Resumen

GONZALEZ PADILLA, Argel et al. Classification of sea clutter using artificial neural networks. EAC [online]. 2013, vol.34, n.1, pp.1-11. ISSN 1815-5928.

The radar detection under the action of sea clutter is an ongoing problem. The effectiveness of this detection can be improved or even optimized if the statistical behavior of the parameters of the signals scattered by the sea surface (sea clutter) is known. In this study, most of the statistical models of ocean clutter under different conditions is given and is accomplished in a single document synthetically group a large volume of information, hard to find, and in many cases, to interpret. The major contribution of this research work is the presentation of the foundations of anauto-adaptive system for detecting radar targets, based on the recognition of different distributions that model the sea clutter collected in a given time interval. Performing a finer classification to specify the range of values taken by the parameters of the distribution, for the time interval being analyzed. This system was successfully simulated using neural networks. The results revealed that can effectively perform recognition distributions of the marine clutter amplitude measurements and parameters distribution.

Palabras clave : sea clutter; auto adaptive system; neural networks.

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