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Article

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

1
Intelligent Systems Laboratory, Department of Systems Engineering and Automation, Universidad Carlos III de Madrid, 28911 Leganés, Madrid, Spain
2
Robesafe Research Group, Department of Electronics, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain
3
Centro Singular de Investigacion en Tecnoloxias da Informacion (CiTIUS), Universidade de Santiago de Compostela, Campus Vida, E-15782, Santiago de Compostela, Galicia, Spain
4
Computational Intelligence Lab, School of Electrical and Electronic Engineering, Yonsei University, 03722 Seoul, Korea
*
Author to whom correspondence should be addressed.
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.
Academic Editor: Antonio R. Jiménez
Sensors 2017, 17(1), 147; https://doi.org/10.3390/s17010147
Received: 5 December 2016 / Revised: 5 January 2017 / Accepted: 11 January 2017 / Published: 13 January 2017
(This article belongs to the Special Issue Smartphone-based Pedestrian Localization and Navigation)
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. View Full-Text
Keywords: WiFi indoor localization; fingerprinting; continuous space estimation; machine learning; location-based services WiFi indoor localization; fingerprinting; continuous space estimation; machine learning; location-based services
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MDPI and ACS Style

Hernández, N.; Ocaña, M.; Alonso, J.M.; Kim, E. Continuous Space Estimation: Increasing WiFi-Based Indoor Localization Resolution without Increasing the Site-Survey Effort. Sensors 2017, 17, 147. https://doi.org/10.3390/s17010147

AMA Style

Hernández N, Ocaña M, Alonso JM, Kim E. Continuous Space Estimation: Increasing WiFi-Based Indoor Localization Resolution without Increasing the Site-Survey Effort. Sensors. 2017; 17(1):147. https://doi.org/10.3390/s17010147

Chicago/Turabian Style

Hernández, Noelia, Manuel Ocaña, Jose M. Alonso, and Euntai Kim. 2017. "Continuous Space Estimation: Increasing WiFi-Based Indoor Localization Resolution without Increasing the Site-Survey Effort" Sensors 17, no. 1: 147. https://doi.org/10.3390/s17010147

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