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

• Published in: Sensors (Basel, Switzerland) Vol. 17; no. 1; p. 147
• Main Authors: Hernández, Noelia, Ocaña, Manuel, Alonso, Jose M, Kim, Euntai
• Format: Journal Article
• Language: English
• 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
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s17010147

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.