GPS-Assisted Path Loss Exponent Estimation for Positioning in IEEE 802.11 Networks

We present a new adaptive method to calculate the path loss exponent (PLE) for microcell outdoor dynamic environments in the 2.4 GHz Industrial, Scientific, and Medical (ISM) frequency band. The proposed method calculates the PLE during random walks by recording signal strength measurements from Rad...

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Main Authors: Ernesto Navarro-Alvarez, Mario Siller, Kyle O'Keefe
Format: Article
Language:English
Published: SAGE Publishing 2013-05-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1155/2013/912029
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spelling doaj-ab087800fc52479e83f9e2a6e46bd1c92020-11-25T03:26:19ZengSAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772013-05-01910.1155/2013/912029GPS-Assisted Path Loss Exponent Estimation for Positioning in IEEE 802.11 NetworksErnesto Navarro-Alvarez0Mario Siller1Kyle O'Keefe2 Electrical Engineering and Computer Science Department, CINVESTAV-Unidad, Guadalajara. Av. Del Bosque 1145, Colonia El Bajio, Zapopan, JAL 45019, Mexico Electrical Engineering and Computer Science Department, CINVESTAV-Unidad, Guadalajara. Av. Del Bosque 1145, Colonia El Bajio, Zapopan, JAL 45019, Mexico Geomatics Engineering Department, University of Calgary, Schulich School of Engineering, 2500 University Dr. NW, Calgary, AB, Canada, T2N 1N4We present a new adaptive method to calculate the path loss exponent (PLE) for microcell outdoor dynamic environments in the 2.4 GHz Industrial, Scientific, and Medical (ISM) frequency band. The proposed method calculates the PLE during random walks by recording signal strength measurements from Radio Frequency (RF) transceivers and position data with a consumer-grade GPS receiver. The novelty of this work lies in the formulation of signal propagation conditions as a parametric observation model in order to estimate first the PLE and then the distance from the received RF signals using nonlinear least squares. GPS data is used to identify long term fading from the received signal's power and helps to refine the power-distance model. Ray tracing geometries for urban canyon (direct line of sight) and nonurban canyon (obstacles) propagation scenarios are used as the physics of the model (design matrix). Although the method was implemented for a lightweight localization algorithm for the 802.11b/g (Wi-Fi) standard, it can also be applied to other ISM band protocols such as 802.15.4 (Zigbee) and 802.15.1 (Bluetooth).https://doi.org/10.1155/2013/912029
collection DOAJ
language English
format Article
sources DOAJ
author Ernesto Navarro-Alvarez
Mario Siller
Kyle O'Keefe
spellingShingle Ernesto Navarro-Alvarez
Mario Siller
Kyle O'Keefe
GPS-Assisted Path Loss Exponent Estimation for Positioning in IEEE 802.11 Networks
International Journal of Distributed Sensor Networks
author_facet Ernesto Navarro-Alvarez
Mario Siller
Kyle O'Keefe
author_sort Ernesto Navarro-Alvarez
title GPS-Assisted Path Loss Exponent Estimation for Positioning in IEEE 802.11 Networks
title_short GPS-Assisted Path Loss Exponent Estimation for Positioning in IEEE 802.11 Networks
title_full GPS-Assisted Path Loss Exponent Estimation for Positioning in IEEE 802.11 Networks
title_fullStr GPS-Assisted Path Loss Exponent Estimation for Positioning in IEEE 802.11 Networks
title_full_unstemmed GPS-Assisted Path Loss Exponent Estimation for Positioning in IEEE 802.11 Networks
title_sort gps-assisted path loss exponent estimation for positioning in ieee 802.11 networks
publisher SAGE Publishing
series International Journal of Distributed Sensor Networks
issn 1550-1477
publishDate 2013-05-01
description We present a new adaptive method to calculate the path loss exponent (PLE) for microcell outdoor dynamic environments in the 2.4 GHz Industrial, Scientific, and Medical (ISM) frequency band. The proposed method calculates the PLE during random walks by recording signal strength measurements from Radio Frequency (RF) transceivers and position data with a consumer-grade GPS receiver. The novelty of this work lies in the formulation of signal propagation conditions as a parametric observation model in order to estimate first the PLE and then the distance from the received RF signals using nonlinear least squares. GPS data is used to identify long term fading from the received signal's power and helps to refine the power-distance model. Ray tracing geometries for urban canyon (direct line of sight) and nonurban canyon (obstacles) propagation scenarios are used as the physics of the model (design matrix). Although the method was implemented for a lightweight localization algorithm for the 802.11b/g (Wi-Fi) standard, it can also be applied to other ISM band protocols such as 802.15.4 (Zigbee) and 802.15.1 (Bluetooth).
url https://doi.org/10.1155/2013/912029
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