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

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, streng...

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Bibliographic Details
Published in:ISPRS international journal of geo-information 2013-09, Vol.2 (3), p.854-868
Main Authors: Mok, Esmond, Cheung, Bernard
Format: Article
Language:English
Subjects:
Algorithms
Data processing
indoor positioning
neural network
Wi-Fi fingerprinting
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Summary: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.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi2030854