New nonparametric detectors under K-distributed sea clutter in radar applications

Zhao, Zhong-Qiu; Ruilai, Xu; Huang, Yong; Guan, Jian

doi:10.1109/cie-radar.2011.6159909

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Con una enorme cantidad de desarrollos vinculados (Meng, Feng, Xue y He, 2013;Tanriverdi, 2012;Yunlong, Chao, Hongzhong y Qiang, 2012;Zhijian, Ruilai, Yong y Jian, 2011;Zhou y Wang, 2009), la distribución K debe su popularidad al hecho de que se relaciona con las características físicas del mar.…”

Section: Introductionunclassified

Modelación estadística de la textura del clutter marino en Matlab

Fernández

Hondal

2017

tecnura

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InvestIgacIón Modelación estadística de la textura del clutter marino en Matlab ResumenContexto: La modelación estadística de la señal interferente conocida como clutter marino se efectúa a través de dos componentes: uno de capilaridad y otro de textura. La distribución más utilizada para la textura es la gamma. No obstante, varios autores han defendido el uso alternativo de la inversa gamma y la inversa gaussiana. Método: Con el objetivo de brindar un acceso fá-cil a la manipulación de los modelos y a la realización de comparaciones entre ellos, los autores del presente artículo simularon en Matlab las caracterís-ticas principales de estas tres distribuciones. Adicionalmente, se agregó la distribución raíz gamma que sustituye a la gamma cuando se trabaja con muestras de amplitud. El método aplicado consistió en la revisión bibliográfica para encontrar las expresiones de cada uno de los parámetros modelados, y la posterior simulación computacional que permitió detectar errores ocasionales que surgen al consultar diferentes estudios. Resultados: Se creó una pequeña librería de simulación estocástica que incluye funciones de densidad y distribución, generación de variables aleatorias, estimación de parámetros y cálculo de momentos estadísticos, entre otros. Además, se elaboraron funciones informáticas complementarias que permitieron la validación por comparación con resultados dados por terceros y mediante la interacción de los diferentes componentes de la librería. Conclusiones: La librería creada habilita el uso de múltiples distribuciones, para la modelación del eco electromagnético de la superficie marina. Esto permitirá generar nuevos detectores de radar que se adapten a condiciones heterogéneas como las encontradas en las costas cubanas, donde alternan distintos niveles de profundidad, manglares, aguas salobres, islotes, vegetación acuática prominente, entre otras. Palabras clave: modelación estadística, clutter marino, textura del clutter, distribución gamma, distribución inversa gamma, distribución inversa gaussiana, distribución raíz gamma. AbstractContext: The statistical modeling of the interference signal known as sea clutter is achieved assuming the input results from the combination of two components: one for the speckle and another for the texture. The Gamma distribution is the more widely applied for the texture component. Nevertheless, several authors have defended the idea of using the Inverse Gamma and Inverse Gaussian instead.

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Section: Introductionunclassified

Modelación estadística de la textura del clutter marino en Matlab

Fernández

Hondal

2017

tecnura

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“…Although other models can be used [4][5][6], the K distribution is widely recognized as the best choice for sea clutter representation [7]. Consequently, several solutions have been proposed taking as a priori condition the assumption of K clutter [8][9][10].…”

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Improved Shape Parameter Estimation in K Clutter with Neural Networks and Deep Learning

Fernández¹,

Vidal²

2016

IJIMAI

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-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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“…The Weibull [4,5], Log-Normal [6,7] and K [8][9][10] distributions are commonly used for sea clutter modeling. They display heavy tails for certain configurations of their respective parameters, according to that observed in radar readings.…”

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CA-CFAR Adjustment Factor Correction with a priori Knowledge of the Clutter Distribution Shape Parameter

Fernández¹,

Martinez²,

Vidal³

2017

IJIMAI

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-Oceanic and coastal radars operation is affected because the targets information is received mixed with and undesired contribution called sea clutter. Specifically, the popular CA-CFAR processor is incapable of maintaining its design false alarm probability when facing clutter with statistical variations. In opposition to the classic alternative suggesting the use of a fixed adjustment factor, the authors propose a modification of the CA-CFAR scheme where the factor is constantly corrected according on the background signal statistical changes. Mathematically translated as a variation in the shape parameter of the clutter distribution, the background signal changes were simulated through the Weibull, Log-Normal and K distributions, deriving expressions which allow choosing an appropriate factor for each possible statistical state. The investigation contributes to the improvement of radar detection by suggesting the application of an adaptive scheme which assumes the clutter shape parameter is known a priori. The offered mathematical expressions are valid for three false alarm probabilities and several windows sizes, covering also a wide range of clutter conditions.

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New nonparametric detectors under K-distributed sea clutter in radar applications

Cited by 5 publications

References 9 publications

Modelación estadística de la textura del clutter marino en Matlab

Modelación estadística de la textura del clutter marino en Matlab

Improved Shape Parameter Estimation in K Clutter with Neural Networks and Deep Learning

CA-CFAR Adjustment Factor Correction with a priori Knowledge of the Clutter Distribution Shape Parameter

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