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Vol. 17 No. 2 (2017)
July-December

ARTICULOS

Technology applied in the operation and detection of antipersonnel mines: state of the art

https://doi.org/10.19053/1900771X.v17.n2.2017.7182

Published 2017-07-04

  • Javier Andrés Ledezma-Ríos

Javier Andrés Ledezma-Ríos
Universidad del Cauca, Popayán-Cauca, Colombia

Abstract

The main objective of this investigation is to know the different technologies implemented for the detection of antipersonnel mines, documented by different bibliographic means of the latest updates used for the detection of buried objects, the factors that affect the loss of energy of the waves as transmitters of information between them, the characteristics of the soil, the amplitude of the emitted signal, the frequency and the conditions of the terrain. This paper informs about the computational means, of their work with the different algorithms to model correct information of what is happening with the phenomenon of detection. Thus, through this research, the scientific community is informed on the parameters of magnetic susceptibility, the percentage of water and porosity of the environment where the emitted waves react, the difficulty of the stability of the signal to be captured to detect antipersonnel mines, in a geographical context. Currently, PVC tubes, cans, syringes and hand-held devices are being used for their production, and the waves will behave differently against these materials.

Keywords

Research, waves, terrain, transmitter, manufacturing.

PDF (Español)

References

  1. L.H. Morales-Pinto, M.C. Fuentes, J.A. Hernández. (2013, jul.). Monitoreo y control de vibraciones por efecto de voladuras en el túnel Sumapaz, concesión Bogotá Girardot. Revista Ingeniería, Investigación y Desarrollo. [En línea]. 13(2), 15-21. Disponible: http://revistas.uptc.edu.co/index.php/ingenieria_ sogamoso/article/view/3419 DOI: https://doi.org/10.19053/1900771X.3419
  2. O. Lopera y N. Milisavljevic. (2007). Prediction of the effects of soil and target properties on the antipersonnel landmine detection performance of ground-penetrating radar: A Colombian case study. Journal of Applied Geophysics. [En línea]. 63(1), 13–23.Disponible: https://doi.org/10.1016/j.jappgeo.2007.02.002 DOI: https://doi.org/10.1016/j.jappgeo.2007.02.002
  3. F. Liu, Y. Ling, X. Xia y X. Shi. (2004). Wavelet methods for Ground Penetrating Radar imaging. Journal of Computational and Applied Mathematics. [En línea]. 169(2), 459–474. Disponible: https://doi. org/10.1016/j.cam.2003.12.036 DOI: https://doi.org/10.1016/j.cam.2003.12.036
  4. N.A. Duarte-Pinilla y H. Paz-Penagos. (2015, jul.). Justificación de una propuesta regulatoria para radiodifusión sonora y transmóviles en las fronteras colombianas. Revista Ingeniería, Investigación y Desarrollo. [En línea]. 15(2), 31-41. Disponible: http://revistas.uptc.edu.co/index.php/ingenieria_ sogamoso/article/view/4248 DOI: https://doi.org/10.19053/1900771X.4248
  5. A.J. Arrieta-Fuentes. (2016, jul.) Dispersión de material particulado (PM 10), con interrelación de factores meteorológicos y topográficos. Revista Ingeniería, Investigación y Desarrollo. [En línea]. 16(2), 43-54. Disponible: http://revistas.uptc.edu.co/index.php/ingenieria_sogamoso/article/view/5445 DOI: https://doi.org/10.19053/1900771X.v16.n2.2016.5445
  6. C. Colla y C. Maierhofer. (2000). Investigations of historic masonry via radar reflection and tomography. Presentado en 2000 8th International Conference on Ground Penetrat- ing Radar (GPR). [En línea]. Disponible: http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=912896 DOI: https://doi.org/10.1117/12.383535
  7. S.A. Arcone y A.J. Delaney. (2000). Gpr images of hidden crevasses in Antarctica. Presentado en 2000 8th International Conference on Ground Penetrating Radar (GPR). [En línea]. Disponible: http://spie.org/Publications/Proceedings/Paper/10.1117/12.383512
  8. D. Daniels. (2004). Ground Penetrating Radar. Stevenage: IET Press. [En línea]. Disponible: http://digital-library.theiet.org/content/journals/10.1049/ el.2017.1570 DOI: https://doi.org/10.1049/PBRA015E
  9. C. van Coevorden, A. Bretones, M. Pantoja, F. Ruiz, S. García y R. Martin. (2006). Ga design of a thin-wire bow-tie antenna for Gpr applications. IEEE Trans. Geosci. Remote Sens. [En línea]. 44(4), 1004–1010. Disponible : http://ieeexplore.ieee. org/document/1610835/ DOI: https://doi.org/10.1109/TGRS.2005.862264
  10. R.M. Morey, S.M. Conklin, S.P. Farrington y J.D. ShinnII. (1999). Tomographic Site Characterization Using CPT, ERT and GPR. [En línea]. Disponible: https://www.osti.gov/scitech/servlets/purl/773811 DOI: https://doi.org/10.2172/773811
  11. N. Joachimowicz, C. Pichot y J.-P. Hugonin. (1991). Inverse scattering: an iterative numerical method for electromagnetic imaging. IEEE Trans. Antennas Propag. [En línea]. 39(12), 1742–1753. Disponible: http://ieeexplore.ieee.org/abstract/document/121595/ DOI: https://doi.org/10.1109/8.121595
  12. C. Christodoulou y M. Georgiopoulos, Applications of Neural Networks in Electromagnetics. Norwood: Artech House, 2001.
  13. D. A. Gomes Vieira, Rede. perceptron com camadas paralelas (plp-parallel layer perceptron) (Ph.D. thesis), Universidade Federal de Minas Gerais, 2006.
  14. W.M. Caminhas, D.A.G. Vieira y J.A. Vasconcelos. (2003). Parallel layer perceptron, Neurocomputing. [En línea]. 55 (3–4), 771–778. Disponible: https:// www.researchgate.net/publication/222302650_ Parallel_layer_perceptron DOI: https://doi.org/10.1016/S0925-2312(03)00440-5
  15. C. Christodoulou, J. Huang y M. Georgiopoulos. (1995). Design of gratings and frequency-selective surfaces using artmap neural networks, J. Electromagn. Waves Appl. [En línea]. 9 (1/2), 17–36. Disponible: http://tandfonline.com/doi/ abs/10.1163/156939395X00235?tab=permissions&scroll=top& DOI: https://doi.org/10.1163/156939395X00235
  16. M.K. Smail, Y.L. Bihan y L. Pichon, “Fast diagnosis of transmission lines using neural networks and principal component analysis”, Int. J. Appl. Electromagn. Mech., vol. 39 n.º 1, 2012, pp. 435–441. DOI: https://doi.org/10.3233/JAE-2012-1493
  17. S. Caorsi y G. Cevini. (2005). An electromagnetic approach based on neural networks for the gpr investigation of buried cylinders, IEEE Geosci. Remote Sens. Lett. [En línea]. 2 (1) 3–7. Disponible: http:// ieeexplore.ieee.org/document/1381337/ DOI: https://doi.org/10.1109/LGRS.2004.839648
  18. L. Newnham y A. Goodier. (2000). Using neural networks to interpret subsurface radar imagery of reinforced concrete 4084. [En línea]. Disponible: http:// proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=912595 DOI: https://doi.org/10.1117/12.383607
  19. X. Travassos, D. Vieira, N. Ida, C. Vollaire y A. Nicolas. (2008) Characterization of inclusions in a nonhomogeneous gpr problem by artificial neural networks, IEEE Trans. Magn. [En línea]. 44(6) 1630– DOI: https://doi.org/10.1109/TMAG.2007.915332
  20. Disponible: http://ieeexplore.ieee.org/document/4527012/
  21. S.G. García, A.R. Bretones, B.G. Olmedo y R.G. Martn, “Finite difference time domain methods”, in Time Domain Techniques in Computational Electromagnetics, D. Poljak (Ed.), Ashurst, UK: WIT Press, 2003, pp. 91–132.
  22. A. Giannopoulos. (1997). The investigation of transmission-line matrix and finite- difference time-domain methods for the forward problem of ground probing radar (Ph.D. thesis), University of York, 1997. [En línea]. Disponible: https://www. researchgate.net/publication/35719439_The_investigation_of_transmission-line_matrix_and_finite-difference_time-domain_methods_for_the_ forward_problem_of_ground_probing_radar
  23. R. Godoy-Rubio. (2005). Métodos de diferencias finitas incondicionalmente estables para la resolución de las ecuaciones de maxwell en el dominio del tiempo (Ph. D. thesis), Universidad de Granada. [En línea]. Disponible: https://dialnet.unirioja.es/servlet/tesis?codigo=108732
  24. B.G. Olmedo, S.G. García, A.R. Bretones y R.G. Martin. (2005). New trends in FDTD methods in computational electrodynamics: unconditionally stable schemes, in Recent Res. Development in Electronics, Transworld Research Network. [En línea]. Disponible en: http://personalpages.manchester. ac.uk/staff/fumie.costen/tmp/TRNEL18_corrected2.pdf
  25. R. Tibshirani, T. Hastie y J. Friedman, The Elements of Statistical Learning, New York City: Springer, 2001.
  26. S. Campana y S. Piro. (2008). Seeing the Unseen, Geophysics and Landscape Archae- ology. London: A Balkema Book, Taylor & Francis. [En línea]. Disponible: http://www.crcnetbase.com/doi/ pdf/10.1201/9780203889558.fmatt DOI: https://doi.org/10.1201/9780203889558
  27. J.B, Rodríguez, et al. (2015). A prediction algorithm for data analysis in GPR-based surveys. [En línea]. Disponible: https://www.researchgate.net/publication/279160326_A_Prediction_Algorithm_for_ Data_Analysis_in_GPR-based_Surveys DOI: https://doi.org/10.1016/j.neucom.2015.05.081
  28. S. Stergiopoulos. (2000). Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging Real Time Systems. Boca Raton: CR Press. [En línea]. Disponible: https://www.crcpress.com/Advanced-Signal-Processing-Handbook-Theory-and-Implementation-for-Radar/Stergiopoulos/p/ book/9781420037395 DOI: https://doi.org/10.1201/9781420037395
  29. L. Travassos, D.A.G. Vieira, N. Ida y A. Nicolas. (2009). In the use of parametric and non-parametric algorithms for the nondestructive evaluation of concrete struc- tures, Res. Nondestruct. Eval. [En línea]. 20(2), 71–93. Disponible: https://www.ncbi. nlm.nih.gov/pmc/articles/PMC3252010/ DOI: https://doi.org/10.1080/09349840802513242
  30. F. Higuera, et al. (2013). Diseño y construcción de un prototipo de generador hidráulico para estudio y desarrollo de estrategias de control para la generación eléctrica en minas subterráneas, Revista Ingeniería, Investigación y Desarrollo. [En línea]. 13(2), 22-27. Disponible: http://revistas.uptc. edu.co/index.php/ingenieria_sogamoso/article/ view/3421 DOI: https://doi.org/10.19053/1900771X.3421
  31. D. A. Gomes Vieira, R. H. Caldeira Takahashi, V. Palade, J. A. Vasconcelos y W. Matos Caminhas, “The q -norm complexity measure and the minimum gradient method: a novel approach to the machine learning structural risk minimization problem”, IEEE Trans. Neural Netw., vol. 19 n.º 8, 2008, pp. 1415–1430. DOI: https://doi.org/10.1109/TNN.2008.2000442
  32. I.T. Jolliffe. (2002). Principal Component Analysis. New York City: Springer. [En línea]. Disponible: http://www.springer.com/us/ book/9780387954424
  33. F. Rosenblatt. (1958). The perceptron: a probabilistic model for information storage and organization in the brain, Psychol. Rev. [En línea]. 65 (6), 386–408. Disponible: http://citeseerx.ist.psu.edu/ viewdoc/summary?doi=10.1.1.588.3775 DOI: https://doi.org/10.1037/h0042519
  34. F. Rosenblatt, (1962). Principles of Neurodynamics. Washington:,Spartan Books. [En línea]. Disponible: https://catalog.hathitrust.org/Record/000203591
  35. M. Minsky y S. Papert, Principal Component Analysis. Cambridge, MA: MIT Press, 1969.
  36. H. Ortiz, I. Gómez, F. Angarita Cediel y C. A. Neira Triana. (2016). Amplificador operacional de transconductancia con alto rango modo común y bajo consumo de potencia. Ingeniería Investigación y Desarrollo [En línea]. 16(2), 78-83. Disponible: http://revistas.uptc.edu.co/index.php/ingenieria_ sogamoso/article/view/5448 DOI: https://doi.org/10.19053/1900771X.v16.n2.2016.5448
  37. S. Grossberg. (1976). Adaptive pattern classification and universal recoding: I. parallel development and coding of neural feature detectors. Biol. Cybern. [En línea]. 23(3), 121–134. DOI: https://doi.org/10.1007/BF00344744
  38. Disponible: https://link.springer.com/article/10.1007/ BF00344744
  39. T. Kohonen. (1972). Correlation matrix memories, IEEE Trans. Comput. [En línea]. C-21(4), 353–359. DOI: https://doi.org/10.1109/TC.1972.5008975
  40. Disponible: http://ieeexplore.ieee.org/document/5008975/
  41. K. Fukushima. (1980). Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biol. Cybern. [En línea]. 36(4), 193–202. Disponible: https://www.ncbi.nlm.nih.gov/pubmed/7370364 DOI: https://doi.org/10.1007/BF00344251
  42. J.J. Hopfield. (2010). Neural networks and physical system with emergent collective computational abilities, Proc. Natl. Acad. Sci. [En línea]. 79, 2554–2558. Disponible: http://www.pnas.org/ content/79/8/2554.abstract DOI: https://doi.org/10.1073/pnas.79.8.2554
  43. D.E. Rumelhart, G.E. Hinton y R.J. Williams. (1986). Learning internal representations by error propagation, in: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, Cambridge, MA, USA: MIT Press, pp. 318–362. [En línea]. Disponible: http://dl.acm.org/citation. cfm?id=104293
  44. J. Shing y R. Jang. (1993). Anfis: Adaptive-network-based fuzzy inference system, IEEE Trans. Syst. Man Cybern. [En línea]. 23, 665–685. Disponible: http://ieeexplore.ieee.org/document/256541/ DOI: https://doi.org/10.1109/21.256541
  45. J.D. Toews, y W.Sirovyak. (2003). Metal Detector Trial – Colombia, DRDC Suffield TM 2003-099, pp. 3-7. [En línea]. Disponible: https://www.yumpu. com/en/document/view/16574172/metal-detector-trial-colombia-gichd
  46. Mullins, C.E. (1977). Magnetic susceptibility of the soil and its significance in soil science: a review, J. Soil Sci. [En línea]. 28, 223246. Disponible: http://onlinelibrary.wiley.com/ doi/10.1111/j.1365-2389.1977.tb02232.x/abstract DOI: https://doi.org/10.1111/j.1365-2389.1977.tb02232.x
  47. J.A. Dearing. (1994). Environmental Magnetic Susceptibility. Kenilworth, UK: Chi Publishing. [En línea]. Disponible: http://onlinelibrary.wiley.com/ doi/10.1111/j.1365-246X.1996.tb04051.x/full
  48. A.S. Balanis, “Geometrical Theory of Diffraction” en Advanced Engineering Electromagnetics. New York: John Wiley & Sons, 1989.
  49. M. Hagan y M.-B. Menhaj. (1994). Training feedforward networks with the Marquardt algorithm, IEEE Trans. Neural Netw. [En línea]. 5(6), 989–993. Disponible: http://ieeexplore.ieee.org/document/329697/ DOI: https://doi.org/10.1109/72.329697
  50. C. Guattari, F. Amico y A. Benedetto. (2010). Integrated road pavement survey using gpr and lfwd, in 2010 13th International Conference on Ground Penetrating Radar (GPR), 2010, pp. 1–6. [En línea]. Disponible: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5540532&filter%3DAND%28p_IS_Number%3A5550063%29&pageNumber=5
  51. K.-Y. Huang y K.-S. Fu. (1987). Decision-theoretic approach for classification of ricker wavelets and detection of seismic anomalies, IEEE Trans. Geosci. Remote Sens. [En línea]. GE-25(2), 118–123. Disponible: http://ieeexplore.ieee.org/document/4072616/ DOI: https://doi.org/10.1109/TGRS.1987.289721
  52. A. M Alani, M. Aboutalebi y G. Kilic. (2013). Applications of ground penetrating radar (GPR) in bridge deck monitoring and assessment. Journal of Applied Geophysics. [En línea]. 97, 45–54. Disponible: https://doi.org/10.1016/j.jappgeo.2013.04.009 DOI: https://doi.org/10.1016/j.jappgeo.2013.04.009
  53. H. W. Yang, Z. Yang Kun y Y. Kun Pei. (2014). Ground-penetrating radar for soil and underground pipelines using the forward modeling simulation method. Optik International Journal for Light and Electron Optics. [En línea]. 125(23), 7075–7079. Disponible: https://doi.org/10.1016/j. ijleo.2014.08.099 DOI: https://doi.org/10.1016/j.ijleo.2014.08.099
  54. R. Rouveure, P. Faure y M.O. Monod. (2016). PELICAN: Panoramic millimeter-wave radarfor perception in mobile robotics applications, Part 1: Principles of FMCW radar and of 2D image construction. Robotics and Autonomous Systems. [En línea]. 81, 1–16. Disponible: https://doi.org/10.1016/j.robot.2016.04.001 DOI: https://doi.org/10.1016/j.robot.2016.04.001
  55. M. Rucka, J. Lachowicz y M. Zielioska, M. (2016). GPR investigation of the strengthening system of a historic masonry tower. Journal of Applied Geophysics. [En línea]. Disponible: https://doi. org/10.1016/j.jappgeo.2016.05.014 DOI: https://doi.org/10.1016/j.jappgeo.2016.05.014
  56. A. Ruffell. (2006). Under-water Scene Investigation Using Ground Penetrating Radar (GPR) in the Search for a Sunken Jet ski, Northern Ireland. Science & Justice. [En línea]. 46(4), 221–230. Disponible: https://www.ncbi.nlm.nih.gov/pubmed/17500424 DOI: https://doi.org/10.1016/S1355-0306(06)71602-1
  57. F. Sagnard, C. Norgeot, X. Derobert, V. Baltazart, E. Merliot, F. Derkx y B. Lebental. (2016). Utility detection and positioning on the urban site Sense-City using Ground-Penetrating Radar systems. Measurement. [En línea]. 88, 318–330.
  58. Disponible: https://doi.org/10.1016/j.measurement.2016.03.044 DOI: https://doi.org/10.1016/j.measurement.2016.03.044
  59. G.P. Tsoflias, S.L. De Vore y M. Lynott (2016). Combining ER and GPR surveys for evidence of prehistoric landscape construction: case study at Mound City, Ohio, USA. Journal of Applied Geophysics. [En línea]. 129, 178–186. Disponible: https://doi. org/10.1016/j.jappgeo.2016.04.002 DOI: https://doi.org/10.1016/j.jappgeo.2016.04.002
  60. L. Seyfi y E. Yaldız. (2010). A novel software for an energy efficient GPR. Advances in Engineering Software. [En línea]. 41(10), 1195–1199. Disponible: http://dl.acm.org/citation.cfm?id=1862460 DOI: https://doi.org/10.1016/j.advengsoft.2010.07.007
  61. D. Seyfried, R. Jansen y J. Schoebel (2014). Shielded loaded bowtie antenna incorporating the presence of paving structure for improved GPR pipe detection. Journal of Applied Geophysics. [En línea]. 111, 289–298. Disponible: https://doi.org/10.1016/j. jappgeo.2014.10.019 DOI: https://doi.org/10.1016/j.jappgeo.2014.10.019
  62. D. Seyfried y J. Schoebel. (2015). Detection capability of a pulsed Ground Penetrating Radar utilizing an oscilloscope and Radargram Fusion Approach for optimal signal quality. Journal of Applied Geophysics. [En línea]. 118, 167–174. Disponible: https://doi.org/10.1016/j.jappgeo.2015.03.029 DOI: https://doi.org/10.1016/j.jappgeo.2015.03.029
  63. D. Seyfried y J. Schoebel. (2016). Ground penetrating radar for asparagus detection. Journal of Applied Geophysics. [En línea]. 126, 191–197. Disponible: https://doi.org/10.1016/j.jappgeo.2016.01.022 DOI: https://doi.org/10.1016/j.jappgeo.2016.01.022
  64. D. Seyfried, K. Schubert y J. Schoebel. (2014a). Investigations on the sensitivity of a stepped- frequency radar utilizing a vector network analyzer for Ground Penetrating Radar. Journal of Applied Geophysics. [En línea]. 111, 234–241. Disponible: http://www.sciencedirect.com/science/article/pii/ S0926985114003103 DOI: https://doi.org/10.1016/j.jappgeo.2014.10.016
  65. A. Taflove y S. C. Hagness. (s.f.). Computational Electrodynamics: The Images of the front covers of Allen’s FDTD books published in 1995. [En línea]. Disponible: http://www.ece.northwestern.edu/ ecefaculty/Allen1.html

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