K-distribution

About: K-distribution is a research topic. Over the lifetime, 1281 publications have been published within this topic receiving 51774 citations.

On the relation between matrix-geometric and discrete phase-type distributions

Abstract: A discrete phase-type distribution describes the time until absorption in a discrete-time Markov chain with a finite number of transient states and one absorbing state. The density f(n) of a discrete phase-type distribution can be expressed by the initial probability vector , the transition probability matrix T of the transient states of the Markov chain and the vector t containing the probabilities of entering the absorbing state from the transient states: f(n) = Tn−1t, n 2 N. If we take a probability density of the same form, but not necessarily require , T and t to have the probabilistic Markov-chain interpretation, we obtain the density of a matrix-geometric distribution. Matrix-geometric distributions can equivalently be defined as distributions on the non-negative integers that have a rational probability generating function. In this thesis it is shown that the class of matrix-geometric distributions is strictly larger than the class of discrete phase-type distributions. We give an example of a set of matrix-geometric distributions that are not of discrete phasetype. We also show that there is a possible order reduction when representing a discrete phase-type distribution as a matrix-geometric distribution. The results parallel the continuous case, where the class of matrix-exponential distributions is strictly larger than the class of continuous phase-type distributions, and where there is also a possible order reduction.

Modeling high-resolution synthetic aperture radar images with heavy-tailed distributions

Abstract: Statistical distributions of synthetic aperture radar （SAR） images based on central limit theorem cannot reflect the statistical characteristics of sharp peak and heavy tail of high-resolution SAR images. By using the generalized central limit theorem, the heavy-tailed distributions （heavy-tailed Rayleigh distribution for amplitude image and heavy-tailed exponential distribution for intensity image） are obtained from the symmetric stable distributions of real and imaginary parts of echoes. Taking the heavy-tailed Rayleigh distribution as an example, the algebraic tails of heavy-tailed distributions are explained as well as the statistical properties of sharp peak and heavy tail. In order to model the high-resolution SAR images with the heavy-tailed distributions, based on second-kind statistical, Characteristics the log-cumulant estimator is proposed to efficiently estimate the parameters of the heavy-tailed distributions. Modeling experiments on real SAR images demonstrate that the heavy-tailed distributions based on the generalized central limit theorem can accurately describe the sharp-peaked and heavy-tailed statistical characteristics of high-resolution SAR images.

Novel mixture model for synthetic aperture radar imagery

Abstract: We propose a novel mixture model that combines two special cases of heavy-tailed Rayleigh distribution. These two special families possess the only analytical forms of heavy-tailed Rayleigh distribution. As a consequence, the mixture model has an analytical form. Because heavy-tailed Rayleigh distribution is a member of spherically invariant random process, one can obtain the parameter estimation by the method-of-moments technique. Finally, the mixture model has been tested on various synthetic aperture radar images, and the performance of this model is strong compared with other models such as K distribution, G0 distribution, and heavy-tailed Rayleigh models.