A research study of environmental factors for the gamma distribution

Environmental factor estimation for the gamma distribution with two parameters is considered here. In the consideration of the characteristics of the gamma distribution, the definition of environmental factors for the exponential distribution and the convenience of data conversion, the definition of environmental factors for the gamma distribution is proposed. In accordance with this definition, the lower and upper confidence limits of environmental factors are derived by Classical, Bayesian and Fiducial methods. Some approximate confidence limits are also given. A practical example is provided and the results indicate that the definition, the confidence limits and their approximants are suitable.     

Bayes prediction results for the inverse gaussian distribution utilizing guess values of parameters

The paper deals with the problem of predicting, on the basis of an observed sample from an inverse Gaussian distribution, a single future observation; as well as the mean of a set of future observations from the same distribution when guess values of the parameters are available. Using the general class of a prior which places a constant weight on the guess values, the paper derives the predictive p.d.f. and hence the prediction limits. The behaviour of the proposed limits as compared with the Padgett approximate classical limits [W. J. Padgett, J. Statist. Comput. Simul.14, 291–299 (1982)] is studied using an example. The proposed limits are found to be better than the classical limits.     

复合高斯杂波中知识辅助的自适应检测算法

研究了在复合高斯杂波中利用先验知识自适应检测目标的问题,证明了基于双参数逆高斯分布纹理的复合高斯模型比传统的K分布、复t分布和单参数逆高斯分布纹理的复合高斯分布模型能够更好地拟合实际杂波。文中选择双参数逆高斯分布作为纹理分量的先验分布、基于广义似然比准则和贝叶斯方法设计得到了一种复合高斯杂波中的自适应检测器。理论分析和数值仿真表明,与自适应匹配滤波器和正则化自适应匹配滤波器相比,该检测器具有更好的检测性能     

基于正态逆高斯模型的非下采样Contourlet变换图像去噪

提出一种基于正态逆高斯先验模型的非下采样Contourlet变换图像去噪算法.在非下采样Contourlet变换域中,以正态逆高斯模型为先验模型,对图像分解系数的稀疏分布统计建模,估计每个子带内的模型参数,在贝叶斯最大后验概率估计准则下推导出与正态逆高斯模型相应的阈值函数表达式,以此对图像进行去噪处理.对于被加性高斯白...     

Generation of anisotropic-smoothness regularization filters for EIT

In the inverse conductivity problem, as in any ill-posed inverse problem, regularization techniques are necessary in order to stabilize inversion. A common way to implement regularization in electrical impedance tomography is to use Tikhonov regularization. The inverse problem is formulated as a minimization of two terms: the mismatch of the measurements against the model, and the regularization functional. Most commonly, differential operators are used as regularization functionals, leading to smooth solutions. Whenever the imaged region presents discontinuities in the conductivity distribution, such as interorgan boundaries, the smoothness prior is not consistent with the actual situation. In these cases, the reconstruction is enhanced by relaxing the smoothness constraints in the direction normal to the discontinuity. In this paper, we derive a method for generating Gaussian anisotropic regularization filters. The filters are generated on the basis of the prior structural information, allowing a better reconstruction of conductivity profiles matching these priors. When incorporating prior information into a reconstruction algorithm, the risk is of biasing the inverse solutions toward the assumed distributions. Simulations show that, with a careful selection of the regularization parameters, the reconstruction algorithm is still able to detect conductivities patterns that violate the prior information. A generalized singular-value decomposition analysis of the effects of the anisotropic filters on regularization is presented in the last sections of the paper.     

基于GPD的稳健贝叶斯压缩感知ISAR成像方法

现有贝叶斯压缩感知(Bayesian Compressed Sensing,BCS)-逆合成孔径雷达(Inverse Synthetic Aperture Radar,ISAR)成像算法中先验分布模型不能很好地满足可压缩性,导致成像精度随脉冲数目的减小、高斯噪声的增强而急剧下降。为此,提出了一种基于广义Pareto分布改进BCS成像方法(Improving BCS imaging based on GPD,IGPCS)。该方法主要在BCS框架下利用广义Pareto先验分布替代传统的广义Gaussian先验分布,以增强模拟信号的稀疏先验和可压缩性。进一步地,为了克服后验概率模型计算困难等问题,采用最大后验(Maximum A Posteriori,MAP)方法对超参数进行估计。通过对Mig-25小型飞机的ISAR模拟实验表明,与传统方法相比,IGPCS方法能够获取极高的成像精度,并且对低脉冲数、强高斯噪声环境具有较强的鲁棒性。     

Assessing the inverse Gaussian distribution assumption

Two easily applied goodness-of-fit tests for the inverse Gaussian distribution are discussed. One of these tests is the familiar Kolmogorov-Smirnov one-sample test that is applied when the form of a probability distribution is completely specified. When the parameters of the distribution are unknown, as is more typical, the Kolmogorov-Smirnov test cannot be directly applied. In this instance, a transformation that uses a distributional result relating the Student-t distribution to the inverse Gaussian distribution allows the Lilliefors test of normality to be adapted to test the inverse Gaussian distribution assumption     

非高斯杂波下知识辅助检测的认知方法

先验信息的使用是提高雷达目标检测性能的有效途径之一,然而先验信息与当前探测环境的失配会严重影响到检测器的性能.本文考虑逆伽马分布纹理、复合高斯杂波下的知识辅助检测算法,推导了先验模型失配条件下(逆伽马分布参数失配)检测器的虚警率和Swerling I型目标的检测概率计算公式,获得了检测性能与模型参数失配之间的量化关系.利用两组不同参数的知识辅助检测器对当前杂波环境进行探测,通过评估检测器的性能,实现了当前杂波环境模型参数的估计.计算机仿真和实测数据的分析结果表明,采用认知方法的知识辅助检测器较常规检测器而言,能够获得更好的检测性能.     

Inverse gaussian distribution and its application to reliability

This paper discusses the problem of estimating parameters for the inverse Gaussian distribution when early failures dominate the problem. Since the Weibull distribution is a well-known competitor of the inverse Gaussian distribution, the estimate for the reliability function can be obtained by using the Weibull distribution. Simulation studies are performed to compare reliability by using both inverse Gaussian and Weibull distributions. Various implications of these results are discussed.     

The first passage time distribution of brownian motion and its applications

This paper traces the development of the first passage time distribution of Brownian motion (inverse Gaussian) together with its various applications in inventory problems, usage and storage times, detection theory, labor turnover, money supply, purchasing models, hospital inpatient stay times, strikes duration, biology, … etc. Besides, since the inverse Gaussian distribution arises as the distribution of first passage time of Brownian motion, its applicability to lifetime modeling is a natural consequence. The general characteristics of the inverse Gaussian model which make it attractive for various applications are also identified.     

基于贝叶斯的高斯杂波背景下MIMO雷达自适应检测算法

对于集中式多输入多输出(Multiple-Input Multiple-Output, MIMO)雷达,该文研究了高斯杂波背景下的目标检测问题。该文假设杂波的协方差矩阵是未知随机的,且服从逆复Wishart分布,基于贝叶斯方法和广义似然比检验准则设计了两种新型自适应检测器。该文提出的贝叶斯检测器具有两个显著的优点:(1)不需要训练数据;(2)杂波的先验知识体现在设计方案中,从而提高了检测性能。仿真结果显示该文提出的贝叶斯检测器的检测性能优于目前常用的非贝叶斯检测器,特别是在发射波形采样数较少时。另外,该贝叶斯检测器在参数失配条件下的性能会有一定程度下降。      

非高斯杂波中知识辅助的信号检测算法

杂波的非高斯性会严重影响到常规信号检测算法的性能,提高非高斯杂波中信号检测性能是雷达信号检测领域的一个研究重点。本文考虑了一种在非高斯杂波中,利用先验信息进行信号检测的方法。假定杂波统计特性满足复合高斯模型,即可以表示为散斑分量与纹理分量的乘积。选择逆伽马分布作为纹理分量的先验分布,基于贝叶斯方法,给出了一种知识辅助的信号检测算法。计算机仿真结果表明,该检测算法的检测性能优于常规的自适应检测算法。进一步,本文采用McMaster大学的IPIX雷达海杂波数据作为研究对象,利用最大似然估计获得杂波非高斯性的先验信息,分析了该算法在实测数据中的检测性能。分析结果表明,在不同的雷达分辨率海杂波中,该算法也具有较好的检测性能。      

非高斯杂波下知识辅助检测器敏感性分析

先验信息的使用可以提高知识辅助检测器的探测性能,若先验信息与当前探测环境不匹配,检测器性能可能会受到影响。该文考虑一种复合高斯杂波下的知识辅助检测器,其采用逆伽马分布作为纹理分量先验分布,分析该检测器在不同杂波纹理分量模型参数条件下的检测性能。首先给出了先验模型参数失配条件下,虚警概率和Swerling I型目标检测概率的计算方法。然后在给定先验模型参数条件下,分析了杂波纹理分量分布参数对检测器性能的影响。理论分析表明,若杂波纹理分量分布参数位于某个区域以内时,检测器可以获得比模型匹配时更好的检测性能,计算机仿真验证了上述结论。     

高斯光束通过位相板后的变化

对基模和高阶高斯光束通过位相板后的变化作了详细的计算分析.研究表明,位相板可以改变光场分布和用功率含量百分比定义的Me2 因子,甚至使Me22.     

Bayes computation for reliability estimation

Bayes estimation of complicated functions requires simpler estimation techniques due to the mathematical difficulties involved in the classical Bayes approach. Bayes estimation enjoys many approximation techniques and computational methods like Metropolis, and Gibbs sampler. Bayes estimation of the reliability of a mixture inverse Gaussian distribution requires a numerical approach since the calculations are immensely difficult from the exact Bayes point of view. Lack of full conditional prior distributions for all 3 parameters of this particular case makes the use of Gibbs sampler inefficient. Application of the rejection method, however, is reasonable since it is very simple to implement without any constraints on the prior distributions or on the hyper-parameters     

逆伽马纹理复合高斯杂波参数的贝叶斯估计方法

复合高斯杂波(CGC)在拟合高分辨力、低掠射角海杂波中已得到广泛应用,带有逆伽马纹理的CGC的强度分布为广义帕累托(GP)分布。在实际雷达工作场景中,由于观测区域内海杂波的非平稳非均匀特性,导致独立同分布杂波样本的获取十分困难。提出一种广义帕累托分布参数的贝叶斯估计方法,通过在线更新数据的先验信息,获取小样本情形下GP分布参数。仿真实验证明,该方法能够在样本数量较小的情况下,对GP数据实现较为精确的参数估计。     

环境因子的定义及研究现状

从普遍接受的三个基本假设出发，讨论环境因子的定义和失效机理不变的约束条件，重点综述环境因子的研究现状和常用方法，并探讨引入反应论模型解决环境因子预测问题的可能途径。     

基于非抽样复轮廓波变换的图像去噪算法研究

将二维双树复小波变换(DT-CWT)与非抽样方向滤波器组(NSDFB)相结合,构造一种新的非抽样复轮廓波变换(NSCCT),并对其平移不变性作了相应证明。同时利用对称的正态逆高斯(NIG)分布先验概率模型和贝叶斯最小均方算法,提出一种基于NSCCT的图像去噪算法。实验结果表明,本文构造的NSCCT能够有效地抑制伪Gibbs现象,并且具有更丰富的方向分量,因而在图像的细节和纹理表现方面有一定的优势。     

Computationally efficient variational Bayesian method for PAPR reduction in multiuser MIMO‐OFDM systems

This paper investigates the use of the inverse‐free sparse Bayesian learning (SBL) approach for peak‐to‐average power ratio (PAPR) reduction in orthogonal frequency‐division multiplexing (OFDM)‐based multiuser massive multiple‐input multiple‐output (MIMO) systems. The Bayesian inference method employs a truncated Gaussian mixture prior for the sought‐after low‐PAPR signal. To learn the prior signal, associated hyperparameters and underlying statistical parameters, we use the variational expectation‐maximization (EM) iterative algorithm. The matrix inversion involved in the expectation step (E‐step) is averted by invoking a relaxed evidence lower bound (relaxed‐ELBO). The resulting inverse‐free SBL algorithm has a much lower complexity than the standard SBL algorithm. Numerical experiments confirm the substantial improvement over existing methods in terms of PAPR reduction for different MIMO configurations.     

Accurate contour control of mechatronic servo systems usingGaussian networks

This paper presents a method of contour control of mechatronic servo systems by using neural networks. The neural network learns the inverse dynamics of the mechatronic servo system. The input data for the mechatronic servo systems are modified from objective trajectories by using the neural network. The Gaussian network is adopted to construct the inverse dynamics of the mechatronic servo system because the Gaussian function is well defined, and its structure and initial parameters can be systematically selected such that the initial network approximates the inverse dynamics of the mechatronic servo system. The actual input/output data of the mechatronic servo system are used for the learning of the Gaussian network. Effectiveness of the proposed method is assured by experimental results of contour control of an X-Y table