Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/6663
Title: An improved neural network training algorithm for Wi-Fi fingerprinting positioning
Authors: Mok, E 
Cheung, BK
Keywords: Indoor positioning
Neural networks
Wi-Fi fingerprinting
Issue Date: Sep-2013
Publisher: MDPI AG
Source: ISPRS International journal of geo-information, Sept. 2013, v. 2, no. 3, p. 854-868 How to cite?
Journal: ISPRS International journal of geo-information 
Abstract: 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.
URI: http://hdl.handle.net/10397/6663
ISSN: 2220-9964 (eISSN)
DOI: 10.3390/ijgi2030854
Rights: © 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
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