RSSI indoor localization through a Bayesian strategy

Fu Zhou; Kaixian Lin; Aifeng Ren; Dongjian Cao; Zhiya Zhang; Masood Ur-Rehman; Xiaodong Yang; Akram Alomainy

doi:10.1109/IAEAC.2017.8054360

RSSI indoor localization through a Bayesian strategy

Fu Zhou
, Kaixian Lin
, Aifeng Ren
, Dongjian Cao
, Zhiya Zhang
, Masood Ur-Rehman
, Xiaodong Yang
, Akram Alomainy

Institute for Research in Applicable Computing (IRAC)

Xidian University
Queen Mary University of London

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

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.

Original language	English
Title of host publication	nan
Publisher	Institute of Electrical and Electronics Engineers Inc.
DOIs	https://doi.org/10.1109/IAEAC.2017.8054360
Publication status	Published - 2 Oct 2017
Event	IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) - Chongqing Duration: 25 Mar 2017 → 26 Mar 2017

Conference

Conference	IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)
City	Chongqing
Period	25/03/17 → 26/03/17
Other	IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (25/03/2017-26/03/2017, Chongqing)

Keywords

Bayes methods
Bayesian theorem
Gaussian distribution
RSSI
Zigbee
maximum likelihood estimation
radio propagation

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10.1109/IAEAC.2017.8054360

https://ieeexplore.ieee.org/document/8054360

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@inproceedings{36ac94ce923844b48b2d64a2d541ad22,

title = "RSSI indoor localization through a Bayesian strategy",

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.",

keywords = "Bayes methods, Bayesian theorem, Gaussian distribution, RSSI, Zigbee, maximum likelihood estimation, radio propagation",

author = "Fu Zhou and Kaixian Lin and Aifeng Ren and Dongjian Cao and Zhiya Zhang and Masood Ur-Rehman and Xiaodong Yang and Akram Alomainy",

year = "2017",

month = oct,

day = "2",

doi = "10.1109/IAEAC.2017.8054360",

language = "English",

booktitle = "nan",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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note = "IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) ; Conference date: 25-03-2017 Through 26-03-2017",

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TY - GEN

T1 - RSSI indoor localization through a Bayesian strategy

AU - Zhou, Fu

AU - Lin, Kaixian

AU - Ren, Aifeng

AU - Cao, Dongjian

AU - Zhang, Zhiya

AU - Ur-Rehman, Masood

AU - Yang, Xiaodong

AU - Alomainy, Akram

PY - 2017/10/2

Y1 - 2017/10/2

N2 - 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.

AB - 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.

KW - Bayes methods

KW - Bayesian theorem

KW - Gaussian distribution

KW - RSSI

KW - Zigbee

KW - maximum likelihood estimation

KW - radio propagation

U2 - 10.1109/IAEAC.2017.8054360

DO - 10.1109/IAEAC.2017.8054360

M3 - Conference contribution

BT - nan

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

Y2 - 25 March 2017 through 26 March 2017

ER -

RSSI indoor localization through a Bayesian strategy

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