Cascade-connected ANN structures for indoor WLAN positioning

Authors:
Miloš Borenović;Aleksandar Nešković;Djuradj Budimir
Affiliations:
School of Electrical Engineering, Belgrade, Serbia and Wireless Communications Research Group, School of Informatics, University of Westminster, London, United Kingdom;School of Electrical Engineering, Belgrade, Serbia;Wireless Communications Research Group, School of Informatics, University of Westminster, London, United Kingdom
Venue:
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Year:
2009

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Abstract

Various radio systems can be used to obtain the position information in indoor environments. Due to the ubiquitous presence of WLAN networks, positioning techniques in these environments are the scope of intense research. This paper explores the properties of cascade-connected Artificial Neural Networks (ANNs) structures. Several cascade-connected ANN structures with space partitioning are compared to the single ANN multilayer feedforward structure. The benefits of using cascade-connected ANNs structures are shown and discussed in terms of the size of the environment and subspaces. The optimal cascade-connected ANN structure with space partitioning shows a 41% decrease in median error with respect to the single ANN model.