基于FFT的湿度传感器测量不确定度评定方法

目前气象计量领域测量不确定度评定一般采用GUM(Guide to the expression of Uncertainty in Measurement,测量不确定度表示指南)方法,该方法存在置信因子近似估计的问题。为进一步提升测量不确定度评定结果的可靠性,提出了一种基于FFT的卷积评定方法。利用FFT算法得到多个不确定度分量的合成分布,并根据指定置信水平准确求出合成分布的置信因子,进而确定扩展不确定度。利用该方法对一款湿度传感器测量不确定度进行了评定,并与GUM方法评定结果进行对比分析。结果表明,该方法计算得到湿度传感器的扩展不确定度为1.54%RH,比GUM方法评定结果（1.68%RH）更加精确,且能直观显示合成分布情况,有助于提高相关业务人员对测量不确定度的理解水平。     

Monte—Carlo法在测量不确定度评定中的应用

测量不确定度是与结果相关联的参数，表征合理赋予的被测量之值的分散性。在测量过程中存在许多可能引起测量结果的不确定度分量，这些分量一般应为服从各种分布的随机变量，当随机变量出现非正态分布时，用常规方法求解会产生较大偏差。本文介绍的Monte-Carlo法是利用计算机模拟生成服从各种分布规律的随机数并行统计分析处理，可合理解决测量不确定度评定过程中较为广泛的问题。     

不确定度理论在虚拟仪器测量系统中的应用

阐述了虚拟仪器系统不确定度评定的方法,以基于虚拟仪器的光辐射测试系统为例,分析了该系统的不确定度来源,应用GUM的不确定度传播规则及不确定度A类、B类评定的方法实现了对光辐射测试系统的不确定度的评定,最后给出了系统的测量结果、误差及不确定度.经评定系统精度高,误差小,该系统不仅可作为检测器,也可作为标准器进行量值传递.此测量不确定度评定方法可解决评估虚拟仪器的测量不确定度的问题.     

航迹关联不确定度的表示

本文给出了航迹关联不确定度的定义,解释了航迹关联不确定度与信息不确定性的香农熵度量和哈特利度量之间的关系,简述了航迹关联不确定度在信息融合过程中的作用,提出了航迹关联不确定度评定方法应满足的五个基本要求,给出了一种航迹关联不确定度评定方法.仿真结果表明,航迹关联不确定性是能度量的,所提出的评定方法能够正确反映目标间隔距...     

基于拟蒙特卡洛方法的动态测量不确定度评定

针对蒙特卡洛方法收敛速度较慢以及仿真结果不稳定的问题,本文从动态测量系统的特性出发,通过引进低偏差点集,产生空间分布较为均匀的拟随机数序列,代替蒙特卡洛方法中的伪随机数序列,提出一种基于拟蒙特卡洛方法的动态测量不确定度评定方法.实验结果表明,该方法与传统基于蒙特卡洛方法的动态测量不确定度评定方法相比,不但大幅度减少了抽...     

电流互感器比值差、相位差测量结果的不确定度评定

一切测量结果均不可避免地具有测量不确定度.本文以一个实例详细介绍了测量不确定度在电流互感器比值差和相位差测量的应用,为电力行业检定或校准电流互感器提供了不确定度的评定方法.     

基于Matlab的测量不确定度灰色评定

测量不确定评定是实验、测试活动中重要的环节,其评定方法及评估模型的选择尤为重要。根据测量不确定度导则,建立了基于matlab GM(1,1)测量不确定度评估模型,并采用Matlab\VC++混合编程的方式进行程序化处理。以金属材料里氏硬度不确定度评定为例进行分析计算,结果表明灰色理论模型适用于里氏硬度标准不确定度评定,该模型的建立为小样本、贫数据量的测量活动提供新的不确定度评定方法。     

抖晃仪校准装置的测量不确定度的评定

一切测量结果都不可避免地具有不确定度.本文依据JJF1059-1999<测量不确定度评定与表示>,对抖晃仪校准装置的测量不确定度进行评定.     

NCIE在多特征选择及SAR目标识别中的应用

针对合成孔径雷达(SAR)图像目标识别问题,采用非线性相关信息熵(NCIE)进行多特征选取进而实现分类。基于混合高斯模型对SAR图像提取的各类特征进行概率建模,采用KL散度评价不同特征之间的相似度。采用非线性相关信息熵评价不同特征组合的相关性,根据最大熵值确定最优特征组合。对于选取的多类特征,基于联合稀疏表示模型进行表征和分类。利用MSTAR数据集对提出方法在标准操作条件和扩展操作条件下进行测试,结果验证了其有效性。     

F45型数字多用表直流电压的测量不确定度评定

介绍了测量不确定度的定义、模型建立及评定方法。以FLUKE公司的5500型多功能源作为校准源,选取多个测量点,对F45型数字多用表的直流电压进行测量不确定度计算与评定。     

基于最大熵准则的多视角SAR目标分类方法

针对合成孔径雷达（Synthetic aperture radar,SAR）目标分类问题,提出基于最大熵准则的多视角方法。采用经典的图像相似度测度构建不同视角SAR图像之间的相关性矩阵,在此基础上分别计算不同视角组合条件下的非线性相关信息熵值。非线性相关信息熵值可分析多个变量之间的统计特性,熵值的大小即可反映不同变量之间的内在关联。根据最大熵的原则选择最优的视角子集,其中SAR图像具有最大的内在相关性。分类过程以联合稀疏表示为基础,对具有最大熵值的多个视角进行联合表示。联合稀疏表示模型同时处理若干稀疏表示问题,在它们具有关联的条件下具有提升重构精度的优势。根据不同视角求解得到的表示系数,按照类别分别计算对于选取多视角的重构误差,并根据误差最小的准则进行最终决策。文中方法可有效对多视角SAR图像样本进行相关性分析,并利用联合稀疏表示利用这种相关性,能够更好提高分类精度。采用MSTAR数据集对方法进行分析测试,通过与几类其他方法在多种测试条件下进行对比,结果显示了最大熵准则在多视角选取中的有效性和文中方法对SAR目标分类性能的优越性。     

Entropy methods for joint distributions in decision analysis

A fundamental step in decision analysis is the elicitation of the decision maker's information about the uncertainties of the decision situation in the form of a joint probability distribution. This paper presents a method based on the maximum entropy principle to obtain a joint probability distribution using lower order joint probability assessments. The approach reduces the number of assessments significantly and also reduces the number of conditioning variables in these assessments. We discuss the order of the approximation provided by the maximum entropy distribution with each lower order assessment using a Monte Carlo simulation and discuss the implications of using the maximum entropy distribution in Bayesian inference. We present an application to a practical decision situation faced by a semiconductor testing company in the Silicon Valley.     

矢量网络分析仪测量不确定度评估算法

虽然目前校准算法已趋成熟,但由于随机误差和剩余系统误差等的存在,矢量网络分析仪(Vector network analyzer,VNA)的测量结果不可避免地会有一定的不准确性.而现有的矢网测量不确定度评估算法中往往只考虑剩余系统误差,忽略了系统线性性能、线缆状态以及测试环境等因素.本文同时考虑了校准后剩余系统误差、系统非线性误差,随机误差及测量环境等因素,建立了整机测量不确定度模型,实现了矢量网络分析仪整机测量不确定度的评估.与现有矢网不确定度评估算法相比,本算法考虑的误差因素更为全面,评估结果更为可靠.利用误差上限传递思想推导出商用VNA散射参数测量不确定度评估公式,并提供了相应的参数获取方案.应用该算法对安捷伦8753ES矢量网络分析仪进行测量不确定度评估,并与安捷伦提供的技术数据进行对比,其结果数据吻合良好.     

浅地层探地雷达自动目标检测与定位研究

该文提出了一种基于图像熵变化及窗口能量检测的探地雷达自动目标检测与定位方法,该方法首先通过探地雷达未经合成孔径处理的图像与经合成孔径处理后的图像之间的熵变化来检测目标,再通过在合成孔径图像中进行窗口能量检测来判断目标所在的位置。通过对实测数据的处理,结果表明该方法取得了较好的效果。     

Joint Monitoring of Ground and Sky for Cereal Crops Based on Scatterometer Measurement and ASAR Images

The joint monitoring of the ground and sky for cereal crops based on microwave data has become a popular method for researches on earth surface objects. Focused on the sensitivity of backscatter from the scatterometer measurement and advanced synthetic aperture radar (ASAR) images to cereal parameters of rice, nine acquisitions, including rice parameters related eco-physiological variables and scattering coefficients, have been carried over the paddy field corresponding to rice growth stages. This paper analyzes the relationship between the corresponding backscatter to the cereal parameters based on the measurement at the interesting bands, polarizations, and incidence angels. Further, a modified water cloud model is built based on the ground measurement and advanced integrated equation model (AIEM), and then cereal parameters from ASAR images are retrieved and verified. The research results show that the sensitivity of backscatter to cereals from the sensor of the radar scatterometer could be helpful to build the retrieve model for synthetic aperture radar (SAR) images, which can achieve the scientific goals of the joint monitoring of ground and sky for cereal crops.     

基于随机调频步进信号的高分辨ISAR成像方法

为充分利用随机调频步进逆合成孔径雷达回波所具有的联合稀疏特征,提高成像性能,该文提出一种基于分布式压缩感知理论的随机调频步进逆合成孔径雷达高分辨成像方法。首先构建随机调频步进信号回波的联合稀疏表示模型,并完成子脉冲的脉冲压缩处理;其次,基于每组子脉冲的随机方式(组与组之间的随机方式不同),构建相应的随机量测矩阵,获取回波的压缩感知信号模型,并利用分布式压缩感知理论实现距离向联合高分辨重构;最后结合回波在方位向的稀疏性,采用快速稀疏重构算法实现方位向高分辨成像。理论分析和仿真结果表明由于充分利用了随机调频步进信号回波的随机性与联合稀疏特征,所提出方法具有重构精度高、距离向采样率低、抗噪性能强等特点。     

A tight upper bound on discrete entropy

The standard upper bound on discrete entropy was derived based on the differential entropy bound for continuous random variables. A tighter discrete entropy bound is derived using the transformation formula of Jacobi theta function. The new bound is applicable only when the probability mass function of the discrete random variable satisfies certain conditions. Its application to the class of binomial random variables is presented as an example     

A Timing-Aware Probabilistic Model for Single-Event-Upset Analysis

With device size shrinking and fast rising frequency ranges, the effect of cosmic radiations and alpha particles known as single-event upset (SEU) and single-event transients (SET), is a growing concern in logic circuits. Accurate understanding and estimation of SEU sensitivities of individual nodes is necessary to achieve better soft error hardening techniques at logic level design abstraction. We propose a probabilistic framework to the study the effect of inputs, circuits structure, and gate delays on SEU sensitivities of nodes in logic circuits as a single joint probability distribution function (pdf). To model the effect of timing, we consider signals at their possible arrival times as the random variables of interest. The underlying joint probability distribution function, consists of two components: ideal random variables without the effect of SEU and the random variables affected by the SEU. We use a Bayesian network to represent the joint pdf which is a minimal compact directional graph for efficient probabilistic modeling of uncertainty. The attractive feature of this model is that not only does it use the conditional independence to arrive at a sparse structure, but it also utilizes the same for smart probabilistic inference. We show that results with exact (exponential complexity) and approximate nonsimulative stimulus-free inference (linear in number of nodes and samples) on benchmark circuits yield accurate estimates in reasonably small computation time     

Bayesian Separation of Images Modeled With MRFs Using MCMC

We investigate the source separation problem of random fields within a Bayesian framework. The Bayesian formulation enables the incorporation of prior image models in the estimation of sources. Due to the intractability of the analytical solution, we resort to numerical methods for the joint maximization of the a posteriori distribution of the unknown variables and parameters. We construct the prior densities of pixels using Markov random fields based on a statistical model of the gradient image, and we use a fully Bayesian method with modified-Gibbs sampling. We contrast our work to approximate Bayesian solutions such as iterated conditional modes (ICM) and to non-Bayesian solutions of ICA variety. The performance of the method is tested on synthetic mixtures of texture images and astrophysical images under various noise scenarios. The proposed method is shown to outperform significantly both its approximate Bayesian and non-Bayesian competitors.