methods, respectively.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Publication milestones

  • Published - 02/10/2017

Publication status

Published - 02/10/2017

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

External Publication IDs

  • handle.net: 10547/623856
  • Scopus: 85034583430

Host publication title

2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

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