Next Article in Journal
Robust Indoor Human Activity Recognition Using Wireless Signals
Next Article in Special Issue
Marginalised Stacked Denoising Autoencoders for Robust Representation of Real-Time Multi-View Action Recognition
Previous Article in Journal
Infrared and Visual Image Fusion through Fuzzy Measure and Alternating Operators
Previous Article in Special Issue
Real-Time Personalized Monitoring to Estimate Occupational Heat Stress in Ambient Assisted Working
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(7), 17168-17194; doi:10.3390/s150717168

MagicFinger: 3D Magnetic Fingerprints for Indoor Location

Department of Information and Communications Engineering, University of Murcia, 30100 Murcia, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Jesús Fontecha and Paul Mccullagh
Received: 30 April 2015 / Revised: 8 July 2015 / Accepted: 8 July 2015 / Published: 15 July 2015
View Full-Text   |   Download PDF [1096 KB, uploaded 15 July 2015]   |  

Abstract

Given the indispensable role of mobile phones in everyday life, phone-centric sensing systems are ideal candidates for ubiquitous observation purposes. This paper presents a novel approach for mobile phone-centric observation applied to indoor location. The approach involves a location fingerprinting methodology that takes advantage of the presence of magnetic field anomalies inside buildings. Unlike existing work on the subject, which uses the intensity of magnetic field for fingerprinting, our approach uses all three components of the measured magnetic field vectors to improve accuracy. By using adequate soft computing techniques, it is possible to adequately balance the constraints of common solutions. The resulting system does not rely on any infrastructure devices and therefore is easy to manage and deploy. The proposed system consists of two phases: the offline phase and the online phase. In the offline phase, magnetic field measurements are taken throughout the building, and 3D maps are generated. Then, during the online phase, the user’s location is estimated through the best estimator for each zone of the building. Experimental evaluations carried out in two different buildings confirm the satisfactory performance of indoor location based on magnetic field vectors. These evaluations provided an error of (11.34 m, 4.78 m) in the (x; y) components of the estimated positions in the first building where the experiments were carried out, with a standard deviation of (3.41 m, 4.68 m); and in the second building, an error of (4 m, 2.98 m) with a deviation of (2.64 m, 2.33 m). View Full-Text
Keywords: indoor location; smartphone; magnetic field; fingerprinting indoor location; smartphone; magnetic field; fingerprinting
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Carrillo, D.; Moreno, V.; Úbeda, B.; Skarmeta, A.F. MagicFinger: 3D Magnetic Fingerprints for Indoor Location. Sensors 2015, 15, 17168-17194.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top