JOURNAL OF COMMUNICATIONS (JCM)
ISSN : 1796-2021
Volume : 4    Issue : 4    Date : May 2009

Neural Networks for Fingerprinting-Based Indoor Localization Using Ultra-Wideband
Anthony Taok, Nahi Kandil, and Sofiene Affes
Page(s): 267-275
Full Text:
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Abstract
This paper discusses the use of neural networks in an underground radio-localization system. In a
highly aggressive environment such as mines, reliability and robustness are essential to any
operational system. Using UWB as the physical wireless propagation medium and combined with
fingerprinting-geolocation and neural networks, this work tends to overcome many of the problems
encountered in indoor environments. Full description of the system and the adopted approach will
help accentuate the role of neural networks in improving the overall performance. Moreover a
comparison between MLP and RBF performance is presented, providing a clear evidence of the role
and importance of the neural networks in offering good accuracy and precision to the final system.

Index Terms
Indoor localization, LOS, NLOS, fingerprinting-localization, ultra-wide-band (UWB), impulse
response (IR), neural networks (NN), multi-layer perceptron (MLP), radial basis functions (RBF).