Continuous Space Estimation: Increasing WiFi-Based Indoor Localization Resolution without Increasing the Site-Survey Effort

Although much research has taken place in WiFi indoor localization systems, their accuracy can still be improved. When designing this kind of system, fingerprint-based methods are a common choice. The problem with fingerprint-based methods comes with the need of site surveying the environment, which...

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Published inSensors (Basel, Switzerland) Vol. 17; no. 1; p. 147
Main Authors Hernández, Noelia, Ocaña, Manuel, Alonso, Jose M, Kim, Euntai
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 13.01.2017
MDPI
Subjects
Accuracy
Construction
Consumption
continuous space estimation
Estimates
fingerprinting
Global positioning systems
GPS
Indoor environments
International conferences
Localization
Location based services
machine learning
Position (location)
Sensors
Signal strength
Smartphones
Support vector machines
Surveying
Ultrasonic imaging
WiFi indoor localization
Madrid Spain
Spain
location-based services
machine learning
fingerprinting
continuous space estimation
WiFi indoor localization
Online AccessGet full text

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Abstract Although much research has taken place in WiFi indoor localization systems, their accuracy can still be improved. When designing this kind of system, fingerprint-based methods are a common choice. The problem with fingerprint-based methods comes with the need of site surveying the environment, which is effort consuming. In this work, we propose an approach, based on support vector regression, to estimate the received signal strength at non-site-surveyed positions of the environment. Experiments, performed in a real environment, show that the proposed method could be used to improve the resolution of fingerprint-based indoor WiFi localization systems without increasing the site survey effort.
AbstractList Although much research has taken place in WiFi indoor localization systems, their accuracy can still be improved. When designing this kind of system, fingerprint-based methods are a common choice. The problem with fingerprint-based methods comes with the need of site surveying the environment, which is effort consuming. In this work, we propose an approach, based on support vector regression, to estimate the received signal strength at non-site-surveyed positions of the environment. Experiments, performed in a real environment, show that the proposed method could be used to improve the resolution of fingerprint-based indoor WiFi localization systems without increasing the site survey effort.
Author Kim, Euntai
Hernández, Noelia
Ocaña, Manuel
Alonso, Jose M
AuthorAffiliation 2 Robesafe Research Group, Department of Electronics, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain; mocana@depeca.uah.es
3 Centro Singular de Investigacion en Tecnoloxias da Informacion (CiTIUS), Universidade de Santiago de Compostela, Campus Vida, E-15782, Santiago de Compostela, Galicia, Spain; josemaria.alonso.moral@usc.es
1 Intelligent Systems Laboratory, Department of Systems Engineering and Automation, Universidad Carlos III de Madrid, 28911 Leganés, Madrid, Spain
4 Computational Intelligence Lab, School of Electrical and Electronic Engineering, Yonsei University, 03722 Seoul, Korea; etkim@yonsei.ac.kr
AuthorAffiliation_xml – name: 3 Centro Singular de Investigacion en Tecnoloxias da Informacion (CiTIUS), Universidade de Santiago de Compostela, Campus Vida, E-15782, Santiago de Compostela, Galicia, Spain; josemaria.alonso.moral@usc.es
– name: 4 Computational Intelligence Lab, School of Electrical and Electronic Engineering, Yonsei University, 03722 Seoul, Korea; etkim@yonsei.ac.kr
– name: 1 Intelligent Systems Laboratory, Department of Systems Engineering and Automation, Universidad Carlos III de Madrid, 28911 Leganés, Madrid, Spain
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  surname: Hernández
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  fullname: Ocaña, Manuel
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  givenname: Jose M
  surname: Alonso
  fullname: Alonso, Jose M
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  organization: Computational Intelligence Lab, School of Electrical and Electronic Engineering, Yonsei University, 03722 Seoul, Korea. etkim@yonsei.ac.kr
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Keywords location-based services
machine learning
fingerprinting
continuous space estimation
WiFi indoor localization
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This paper is an extended version of Hernández, N.; Ocaña, M.; Alonso, J.M.; Kim, E. WiFi-based indoor localization using a continuous space estimator from topological information. In Proceedings of the International Conference on Indoor Positioning and Indoor Navigation, Banff, AB, Canada, 13–16 October 2015; pp. 1–4.
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StartPage 147
SubjectTerms Accuracy
Construction
Consumption
continuous space estimation
Estimates
fingerprinting
Global positioning systems
GPS
Indoor environments
International conferences
Localization
Location based services
machine learning
Position (location)
Sensors
Signal strength
Smartphones
Support vector machines
Surveying
Ultrasonic imaging
WiFi indoor localization
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Title Continuous Space Estimation: Increasing WiFi-Based Indoor Localization Resolution without Increasing the Site-Survey Effort
URI https://www.ncbi.nlm.nih.gov/pubmed/28098773
https://www.proquest.com/docview/1862134242/abstract/
https://search.proquest.com/docview/1861568253
https://search.proquest.com/docview/1880035708
https://pubmed.ncbi.nlm.nih.gov/PMC5298720
https://doaj.org/article/55c50da06c114ef0bbcc1dc5bd67382a
Volume 17
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