Next Article in Journal
A Suite of Tools for ROC Analysis of Spatial Models
Next Article in Special Issue
A Self-Contained and Self-Checking LPS with High Accuracy
Previous Article in Journal
Spatio-Temporal Data Construction
Previous Article in Special Issue
HCTNav: A Path Planning Algorithm for Low-Cost Autonomous Robot Navigation in Indoor Environments
ISPRS Int. J. Geo-Inf. 2013, 2(3), 854-868; doi:10.3390/ijgi2030854

An Improved Neural Network Training Algorithm for Wi-Fi Fingerprinting Positioning

1,*  and 1,2
1 Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China 2 CIRRELT & Department of Mathematics and Industrial Engineering, Ecole Polytechnique de Montreal, P.O. Box 6079, Station Centre-Ville, Montréal, PQ H3C 3A7, Canada
* Author to whom correspondence should be addressed.
Received: 2 July 2013 / Revised: 14 August 2013 / Accepted: 14 August 2013 / Published: 3 September 2013
(This article belongs to the Special Issue Indoor Positioning and Indoor Navigation)
View Full-Text   |   Download PDF [672 KB, uploaded 3 September 2013]   |  


Ubiquitous positioning provides continuous positional information in both indoor and outdoor environments for a wide spectrum of location based service (LBS) applications. With the rapid development of the low-cost and high speed data communication, Wi-Fi networks in many metropolitan cities, strength of signals propagated from the Wi-Fi access points (APs) namely received signal strength (RSS) have been cleverly adopted for indoor positioning. In this paper, a Wi-Fi positioning algorithm based on neural network modeling of Wi-Fi signal patterns is proposed. This algorithm is based on the correlation between the initial parameter setting for neural network training and output of the mean square error to obtain better modeling of the nonlinear highly complex Wi-Fi signal power propagation surface. The test results show that this neural network based data processing algorithm can significantly improve the neural network training surface to achieve the highest possible accuracy of the Wi-Fi fingerprinting positioning method.
Keywords: indoor positioning; neural network; Wi-Fi fingerprinting indoor positioning; neural network; Wi-Fi fingerprinting
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote |
MDPI and ACS Style

Mok, E.; Cheung, B.K. An Improved Neural Network Training Algorithm for Wi-Fi Fingerprinting Positioning. ISPRS Int. J. Geo-Inf. 2013, 2, 854-868.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here


[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert