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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Patent•
29 Aug 2011
TL;DR: In this article, robust methods are developed to provide bounds and probability distributions for the locations of objects as well as for associated variables that affect the accuracy of the location such as the positions of stations, the measurements, and errors in the speed of signal propagation.
Abstract: Robust methods are developed to provide bounds and probability distributions for the locations of objects as well as for associated variables that affect the accuracy of the location such as the positions of stations, the measurements, and errors in the speed of signal propagation. Realistic prior probability distributions of pertinent variables are permitted for the locations of stations, the speed of signal propagation, and errors in measurements. Bounds and probability distributions can be obtained without making any assumption of linearity. The sequential methods used for location are applicable in other applications in which a function of the probability distribution is desired for variables that are related to measurements.
1 citations
Dissertation•
01 Jan 2000
1 citations
01 Jan 2014
TL;DR: In this paper, statistical models introduce probability distributions to describe the variation due to noise, and thereby achieve quantitative expressions of knowledge about the signal, a process we will describe more fully in Chapters 7 and 10.
Abstract: In Chapter 1 we said that a measurement is determined in part by a “signal” of interest, and in part by unknown factors we may call “noise.” Statistical models introduce probability distributions to describe the variation due to noise, and thereby achieve quantitative expressions of knowledge about the signal—a process we will describe more fully in Chapters 7 and 10.
1 citations
15 Jul 2009
TL;DR: A fuzzy neural approach for estimating the parameters of the K distribution is proposed and is used for the training process, obtaining improved results compared with those obtained by the method based on higher order and fractional moments for small sample sizes.
Abstract: Recently, artificial neural network have been used in radar signal detection. In this paper, a fuzzy neural approach for estimating the parameters of the K distribution is proposed. We use the back propagation (BP) algorithm for the training process. We obtain improved results compared with those obtained by the method based on higher order and fractional moments for small sample sizes.
1 citations
Journal Article•
TL;DR: Compared to the traditional maximum likelihood estimator, the log-cumulant estimator of K distribution with analytical expressions was easy to compute and achieves high estimation accuracy.
Abstract: In order to efficiently estimate the parameters of K distribution,this paper proposed the log-cumulant estimator.Based on second-kind statistics,first it derived the second-kind first characteristic function of K distribution by taking the Mellin transformation to the probability density function of K distribution,second obtained the second-kind second cha-racteristic function of K distribution from the logarithmic transformation of the second-kind first characteristic function,and last deduced the first two log-cumulants to estimate the parameters of K distribution by taking the derivative of the second-kind second characteristic function.Compared to the traditional maximum likelihood estimator,the log-cumulant estimator of K distribution with analytical expressions was easy to compute.Monte Carlo simulations demonstrate that the log-cumulant estimator of K distribution based on second-kind statistics achieves high estimation accuracy.
1 citations