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Optimal selection of the CA-CFAR adjustment factor for K power sea clutter with statistical variations

Selección óptima del factor de ajuste CA-CFAR para clutter marino de potencia K estadísticamente variable

dc.creatorMachado Fernández, José Raúl
dc.creatorBacallao Vidal, Jesús de la Concepción
dc.date2017-01-18
dc.date.accessioned2022-12-15T16:04:01Z
dc.date.available2022-12-15T16:04:01Z
dc.identifierhttps://revistas.unimilitar.edu.co/index.php/rcin/article/view/1714
dc.identifier10.18359/rcin.1714
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5355661
dc.descriptionThe presence of the sea clutter interfering signal sets limitations on the quality of radar detection in coastal and ocean environments. The CA-CFAR processor is the classic solution for detecting radar targets. It usually operates keeping constant its adjustment factor during the entire operation period. As a consequence, the scheme does not take into account the slow statistical variations of the background signal when performing the clutter discrimination. To solve this problem, the authors conducted an intensive processing of 40 million computer generated clutter power samples in MATLAB. As a result, they found the optimal adjustment factor values to be applied in 40 possible clutter statistical states, suggesting thus the use of the CA-CFAR architecture with a variable adjustment factor. In addition, a curve fitting procedure was performed, obtaining mathematical expressions that generalize the results for the whole addressed range of clutter statistical states. The experiments were executed with a 64 cells CA-CFAR and found the adjustment factor values for three common false alarms probabilities. The K distribution was used as clutter model, thanks to its wide popularity. This paper facilitates the handling of the K power distribution avoiding the use of Gamma and Bessel functions, commonly found in developments related to the K model. Moreover, requirements for building an adaptive clutter detector in K power clutter with a priori knowledge of the shape parameter were fulfill. Also, several recommendations are given to continue the development of a more overall solution which will also include the estimation of the shape parameter.en-US
dc.descriptionLa presencia de la señal interferente de clutter marino establece limitaciones en la calidad de la detección de radar en ambientes costeros y de alta mar. El procesador CA-CFAR es la solución clásica para detectar blancos de radar. Usualmente mantiene su factor de ajuste constante todo el período de operación. Como consecuencia, el esquema no toma en consideración las variaciones estadísticas de la señal de fondo cuando realiza la discriminación del clutter. Para resolver este problema, los autores realizaron un procesamiento intensivo de 40 millones de muestras de clutter de intensidad, generadas en computadora a través de MATLAB. Como resultado, encontraron los valores óptimos del factor de ajuste a ser aplicados para 40 posibles estados estadísticos del clutter, sugiriendo el uso de la arquitectura CA-CFAR con un factor de ajuste variable. Adicionalmente, fue llevado a cabo un ajuste de curvas, obteniéndose expresiones matemáticas que generalizan los resultados en todo el intervalo de considerado de estados estadísticos del clutter. Los experimentos se ejecutaron con un CA-CFAR de 64 celdas y apuntaron a encontrar los valores del factor de ajuste para tres probabilidades de falsa alarma comunes. La distribución K fue elegida como el modelo usado para el clutter, gracias a su amplia popularidad. Este artículo facilita el manejo de la distribución K de intensidad, evitando el uso de funciones Gamma y Bessel, comúnmente encontradas en desarrollos relacionados con el modelo K. Además, fueron cumplidos los requerimientos necesarios para construir un detector adaptativo en clutter de potencia K con conocimiento previo del parámetro de forma. Al mismo tiempo, fueron dadas varias recomendaciones para continuar el desarrollo de una solución más general que también incluirá la estimación del parámetro de forma.es-ES
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dc.languageeng
dc.publisherUniversidad Militar Nueva Granadaes-ES
dc.relationhttps://revistas.unimilitar.edu.co/index.php/rcin/article/view/1714/1680
dc.relationhttps://revistas.unimilitar.edu.co/index.php/rcin/article/view/1714/2484
dc.relationhttps://revistas.unimilitar.edu.co/index.php/rcin/article/view/1714/3087
dc.relationhttps://revistas.unimilitar.edu.co/index.php/rcin/article/view/1714/3088
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dc.rightsDerechos de autor 2016 Ciencia e Ingeniería Neogranadinaes-ES
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0es-ES
dc.sourceCiencia e Ingenieria Neogranadina; Vol. 27 No. 1 (2017); 61-76en-US
dc.sourceCiencia e Ingeniería Neogranadina; Vol. 27 Núm. 1 (2017); 61-76es-ES
dc.sourceCiencia e Ingeniería Neogranadina; v. 27 n. 1 (2017); 61-76pt-BR
dc.source1909-7735
dc.source0124-8170
dc.subjectsea clutteren-US
dc.subjectK power distributionen-US
dc.subjectCA-CFAR detectoren-US
dc.subjectselection of the adjustment factoren-US
dc.subjectadaptive detection thresholdsen-US
dc.subjectclutter marinoes-ES
dc.subjectdistribución K de la potenciaes-ES
dc.subjectdetector de promediación CA-CFARes-ES
dc.subjectselección del factor de ajustees-ES
dc.subjectadaptación del umbral de detecciónes-ES
dc.titleOptimal selection of the CA-CFAR adjustment factor for K power sea clutter with statistical variationsen-US
dc.titleSelección óptima del factor de ajuste CA-CFAR para clutter marino de potencia K estadísticamente variablees-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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