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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01 Jan 2013
TL;DR: Simulation shows that the method proposed can fit real clutter data of IPIX ice radar well not only on main part but also in the tail.
Abstract: Real clutter data fitting is very important for reach on clutter characteristic. In this paper, we proposed a new method to fit real clutter data of IPIX ice radar. Different distributions are used to fit different regions. KA distribution is adopt to fit the tail data part while K distribution is adopt to fit other part. Meanwhile, coordinate change is adopted to calculate tail data. Simulation shows that the method proposed in this paper can fit real clutter data of IPIX ice radar well not only on main part but also in the tail. (5 pages)
1 citations
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23 Jun 2016
TL;DR: In this paper, a near-exact distribution for positive linear combinations of independent non-central Gamma random variables is developed, which correspond to mixtures of Generalized Near-Integer Gamma distributions.
Abstract: In this paper near-exact distributions for positive linear combinations of independent non-central Gamma random variables are developed. The authors show that through an adequate factorization of the characteristic function it is possible to obtain precise near-exact distributions which correspond to mixtures of Generalized Near-Integer Gamma distributions. Numerical studies are conducted in order to assess the precision of these approximations.
1 citations
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TL;DR: In this article, selected percentiles, the terminus and the mode of the four-parameter generalized gamma distribution (g.g.d.) are tabulated and tables are also provided which display the Pearson shape parameters 3, and 3? as functions of the basic g.d. shape parameters and vice-versa.
Abstract: Selected percentiles, the terminus and the mode of the four-parameter generalized gamma distribution (g.g.d.) are tabulated. Tables are also provided which display the Pearson shape parameters 3, and 3? as functions of the basic g.g.d. shape parameters and vice-versa. Charts are provided which facilitate a comparison of the g.g.d. with distributions of the Pearson system
1 citations
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1 citations
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07 Apr 2014
TL;DR: In this article, the polarization dependent and independent scattering contributions of the sea surface were separated using complex scattering matrix better than using the NRCS, and a polarimetric generalized K distribution (Pol-K) motivated by ENL analysis had been established.
Abstract: The polarization dependent and independent scattering contributions of the sea surface could be separated using complex scattering matrix better than using the NRCS (Normalized Radar Cross Section) [1]. By achieving this separation, a polarimetric generalized K distribution (Pol-K) motivated by ENL analysis had been established. It is different from traditional speckle models which consider only the direct radar measurements and ignore the different scattering information inside pixel areas. The principle of this statistical modelling is based on the physical generation of speckle for each scattering mechanisms, i.e., polarization dependent Bragg and independent Scalar contributions. K distribution is a special case of PolK when the phase parameter α = 0. For the separated scattering contributions, K distribution describes the `tail' shape of their histograms, but overestimates the peak `height'. This disadvantage disables K distribution for oil slick detection using the separated scattering contributions. In this work, we test the capability of Pol-K distribution for oil slick detection on the RADARSAT-2 sea surface imagery, for which the peak `height' is an important property.
1 citations