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A Preliminary Study on SVM Based Analysis of Underwater Magnetic Signals for Port Protection

  • Davide Leoncini
  • Sergio Decherchi
  • Osvaldo Faggioni
  • Paolo Gastaldo
  • Maurizio Soldani
  • Rodolfo Zunino
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 63)

Abstract

People who attend to the problem of underwater port protection usually use sonar based systems. Recently it has been shown that integrating a sonar system with an auxiliary array of magnetic sensors can improve the effectiveness of the intruder detection system. One of the major issues that arise from the integrated magnetic and acoustic system is the interpretation of the magnetic signals coming from the sensors. In this paper a machine learning approach is explored for the detection of divers or, in general, of underwater magnetic sources that should ultimately support an automatic detection system which currently requires a human online monitoring or an offline signal processing. The research proposed here, by means of a windowing of the signals, uses Support Vector Machines for classification, as tool for the detection problem. Empirical results show the effectiveness of the method.

Keywords

underwater detection systems port protection magnetic signal processing Support Vector Machine 

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References

  1. 1.
    Faggioni, O., Gabellone, A., Hollett, R., Kessel, R.T., Soldani, M.: Anti-intruder port protection “MAC (Magnetic ACoustic) System”: advances in the magnetic component performance. In: Proceedings of 1st WSS Conference, Copenhagen, Denmark, August 25-28 (2008) CD-ROMGoogle Scholar
  2. 2.
    Kanasewich, E.R.: Time Sequence Analysis in Geophysics. The University of Alberta Press, Edmonton (1981)Google Scholar
  3. 3.
    Faggioni, O., Soldani, M., Gabellone, A., Maggiani, P., Leoncini, D.: Development of anti intruders underwater systems: time domain evaluation of the self-informed magnetic networks performance. In: Corchado, E., Zunino, R., Gastaldo, P., Herrero, Á. (eds.) Proceedings of the International Workshop on Computational Intelligence in Security for Information Systems CISIS 2008. Advances in Soft Computin, vol. 53, pp. 100–107. Springer, Heidelberg (2009)Google Scholar
  4. 4.
    Gabellone, A., Faggioni, O., Soldani, M., Guerrini, P.: CAIMAN Experiment. In: Proceedings of UDT Europe 2007 Conference, Naples, Italy, June 5-7 (2007) CD-ROMGoogle Scholar
  5. 5.
    Gabellone, A., Faggioni, O., Soldani, M., Guerrini, P.: CAIMAN (Coastal Anti Intruder MAgnetometers Network). In: Proceedings of RTO-MP-SET-130 Symposium on NATO Military Sensing, Orlando, Florida, USA, March 12-14, NATO classified (2008) CD-ROMGoogle Scholar
  6. 6.
    Faggioni, O., Soldani, M., Zunino, R., Leoncini, D., Di Gennaro, E., Gabellone, A., Maggiani, P.V., Falcucci, V., Michelizza, E.: Building the Synthetic “MAC System”: an Analytical Integration of Magnetic and Acoustic Subsystems for Port Protection Scenarios. In: Proceedings of UDT Europe 2009 Conference, Cannes, France, June 9-11 (2009) CD-ROMGoogle Scholar
  7. 7.
    Hettich, S., Bay, S.D.: The UCI KDD Archive. University of California, Department of Information and Computer Science, Irvine (1999), http://kdd.ics.uci.edu Google Scholar
  8. 8.
    Vapnik, V.: Statistical Learning Theory. Wiley-Interscience Pub., Hoboken (1998)zbMATHGoogle Scholar
  9. 9.
    Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (2002)Google Scholar
  10. 10.
    Chang, C.C., Lin, C.J.: LibSVM: a library for Support Vector Machines, http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Davide Leoncini
    • 1
  • Sergio Decherchi
    • 1
  • Osvaldo Faggioni
    • 2
  • Paolo Gastaldo
    • 1
  • Maurizio Soldani
    • 2
  • Rodolfo Zunino
    • 1
  1. 1.Dept. Biophysical and Electronic EngineeringUniversity of GenoaGenoaItaly
  2. 2.Istituto Nazionale di Geofisica e VulcanologiaPortovenere (SP)Italy

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