K-distribution

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

Using Multilook Averaging for Coherently Modeled Scattering From a Pine Tree at L-Band

Abstract: In this letter, we study scattering from a scots pine tree by using a realistic cylinder model of the tree and the coherent electric field scattering model. We study the directional variability of bistatic and monostatic scattering at L-band and show that, due to large variations in results, some averaging technique should be used to describe and interpret the model output efficiently. We propose that the needed averaging can be done by multilooking and that multilook data could be easily generated by rotating the tree model randomly around its vertical axis. We show that the resulting scattering data obey generally the multidimensional Gaussian distribution or the more general K-distribution, in a way similar to synthetic aperture radar (SAR) image pixels, and can therefore be represented by a single averaged covariance matrix. The trunk-ground reflection to the backscattering direction adds to the model output non-Gaussian behavior, which can be treated as texture. Covariance matrix formalism allows us to use descriptors which are commonly used to analyze SAR images, like target entropy and alpha angle. The method helps interpretation and comparison between the model output and SAR image.

Tri-fractile parameter estimation method of K distribution based sea clutter amplitude model

Abstract: The invention discloses a t ri-fractile parameter estimation method of a K distribution based sea clutter amplitude model, and mainly solves the problem that a present parameter estimation method of the K distribution sea clutter amplitude model is low in estimation performance when abnormal samples are included. Two fractiles ralpha and rbeta are selected according to a functional relation related fractile position parameters, the ratio of one fractile to the other is calculated, and the ratio and a reverse shape parameter form a table in one to one correspondence; actually measured radar data is obtained, the two fractiles ralpha and rbeta are read from the data, and an estimated value of reverse shape parameter is obtained by searching the table; and a scale parameter estimated value isobtained by calculation and searching on the basis of the estimated value of the reverse shape parameter and a K distribution cumulative probability distribution function. Part of the sea clutter amplitude characteristics is used, three fractiles are searched and calculated, the estimation performance of the parameter estimation method of the K distribution sea clutter amplitude model is improvedwhen abnormal samples are included, and the method can be used for sea condition exploration or target detection.

Exact distributions of order statistics from ln,p-symmetric sample distributions

Abstract: We derive the exact distributions of order statistics from a finite number of, in general, dependent random variables following a joint ln,p-symmetric distribution. To this end,we first review the special cases of order statistics fromspherical aswell as from p-generalized Gaussian sample distributions from the literature. To study the case of general ln,p-dependence, we use both single-out and cone decompositions of the events in the sample space that correspond to the cumulative distribution function of the kth order statistic if they are measured by the ln,p-symmetric probability measure.We show that in each case distributions of the order statistics from ln,p-symmetric sample distribution can be represented as mixtures of skewed ln−ν,p-symmetric distributions, ν ∈ {1, . . . , n − 1}.

A Two-Dimensional Continuum of a Priori Probability Distributions on Constituents

Abstract: Hintikka has defined a one-dimensional continuum of a priori probability distributions on constituents and has built on it a two-dimensional continuum of inductive methods with the aid of Carnap’s λ-continuum ([1]), which plays also a fundamental role in his continuum of a priori distributions, and the formula of Bayes ([2]). Here a two-dimensional continuum of a priori probability distributions will be introduced. On its base a three-dimensional continuum of inductive methods can be constructed in the same way as Hintikka has done. The importance of the new continuum of a priori distributions, which is also based on Carnap’s λ-continuum but in a completely different way, is that it leaves room for almost all kinds of a priori considerations, whereas Hintikka’s continuum admits only considerations that lead to increasing probability for the constituents by increasing size.