K-distribution

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

Research on K Distribution in Anderson-Darling Goodness-of-Fit Test

Abstract: Identification of radar clutter model is one important step to choose optimum CFAR processor in radar signal detection, and Anderson-Darling (AD) goodness-of-fit test considered the practical and effective way under small sample size. As K distribution is a complex model providing suitable description for the amplitude of microwave sea clutter, the research on K distribution in AD test is useful but very difficult. Moment-based methods are widely used to estimate parameters of K distribution and comparison show that methods with lower moments are more accurate. Further in AD test, the influence of shape parameter on critical value (CV) for K distribution is presented. Taking it into account, tables of AD test results using varied critical value advanced in our study and fixed critical value proposed in previous research are shown based on Monte Carlo simulations.

Expected Utility from Multinomial Second-order Probability Distributions

Abstract: We consider the problem of maximizing expected utility when utilities and probabilities are given by discrete probability distributions so that expected utility is a discrete stochastic variable. As for discrete second-order distributions, that is probability distributions where the variables are themselves probabilities, the multinomial family is a reasonable choice at least if first-order probabilities are interpreted as relative frequencies. We suggest a decision rule that reflects the uncertainty present in distribution-based probabilities and utilities and we show an example of this rule in action with multinomial second-order distributions. Index Terms—Imprecise probability. second-order probability, discrete probability distributions, multinomial distributions, expected utilty.

Modified Regression Approach for Modeling Probability Distributions

Abstract: Amodified regression approach is presented for modeling probability distributions. The approach combines a fitted probability distribution with polynomial regression. The method is based on fitting a reference probability distribution to a set of data. Polynomial regression is then used to modify the fitted reference distribution for a closer fit to the data. The modified regression approach is a fast and statistically efficient method of modeling probability distributions for random variable generation and percentile estimation.

A New Fractal Model for HF Sea-clutter

Abstract: The model for radar sea clutter using fractal theory has potential application in many research fields.A new time domain fractal model for the sea clutter that based on the multifractal characteristics of HF sea clutter has been introduced.By using probability density function comparison analysis among 4 most commonly used distributions such as Weibull,Log Normal,Rayleigh and K distribution,and through a statistical test by modified Kolmogorov Smirnov(K-S),the conclusion of HF radar sea clutter bearing the apriori statistical characteristics has been obtained.This conclusion proves that the new time domain fractal model for HF radar sea clutter is quite reasonable and applicable in the research of target detection under HF sea-clutter background.