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Revista Cubana de Ciencias Informáticas
versão On-line ISSN 2227-1899
Resumo
GUILLEN SORIANO, Camilo e FERRY CHAVEZ, Nelson. Algorithms to Generate Random Samples following the Swerling Models. Rev cuba cienc informat [online]. 2019, vol.13, n.2, pp. 1-12. ISSN 2227-1899.
This article presents in an easily reproducible way, algorithms to generate samples of the radar video-signal ruled by the four Swerling models, in addition to the case of non-fluctuating target. The two widely used linear and quadratic detectors are taken into account for samples generation and it is verified that these samples present the required statistical characteristics. The proposed algorithms constitute the basis to develop simulations where it is necessary to reproduce video signals prior to any real implementation.
Palavras-chave : Radar; Swerling models; random samples generation.