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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2016
    • vol. 3, nº 7, june 2016
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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2016
    • vol. 3, nº 7, june 2016
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    Improved Shape Parameter Estimation in K Clutter with Neural Networks and Deep Learning

    Autor: 
    Machado Fernández, José Raúl
    ;
    Bacallao Vidal, Jesús de la Concepción
    Fecha: 
    2016
    Palabra clave: 
    sea clutter; K distribution; shape parameter estimation; artificial neural networks; deep learning; IJIMAI
    Tipo de Ítem: 
    Articulo Revista Indexada
    URI: 
    https://reunir.unir.net/handle/123456789/11236
    DOI: 
    http://doi.org/ 10.9781/ijimai.2016.3714
    Dirección web: 
    https://www.ijimai.org/journal/bibcite/reference/2554
    Open Access
    Resumen:
    The discrimination of the clutter interfering signal is a current problem in modern radars’ design, especially in coastal or offshore environments where the histogram of the background signal often displays heavy tails. The statistical characterization of this signal is very important for the cancellation of sea clutter, whose behavior obeys a K distribution according to the commonly accepted criterion. By using neural networks, the authors propose a new method for estimating the K shape parameter, demonstrating its superiority over the classic alternative based on the Method of Moments. Whereas both solutions have a similar performance when the entire range of possible values of the shape parameter is evaluated, the neuronal alternative achieves a much more accurate estimation for the lower Fig.s of the parameter. This is exactly the desired behavior because the best estimate occurs for the most aggressive states of sea clutter. The final design, reached by processing three different sets of computer generated K samples, used a total of nine neural networks whose contribution is synthesized in the final estimate, thus the solution can be interpreted as a deep learning approximation. The results are to be applied in the improvement of radar detectors, particularly for maintaining the operational false alarm probability close to the one conceived in the design.
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