特征函数法在测量不确定度评定中的应用

IF 0.1 Q4 INSTRUMENTS & INSTRUMENTATION
V. Witkovský
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引用次数: 0

摘要

适用于建模测量并确定其不确定性的概率分布通常基于GUM中描述的标准近似方法,即GUM不确定性框架(GUF),使用不确定性传播定律(也称为delta方法)或基于使用蒙特卡罗方法(MCM)计算的概率传播定律的更准确的方法。作为GUF和MCM的替代方案,我们提出了一种特征函数方法(CFA),该方法适用于通过反转相关特征函数(CF)来使用线性测量模型中测量量的精确概率分布来确定测量不确定性,该特征函数被定义为概率密度函数(PDF)的傅立叶变换。在本文中,我们介绍了特征函数方法(工具箱CharFunTool)的MATLAB实现现状,并通过一个简单的例子说明了CFA在确定分布和不确定度评估中的使用和适用性。将所提出的方法与GUM、MCM和峰度不确定度方法(KUM)进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of the characteristic functions approach to measurement uncertainty evaluation
Probability distributions suitable for modelling measurements and determining their uncertainties are usually based on a standard approximation approach as described in GUM, i.e. the GUM uncertainty framework (GUF), using the law of uncertainty propagation (also known as the delta method) or a more accurate method based on the law of probability propagation calculated using the Monte Carlo method (MCM). As an alternative to GUF and MCM, we present a characteristic function approach (CFA), which is suitable for determining measurement uncertainties by using the exact probability distribution of a measured quantity in linear measurement models by inverting the associated characteristic function (CF), which is defined as a Fourier transform of the probability density function (PDF). In this paper, we present the current state of the MATLAB implementation of the characteristic function approach (the toolbox CharFunTool) and illustrate the use and applicability of the CFA for determining the distribution and uncertainty evaluation with a simple example. The proposed approach is compared with GUM, MCM and the kurtosis uncertainty method (KUM).
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来源期刊
Ukrainian Metrological Journal
Ukrainian Metrological Journal INSTRUMENTS & INSTRUMENTATION-
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