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Support Vector Machine and Probability Neural Networks in a Device-Free Passive Localization (DFPL) Scenario

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EN
The holy grail of tracking people indoors is being able to locate them when they are not carrying any wireless tracking devices. The aim is to be able to track people just through their physical body interfering with a standard wireless network that would be in most peoples home. The human body contains about 70% water which attenuates the wireless signal reacting as an absorber. The changes in the signal along with prior fingerprinting of a physical location allow identification of a person’s location. This paper is focused on taking the principle of Device-free Passive Localisation (DfPL) and applying it to be able to actually distinguish if there is more than one person in the environment. In order to solve this problem, we tested a Support Vector Machine (SVM) classifier with kernel functions such as Linear, Quadratic, Polynomial, Gaussian Radial Basis Function (RBF) and Multilayer Perceptron (MLP), and a Probabilistic Neural Network (PNN) in order to detect movement based on changes in the wireless signal strength.
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autor
  • School of Computing and Intelligent Systems, Faculty of Computing and Engineering, University of Ulster, Derry, N. Ireland, BT48 7JL, UK
  • Centrul de Calcul Info98 S.A., 2 Timisoara Street, 332015, Petrosani, Romania
autor
  • School of Computing and Intelligent Systems, Faculty of Computing and Engineering, University of Ulster, Derry, N. Ireland, BT48 7JL, UK
autor
  • School of Computing and Intelligent Systems, Faculty of Computing and Engineering, University of Ulster, Derry, N. Ireland, BT48 7JL, UK
autor
  • School of Computing and Intelligent Systems, Faculty of Computing and Engineering, University of Ulster, Derry, N. Ireland, BT48 7JL, UK
  • Institute of Telecommunication, University of Technology and Life Science, ul. Kaliskiego 7, 85-789
  • Institute of Telecommunication, University of Technology and Life Science, ul. Kaliskiego 7, 85-789 Bydgoszcz, Poland
Bibliografia
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  • [3] J. Wilson, N. Patwari, See-through walls: Motion tracking using variance-based radio tomography networks, Mobile Computing, IEEE Transactions on, Vol. 10, No. 5, pp. 612 - 621, may 2011
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  • [9] Microsoft Research, Easy Living, http://www.research.microsoft.com/ , 2011
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  • [14] A. Kosba, A. Abdelkader, M. Youssef, Analysis of a device-free passive tracking system in typical wireless environments, in New Technologies, Mobility and Security (NTMS), 2009 3rd International Conference on, pp. 1 - 5, dec. 2009
  • [15] L.-P. Song, C. Yu, Q. H. Liu, Through-wall imaging (twi) by radar: 2-d tomographic results and analyses, Geoscience and Remote Sensing, IEEE Transactions on, Vol. 43, No. 12, pp. 2793 - 2798, dec. 2005
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  • [17] S. Sumathi, P. Surekha, Computational Intelligence Paradigms Computational Intelligence Paradigms Theory and Applications, 2010
Typ dokumentu
Bibliografia
Identyfikator YADDA
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