title: Continuous Space Estimation: Increasing WiFi-Based Indoor Localization Resolution without Increasing the Site-Survey Effort.
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.
authors.affiliation: Intelligent Systems Laboratory, Department of Systems Engineering and Automation, Universidad Carlos III de Madrid, 28911 Leganés, Madrid, Spain. noelia.hernandez@uc3m.es.
authors.forename: Noelia
authors.initials: N
authors.lastname: Hernández
authors.affiliation: Robesafe Research Group, Department of Electronics, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain. mocana@depeca.uah.es.
authors.forename: Manuel
authors.initials: M
authors.lastname: Ocaña
authors.affiliation: 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.
authors.forename: Jose M
authors.initials: JM
authors.lastname: Alonso
authors.affiliation: Computational Intelligence Lab, School of Electrical and Electronic Engineering, Yonsei University, 03722 Seoul, Korea. etkim@yonsei.ac.kr.
authors.forename: Euntai
authors.initials: E
authors.lastname: Kim
keywords: WiFi indoor localization; continuous space estimation; fingerprinting; location-based services; machine learning