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SDR-based passive indoor localization system for GSM

Authors:

Islam Alyafawi,

Desislava C. Dimitrova,

Torsten BraunAuthors Info & Claims

SRIF '14: Proceedings of the 2014 ACM workshop on Software radio implementation forum

Pages 7 - 14

https://doi.org/10.1145/2627788.2627790

Published: 18 August 2014 Publication History

Abstract

This study deals with indoor positioning using GSM radio, which has the distinct advantage of wide coverage over other wireless technologies. In particular, we focus on passive localization systems that are able to achieve high localization accuracy without any prior knowledge of the indoor environment or the tracking device radio settings. In order to overcome these challenges, newly proposed localization algorithms based on the exploitation of the received signal strength (RSS) are proposed. We explore the effects of non-line-of-sight communication links, opening and closing of doors, and human mobility on RSS measurements and localization accuracy. We have implemented the proposed algorithms on top of software defined radio systems and carried out detailed empirical indoor experiments. The performance results show that the proposed solutions are accurate with average localization errors between 2.4 and 3.2 meters.

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Cited By

Chang YWang QYin ZJu CBi ZWang J(2023)Improved Kalman Filter-Based Weighted Center-of-Mass Localization Algorithm2023 15th International Conference on Communication Software and Networks (ICCSN)10.1109/ICCSN57992.2023.10297354(438-443)Online publication date: 21-Jul-2023
https://doi.org/10.1109/ICCSN57992.2023.10297354
Maskeliūnas RDamaševičius RSegal S(2019)A Review of Internet of Things Technologies for Ambient Assisted Living EnvironmentsFuture Internet10.3390/fi1112025911:12(259)Online publication date: 12-Dec-2019
https://doi.org/10.3390/fi11120259
Bojic IYoshimura YRatti C(2019)Opportunities and Challenges of Trip Generation Data Collection Techniques Using Cellular NetworksIEEE Communications Magazine10.1109/MCOM.2017.1500181CM55:3(204-209)Online publication date: 3-Jan-2019
https://dl.acm.org/doi/10.1109/MCOM.2017.1500181CM
Show More Cited By

Index Terms

SDR-based passive indoor localization system for GSM
1. Networks
  1. Network types
    1. Mobile networks
    2. Wireless access networks

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Published In

cover image ACM Conferences

SRIF '14: Proceedings of the 2014 ACM workshop on Software radio implementation forum

August 2014

100 pages

ISBN:9781450329958

DOI:10.1145/2627788

General Chair:
Li Erran Li
Bell Labs, Alcatel-Lucent, USA
,
Program Chairs:
Shyam Gollakota
University of Washington, USA
,
Patrick Murphy
Mango Communications, USA
,
Marina Petrova
RWTH Aachen University, Germany

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCOMM: ACM Special Interest Group on Data Communication

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 August 2014

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SIGCOMM'14

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SIGCOMM

SIGCOMM'14: ACM SIGCOMM 2014 Conference

August 18, 2014

Illinois, Chicago, USA

Acceptance Rates

SRIF '14 Paper Acceptance Rate 10 of 18 submissions, 56%;

Overall Acceptance Rate 23 of 41 submissions, 56%

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Cited By

Chang YWang QYin ZJu CBi ZWang J(2023)Improved Kalman Filter-Based Weighted Center-of-Mass Localization Algorithm2023 15th International Conference on Communication Software and Networks (ICCSN)10.1109/ICCSN57992.2023.10297354(438-443)Online publication date: 21-Jul-2023
https://doi.org/10.1109/ICCSN57992.2023.10297354
Maskeliūnas RDamaševičius RSegal S(2019)A Review of Internet of Things Technologies for Ambient Assisted Living EnvironmentsFuture Internet10.3390/fi1112025911:12(259)Online publication date: 12-Dec-2019
https://doi.org/10.3390/fi11120259
Bojic IYoshimura YRatti C(2019)Opportunities and Challenges of Trip Generation Data Collection Techniques Using Cellular NetworksIEEE Communications Magazine10.1109/MCOM.2017.1500181CM55:3(204-209)Online publication date: 3-Jan-2019
https://dl.acm.org/doi/10.1109/MCOM.2017.1500181CM
Cheng YChang RChen L(2017)A Comparative Study of Machine-Learning Indoor Localization Using FM and DVB-T Signals in Real Testbed Environments2017 IEEE 85th Vehicular Technology Conference (VTC Spring)10.1109/VTCSpring.2017.8108573(1-7)Online publication date: Jun-2017
https://doi.org/10.1109/VTCSpring.2017.8108573
Ramasubramanian GDietrich C(2017)Performance characterization of USRPsAnalog Integrated Circuits and Signal Processing10.1007/s10470-017-1099-8Online publication date: 21-Dec-2017
https://doi.org/10.1007/s10470-017-1099-8
Alyafawi IKiener SBraun T(2016)Hybrid indoor localization using multiple Radio Interfaces2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)10.1109/WoWMoM.2016.7523525(1-7)Online publication date: Jun-2016
https://doi.org/10.1109/WoWMoM.2016.7523525
Alyafawi IBraun TDimitrova D(2015)Robust indoor localization of narrowband signals2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)10.1109/PIMRC.2015.7343661(2193-2198)Online publication date: Aug-2015
https://doi.org/10.1109/PIMRC.2015.7343661

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