Topic
K-distribution
About: K-distribution is a research topic. Over the lifetime, 1281 publications have been published within this topic receiving 51774 citations.
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TL;DR: To deal with the compatibility issue of full conditional distributions of a (discrete) random vector, a graphical representation is introduced where a vertex corresponds to a configuration of the random vector and an edge connects two vertices if and only if the ratio of the probabilities of the two corresponding configurations is specified through one of the givenFull conditional distributions.
5 citations
TL;DR: In this paper, a system able to recognize the shape parameter of the K distribution, knowing a priori the value of the scale parameter, is proposed. And the result is appropriate for real-time operating conditions as it is based on a neural networks approximation in the pattern recognition role.
Abstract: The main problem faced today by sea radars is the elimination of clutter, which is undesirable contribution that appears mixed with the target information. The unwanted signal is produced by the echo caused by the rebound of the primary emission at the sea surface. One of the most popular probability distributions in clutter modeling is the K distribution. Helpful in efficient detectors design, a system able to recognize the shape parameter of the K distribution, knowing a priori the value of the scale parameter, is proposed. The result is appropriate for real time operating conditions as it’s based on a neural networks approximation in the pattern recognition role.
5 citations
01 Jan 2008
TL;DR: In this paper, some bivariate probability distributions for a discrete random variable and a continuous random variable are defined by using the Lagrangian probability distributions and the covariance between the variables of the bivariate distributions is obtained.
Abstract: In this paper, some bivariate probability distributions for a discrete random variable and a continuous random variable are defined by using the Lagrangian probability distributions. The covariance between the variables of the bivariate probability distribu- tion is obtained. From the bivariate probability distributions, we derive some mixture distributions. The moments of the mixture distributions are also discussed. Finally, we give some examples of the bivariate probability distributions and their corresponding mixture distributions.
5 citations
Journal Article•
TL;DR: For the X-band radar sea clutter with different range resolutions and polarization, the extraction algorithm of texture is given and power spectrum density of clutter is analyzed at first as discussed by the authors, and the results show that the amplitude probability density function can be fit to the generalized K distribution with log-normal texture.
Abstract: A lot of literatures indicate that the statistical characteristics of sea clutter at low grazing angle can be modeled as compound Gaussian process,which is the product of speckle with shorter coherent length and texture with longer coherent length,and is non-Gaussian,non-homogeneous,and non-stationary.In this paper,for the X-band radar sea clutter with different range resolutions and polarization,the extraction algorithm of texture is given and power spectrum density of clutter is analyzed at first.The results show that the power spectrum density of clutter can be modeled as exponential model.Secondly,the non-Gaussian of clutter is analyzed in temporal and spatial domains,and the results show that the amplitude probability density function can be fit to the generalized K distribution with log-normal texture.The non-stationarity and non-homogeneity of sea clutter is analyzed at last.
5 citations
TL;DR: Probability generating functions evaluated on finite difference operators were used systematically to derive formulas for moments of discrete distributions in this article, where they were used to derive probability generating functions for moments in discrete distributions.
Abstract: Probability generating functions evaluated on finite difference operators are used systematically to derive formulas for moments of discrete distributions.
5 citations