Advertisement

Support Vector Machine Classification in a Device-Free Passive Localisation (DfPL) Scenario

  • Gabriel Deak
  • Kevin Curran
  • Joan Condell
  • Daniel Deak
  • Piotr Kiedrowski
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 184)

Summary

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) in order to detect movement based on changes in the wireless signal strength.

Keywords

Suport Vector Machine Wireless Sensor Network Kernel Function Receive Signal Strength Indicator Gaussian Radial Basis Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Frazier, L.M.: Surveillance through walls and other opaque materials. IEEE Aerospace and Electronic Systems Magazine 11(10), 6–9 (1996)CrossRefGoogle Scholar
  2. 2.
    Ma, L., Zhang, Z., Tan, X.: A novel through-wall imaging method using ultra wideband pulse system. In: International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2006, pp. 147–150 (December 2006)Google Scholar
  3. 3.
    Wilson, J., Patwari, N.: See-through walls: Motion tracking using variance-based radio tomography networks. IEEE Transactions on Mobile Computing 10(5), 612–621 (2011)CrossRefGoogle Scholar
  4. 4.
    Aryanfar, F., Sarabandi, K.: Through wall imaging at microwave frequencies using space-time focusing. In: IEEE Antennas and Propagation Society International Symposium, vol. 3, pp. 3063–3066 (June 2004)Google Scholar
  5. 5.
    Gazit, E.: Improved design of the vivaldi antenna. IEEE Proceedings H, Microwaves, Antennas and Propagation 135(2), 89–92 (1988)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Valtonen, M., Maentausta, J., Vanhala, J.: Tiletrack: Capacitive human tracking using floor tiles. In: IEEE International Conference on Pervasive Computing and Communications, PerCom 2009, pp. 1–10 (March 2009)Google Scholar
  7. 7.
    Krumm, J.: Ubiquitous Computing Fundamentals. CRC Press (2010)Google Scholar
  8. 8.
    Krumm, J., Harris, S., Meyers, B., Brumitt, B., Hale, M., Shafer, S.: Multi-camera multi-person tracking for easy living. In: Proceedings of Third IEEE International Workshop on Visual Surveillance, pp. 3–10 (2000)Google Scholar
  9. 9.
    Microsoft Research. Easy Living (2011), http://www.research.microsoft.com/
  10. 10.
    Deak, G., Curran, K., Condell, J.: Filters for RSSI-based measurements in a Device-free Passive Localisation Scenario. International Journal on Image Processing & Communications 15(1), 23–34 (2011)Google Scholar
  11. 11.
    Deak, G., Curran, K., Condell, J.: History Aware Device-free Passive (DfP) Localisation. International Journal on Image Processing & Communications 16(3-4), 21–30 (2012)Google Scholar
  12. 12.
    Moussa, M., Youssef, M.: Smart cevices for smart environments: Device-free passive detection in real environments. In: IEEE International Conference on Pervasive Computing and Communications, PerCom 2009, pp. 1–6 (2009)Google Scholar
  13. 13.
    Youssef, M., Mah, M., Agrawala, A.: Challenges: device-free passive localization for wireless environments. In: Proceedings of the 13th Annual ACM International Conference on Mobile Computing and Networking, pp. 222–229 (2007)Google Scholar
  14. 14.
    Kosba, A., Abdelkader, E., Youssef, A., Analysis, M.: of a device-free passive tracking system in typical wireless environments. In: 2009 3rd International Conference on New Technologies, Mobility and Security, NTMS, pp. 1–5 (December 2009)Google Scholar
  15. 15.
    Song, L.-P., Yu, C., Liu, Q.H.: Through-wall imaging (twi) by radar: 2-d tomographic results and analyses. IEEE Transactions on Geoscience and Remote Sensing 43(12), 2793–2798 (2005)CrossRefGoogle Scholar
  16. 16.
    Mathworks. R2012a Documentation, Bioinformatics Toolbox (2012), http://www.mathworks.co.uk/help/toolbox/bioinfo/

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gabriel Deak
    • 1
  • Kevin Curran
    • 1
  • Joan Condell
    • 1
  • Daniel Deak
    • 2
  • Piotr Kiedrowski
    • 3
  1. 1.Intelligent System Research CentreDerryUK
  2. 2.S.C. Centrul de Calcul Info98 S.A.PetrosaniRomania
  3. 3.Institute of TelecommunicationUniversity of Technology and Life ScienceBydgoszczPoland

Personalised recommendations