Abstract
This paper presents a new neural network based approach to the prediction of mobile locations using signal strength measurements in a simulated metropolitan area. The prediction of a mobile location using propagation path loss (signal strength) is a very difficult and complex task. Several techniques have been proposed recently mostly based on linearized, geometrical and maximum likelihood methods. An alternative approach based on artificial neural networks is proposed in this paper which offers the advantages of increased flexibility to adapt to different environments and high speed parallel processing. The paper first gives an overview of conventional location estimation techniques and the various propagation models reported to-date, and a new signal-strength based neural network technique is then described. A simulated mobile architecture based on the COST-231 Non-line of Sight (NLOS) Walfisch-Ikegami implementation of a metropolitan environment is used to assess the generalization performance of a Multi-Layered Perceptron (MLP) Neural Network based mobile location predictor with promising initial results.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Muhammad, J., Hussain, A., Neskovic, A., Magill, E. (2005). New Neural Network Based Mobile Location Estimation in a Metropolitan Area. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_148
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DOI: https://doi.org/10.1007/11550907_148
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