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
Open AccessArticle

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

Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
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.
ISPRS Int. J. Geo-Inf. 2013, 2(3), 854-868;
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)
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. View Full-Text
Keywords: indoor positioning; neural network; Wi-Fi fingerprinting indoor positioning; neural network; Wi-Fi fingerprinting
Show Figures

Figure 1

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.

Show more citation formats Show less citations formats

Article Access Map

Only visits after 24 November 2015 are recorded.
Back to TopTop