Next Article in Journal
An Innovative Optical Sensor for the Online Monitoring and Control of Biomass Concentration in a Membrane Bioreactor System for Lactic Acid Production
Next Article in Special Issue
Penetration Depth Measurement of Near-Infrared Hyperspectral Imaging Light for Milk Powder
Previous Article in Journal
Physiological Signal Monitoring Bed for Infants Based on Load-Cell Sensors
Previous Article in Special Issue
Underwater Imaging Using a 1 × 16 CMUT Linear Array
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(3), 410; doi:10.3390/s16030410

A Crowd-Sourcing Indoor Localization Algorithm via Optical Camera on a Smartphone Assisted by Wi-Fi Fingerprint RSSI

College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China
*
Author to whom correspondence should be addressed.
Academic Editor: Gonzalo Pajares Martinsanz
Received: 24 January 2016 / Revised: 15 March 2016 / Accepted: 16 March 2016 / Published: 19 March 2016
(This article belongs to the Special Issue Imaging: Sensors and Technologies)
View Full-Text   |   Download PDF [5752 KB, uploaded 19 March 2016]   |  

Abstract

Indoor positioning based on existing Wi-Fi fingerprints is becoming more and more common. Unfortunately, the Wi-Fi fingerprint is susceptible to multiple path interferences, signal attenuation, and environmental changes, which leads to low accuracy. Meanwhile, with the recent advances in charge-coupled device (CCD) technologies and the processing speed of smartphones, indoor positioning using the optical camera on a smartphone has become an attractive research topic; however, the major challenge is its high computational complexity; as a result, real-time positioning cannot be achieved. In this paper we introduce a crowd-sourcing indoor localization algorithm via an optical camera and orientation sensor on a smartphone to address these issues. First, we use Wi-Fi fingerprint based on the K Weighted Nearest Neighbor (KWNN) algorithm to make a coarse estimation. Second, we adopt a mean-weighted exponent algorithm to fuse optical image features and orientation sensor data as well as KWNN in the smartphone to refine the result. Furthermore, a crowd-sourcing approach is utilized to update and supplement the positioning database. We perform several experiments comparing our approach with other positioning algorithms on a common smartphone to evaluate the performance of the proposed sensor-calibrated algorithm, and the results demonstrate that the proposed algorithm could significantly improve accuracy, stability, and applicability of positioning. View Full-Text
Keywords: fingerprint localization; optical camera; image processing; orientation sensor; crowd-sourcing; smartphone fingerprint localization; optical camera; image processing; orientation sensor; crowd-sourcing; smartphone
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

Chen, W.; Wang, W.; Li, Q.; Chang, Q.; Hou, H. A Crowd-Sourcing Indoor Localization Algorithm via Optical Camera on a Smartphone Assisted by Wi-Fi Fingerprint RSSI. Sensors 2016, 16, 410.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

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