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Computational Intelligence Methods for Underwater Magnetic-based Protection Systems
Author(s)
Type
Conference paper
Language
English
Obiettivo Specifico
1.6. Osservazioni di geomagnetismo
2.5. Laboratorio per lo sviluppo di sistemi di rilevamento sottomarini
3.4. Geomagnetismo
Status
Published
Issued date
July 31, 2011
Conference Location
San Jose, California, USA
Subjects
Abstract
Magnetic-based detection technologies for
undersea protection systems are very effective in monitoring critical areas where weak signal sources are difficult to identify
(e.g. diver intrusion in proximity of the seafloor). The complexity of the involved geomagnetic phenomena and the nature of the target detection strategy require the use of
adaptive methods for signal processing. The paper shows that Computational Intelligence (CI) models can be integrated with those magnetic-based technologies, and presents an effective, reliable system for adaptive undersea protection. Two different
CI paradigms are successfully tested for the specific application task: Circular BackPropagation (CBP) and Support Vector
Machines (SVMs). Experimental results on real data prove the advantage of the integrated approach over existing conventional methods. Individual CI components and the overall detection
system have been verified in real experiments.
undersea protection systems are very effective in monitoring critical areas where weak signal sources are difficult to identify
(e.g. diver intrusion in proximity of the seafloor). The complexity of the involved geomagnetic phenomena and the nature of the target detection strategy require the use of
adaptive methods for signal processing. The paper shows that Computational Intelligence (CI) models can be integrated with those magnetic-based technologies, and presents an effective, reliable system for adaptive undersea protection. Two different
CI paradigms are successfully tested for the specific application task: Circular BackPropagation (CBP) and Support Vector
Machines (SVMs). Experimental results on real data prove the advantage of the integrated approach over existing conventional methods. Individual CI components and the overall detection
system have been verified in real experiments.
References
[1] D. Yao, M.R. Azimi-Sadjadi, A.A. Jamshidi, and G.J. Dobeck, “A
study of effects of sonar bandwidth for underwater target
classification,” IEEE Journal of Oceanic Engineering, vol. 27, July
2002 pp. 619 - 627
[2] D. Li, M.R. Azimi-Sadjiadi, and M. Robinson, “Comparison of
different classification algorithms for underwater target
discrimination,” IEEE Transactions on Neural Networks, vol. 15,
Jan. 2004, pp. 189-194
[3] M.R. Azimi-Sadjadi, D. Yao, Q. Huang, and G.J. Dobeck,
“Underwater target classification using wavelet packets and neural
networks,” IEEE Trans. on Neural Networks, vol. 11, May 2000, pp.
784-794
[4] M. R. Azimi-Sadjadi, D. Yao, A.A. Jamshidi, and J.G. Dobeck,
“Underwater target classification in changing environments using an
adaptive feature mapping,” IEEE Trans. on Neural Networks, vol. 13,
Sept. 2002, pp. 1099-1111
[5] R.P. Gorman and T.J. Sejnowski, “Analysis of Hidden Units in a
Layered Network Trained to Classify Sonar Targets,” Neural
Networks, vol. 1, 1988, pp. 75-89.
[6] R.J. Urick. Principles of Underwater Sound. McGraw-Hill (New
York), 1983.
[7] O. Faggioni, A. Gabellone, R. Hollett, R.T. Kessel, and M. Soldani,
“Anti-intruder port protection MAC (Magnetic ACoustic) System:
advances in the magnetic component performance,” 1st WSS
Conference, August 25-28, Copenhagen, Denmark, 2008.
[8] V. Vapnik, Statistical Learning Theory, John Wiley, New York,
1998, pp. 339-346
[9] S. Ridella, S. Rovetta, and R. Zunino, “Circular backpropagation
networks for classification,” IEEE Trans. on Neural Networks, vol. 8,
no. 1, 1997, , pp. 84-97
[10] A. Gabellone, O. Faggioni, M. Soldani, and P. Guerrini, “CAIMAN
(Coastal Anti Intruder MAgnetometers Network),” Proc. of RTO-MPSET-
130 Symposium on NATO Military Sensing, March 12-14,
Orlando, Florida, USA, 2008, NATO classified.
[11] L.P. Wang and X.J. Fu, Data Mining with Computational
Intelligence, Springer, Berlin, 2005.
[12] K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward
networks are universal approximators,” Neural Networks, vol. 2, no.
5, pp. 359–356,1989.
[13] S. Dasgupta and A. Gupta, “An elementary proof of the Johnson–
Lindenstrauss lemma”, Technical report 99–006, U. C. Berkeley,
March 1999.
[14] S. Decherchi, P. Gastaldo, and R. Zunino, "K-Means clustering for
Content Based Document Management in Intelligence," in Advances
In Artificial Intelligence for Privacy Protection and Security, Editors:
Augusti Solanas and Antoni Martinez Bellesté, World Scientific
Publishing, 2009
[15] D. Leoncini, S. Decherchi, O. Faggioni, P. Gastaldo, M. Soldani, and
R. Zunino, “A Preliminary Study on SVM based Analysis of
Underwater Magnetic Signals For Port Protection,” Proc. CISIS 2009,
Burgos, Spain.
[16] C.C.Chang and C.J. Lin, “LibSVM: a library for Support Vector
Machines” [http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf]
study of effects of sonar bandwidth for underwater target
classification,” IEEE Journal of Oceanic Engineering, vol. 27, July
2002 pp. 619 - 627
[2] D. Li, M.R. Azimi-Sadjiadi, and M. Robinson, “Comparison of
different classification algorithms for underwater target
discrimination,” IEEE Transactions on Neural Networks, vol. 15,
Jan. 2004, pp. 189-194
[3] M.R. Azimi-Sadjadi, D. Yao, Q. Huang, and G.J. Dobeck,
“Underwater target classification using wavelet packets and neural
networks,” IEEE Trans. on Neural Networks, vol. 11, May 2000, pp.
784-794
[4] M. R. Azimi-Sadjadi, D. Yao, A.A. Jamshidi, and J.G. Dobeck,
“Underwater target classification in changing environments using an
adaptive feature mapping,” IEEE Trans. on Neural Networks, vol. 13,
Sept. 2002, pp. 1099-1111
[5] R.P. Gorman and T.J. Sejnowski, “Analysis of Hidden Units in a
Layered Network Trained to Classify Sonar Targets,” Neural
Networks, vol. 1, 1988, pp. 75-89.
[6] R.J. Urick. Principles of Underwater Sound. McGraw-Hill (New
York), 1983.
[7] O. Faggioni, A. Gabellone, R. Hollett, R.T. Kessel, and M. Soldani,
“Anti-intruder port protection MAC (Magnetic ACoustic) System:
advances in the magnetic component performance,” 1st WSS
Conference, August 25-28, Copenhagen, Denmark, 2008.
[8] V. Vapnik, Statistical Learning Theory, John Wiley, New York,
1998, pp. 339-346
[9] S. Ridella, S. Rovetta, and R. Zunino, “Circular backpropagation
networks for classification,” IEEE Trans. on Neural Networks, vol. 8,
no. 1, 1997, , pp. 84-97
[10] A. Gabellone, O. Faggioni, M. Soldani, and P. Guerrini, “CAIMAN
(Coastal Anti Intruder MAgnetometers Network),” Proc. of RTO-MPSET-
130 Symposium on NATO Military Sensing, March 12-14,
Orlando, Florida, USA, 2008, NATO classified.
[11] L.P. Wang and X.J. Fu, Data Mining with Computational
Intelligence, Springer, Berlin, 2005.
[12] K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward
networks are universal approximators,” Neural Networks, vol. 2, no.
5, pp. 359–356,1989.
[13] S. Dasgupta and A. Gupta, “An elementary proof of the Johnson–
Lindenstrauss lemma”, Technical report 99–006, U. C. Berkeley,
March 1999.
[14] S. Decherchi, P. Gastaldo, and R. Zunino, "K-Means clustering for
Content Based Document Management in Intelligence," in Advances
In Artificial Intelligence for Privacy Protection and Security, Editors:
Augusti Solanas and Antoni Martinez Bellesté, World Scientific
Publishing, 2009
[15] D. Leoncini, S. Decherchi, O. Faggioni, P. Gastaldo, M. Soldani, and
R. Zunino, “A Preliminary Study on SVM based Analysis of
Underwater Magnetic Signals For Port Protection,” Proc. CISIS 2009,
Burgos, Spain.
[16] C.C.Chang and C.J. Lin, “LibSVM: a library for Support Vector
Machines” [http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf]
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