Automatic Identification Target in the SAR using Corrective PSO Algorithm

Document Type : Original Article

Authors

Abstract

The main purpose in this Paper, to express strong algorithmic optimization in their ability to detect expression target of SAR that use in aircraft, plane and satellites to observe the Target on the ground. For this purpose,the SAR technology and its applications are introduced. Previous algorithms used in this field with poor diagnosis and appropriate speed is low, like Means and fuzzy and PSO algorithms. Also, due to the lack of algorithms combines the former, require the use of modern methods and some combination. In this Paper tried to choose a new evolutionary algorithm with distribution into local optima and proportion of the public data, that the PSO algorithm is most appropriate. The biggest problem in particle swarm algorithm getting stuck at a local optimum must incorporate it into combinatorial also fix the problem that eventually got GAPSO algorithm.

Keywords

References

  1. G. W. Stimson, “Introduction to Airborne Radar”; Mendham
  2. New Jersey,1998.
  3. G. Franceschetti, R. Lanari; “Synthetic. Aperture Radar
  4. Processing”, published by CRC Press, 1999.
  5. A. I. Mohungoo, “An Airborne SAR Receiver Design and
  6. Implementation”; University of Cape Town, 2004.
  7. D. G. Coetzer, “Design and Implementation of a X-band
  8. Transmitter and Frequency Distribution Unit for a Synthetic
  9. Aperture Radar”; Masters thesis, University of Cape Town,
  10.  
  11. M. Y. Chua and V. C. Koo, “Fpga-based chirp generator for
  12. high resolution uav SAR”, Progress In Electromagnetics
  13. Research; Faculty of Engineering and
  14. Technology,Multimedia University,Jalan Ayer Keroh Lama,
  15. Melaka 75450, Malaysia, 2009.
  16. R. C. Gonzalez and R. E. Woods, “Digital Image Processing
  17. (2nd ed.), Prentice-Hall, 2003.
  18. J. B. MacQueen; “Some Methods for classification and
  19. Analysis of Multivariate Observations”; Proceedings of 5-th
  20. Berkeley Symposium on Mathematical Statistics and
  21. Probability, Berkeley, University of California Press, 1967,
  22. -297.
  23. E. Alpaydin, “Introduction to Machine Learning”; the MIT
  24. Press, 2004.
  25. J. Sander, “Principles of Knowledge Discovery in Data:
  26. Clustering I”; Department of Computing Science University
  27. of Alberta, Tutorial Slides, 2003.
  28. M. Halkidi, Y. Batistakis and M. Vazirgiannis; “On
  29. Clustering Validation Techniques”, Journal of Intelligent
  30. Systems, vol. 17:2/3, pp 107-145, 2001.
  31. R. Duda, P. Hart; “Pattern Classification and Scene
  32. Analysis”; John Wiley & Sons, 1973.
  33. G. E. Tsekouras and H. Sarimveis; “A New Approach for
  34. Measuring the Validity of the Fuzzy C-Means Algorithm”,
  35. Advances in Engineering Software, vol. 35, pp. 567–575,
  36.  
  37. N. Zahid, M. Limouri and A. Essaid, “A New Cluster-
  38. Validity for Fuzzy Clustering”, Pattern Recognition , vol. 32,
  39. pp. 1089-1097, 1999.
  40. J. Kennedy and R. Eberhart, “Particle Swarm Optimization”;
  41. Proceedings of IEEE International Conference on Neural
  42. Networks, pp. 1942–1948, 1995.
  43. Y. Zhang and S.Wang, “Pathological Brain Detection in
  44. Magnetic Resonance Imaging Scanning by Wavelet Entropy
  45. and Hybridization of Biogeography-based Optimization and
  46. Particle Swarm Optimization”. Progress in Electromagnetics
  47. Research, Pier, vol. 152, pp. 41–58, 2015.
  48. R. C. Eberhart and Y. Shi, “Comparing inertia weights and
  49. constriction factors in particle swarm optimization”. Proc.
  50. Congress on Evolutionary Computation 2000, San Diego,
  51. CA, pp. 84-88, 2000.
  52. Y. Shi and R. C. Eberhart, “A Modified Particle Swarm
  53. Optimizer”, IEEE International Conference of Evolutionary
  54. Computation, Anchorage, Alaska, May 1998.
  55. A. Salman M. Omran and A. Engelbrecht; “Image
  56. classification using particle swarm optimization”, in
  57. Conference on Simulated Evolution and Learning,
  58. Singapore, vol. 1, pp. 370–374, 2002.
  59. X. Cui, T. E. Potok, and P. Palathingal, “Document
  60. Clustering using Particle Swarm Optimization” , Applied
  61. Software Engineering Research Group Computational
  62. Sciences and Engineering Division Oak Ridge National
  63. Laboratory, IEEE International Conference , 2005.
  64. R. G. Congalton, R. G. Oderwald, and R. A. Mead,
  65. “Assessing Landsat classification accuracy using discrete
  66. multivariate statistical techniques”; Photogramm. Eng.
  67. Remote Sens, 1983, 1671-1678.
  68. T. Blackwell and J. Branke, and X. Li, “Particle swarms for
  69. Dynamic optimization problems Swarm Intelligence”;
  70. Springer Berlin Heidelberg, pp. 193-217, 2008.
  71. Y. Mimoun and F. Benhamida, “Genetic Algorithm-Particle
  72. Swarm Optimization (GA-PSO) for Economic Load
  73. Dispatch”; p. 369, 2011.
  74. D. H. Kim; “Improvement of Genetic Algorithm Using PSO
  75. and Euclidean Data Distance”; intjit.org, Special Issue on
  76. Intelligent Computing, vol. 12, pp.142–148, 2006.
Radar
Volume 4, Issue 2 - Serial Number 2
November 2016
Pages 45-53

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History

  • Receive Date: 01 January 2016
  • Revise Date: 22 January 2024
  • Accept Date: 19 September 2018
  • Publish Date: 22 July 2016

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