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Ship detection by different data selection templates and multilayer perceptrons from incoherent maritime radar data

Ship detection by different data selection templates and multilayer perceptrons from incoherent maritime radar data

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This study presents a novel way for detecting ships in sea clutter. For this purpose, the information contained in the Radar images obtained by an incoherent X-band maritime Radar is used. The ship detection is solved by feedforward artificial neural networks, such as the multilayer perceptrons (MLPs). In a first approach, the MLP processes the information extracted from the Radar images using the commonly used horizontal and vertical selection templates. But, if a suitable combination of these selection templates is done, better detection performances are achieved. So, two improved selection templates are proposed, which are based on cross and plus shapes. All these templates are also applied in a commonly used detector taken as reference, the CA-CFAR detector. Its performance is compared with the one achieved by the proposed detector. This comparison shows how the MLP-based detector outperforms the CA-CFAR detector in all the cases under study. The results are presented in terms of objective (probabilities of false alarm and detection) and subjective estimations of their performances. The improved MLP-based detector also presents low computational cost and high robustness in its performance against changes in the sea conditions and ship properties.

References

    1. 1)
      • Introduction to radar systems
    2. 2)
      • Automatic identification system (AIS): Data reliability and human error implications
    3. 3)
    4. 4)
    5. 5)
      • Watts, S.: `The performance of cell-averaging CFAR systems in sea clutter', IEEE Int. Radar Conf., 2000, p. 398–403
    6. 6)
      • Automatic censoring CFAR detector based on ordered data variability for nonhomogeneous environments
    7. 7)
      • Introducing switching ordered statistic CFAR type I in different radar environments
    8. 8)
      • Performance of distributed CFAR processors in Pearson distributed clutter
    9. 9)
      • Neural networks. A comprehensive foundation
    10. 10)
      • The multilayer perceptron as an approximation to a Bayes optimal discriminant function
    11. 11)
      • Study of two error functions to approximate the Neyman-Pearson detector using supervised learning machines
    12. 12)
      • Sea clutter reduction and target enhancement by neural networks in a marine radar system
    13. 13)
      • Vicen-Bueno, R., Carrasco-Álvarez, R., Rosa-Zurera, M., Nieto-Borge, J.C.: `Sea clutter power reduction in radar measurement systems by feedforward multilayer perceptrons with medium input data integration rate', Proc. IEEE Int. Instrument and Measure Technology Conf. (12MTC 2009), 2009, p. 1069–1074
    14. 14)
      • López-Risueno, G., Grajal, J., Haykin, S., Díaz-Oliver, R.: `Convolutional neural networks for radar detection', Proc. Int. Conf. on Artificial Neural Networks, ICANN'02, 2002, p. 1150–1155
    15. 15)
      • Signal detection using the radial basis function coupled map lattice
    16. 16)
      • Radial basis function neural network for pulse radar detection
    17. 17)
      • Jun, Y., Xiaoyan, M., Qianhong, L., Bin, L., Bin, D.: `A modified RBF neural network and its application in radar', Proc. Fourth Int. Symp. on Neural Networks, ISNN'07, 2007, p. 981–987
    18. 18)
      • Radar survey of near shore bathymetry within the OROMA project
    19. 19)
      • Reichert, K., Hessner, K., Dannenberg, J., Trankmann, I., Lund, B.: `X-band radar as a tool to determine spectral and single wave properties', Eigth Int. Workshop on Wave Hindcasting and Forecasting, 2005
    20. 20)
      • World Meteorological Organization official Web site: http://www.wmo.int/pages/index_en.html
    21. 21)
      • Neural networks for pattern recognition
    22. 22)
      • Monte Carlo simulation and random number generation
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