逐次截尾样本下电子元件混联系统可靠性指标的EB估计

在逐次截尾样本下,研究电子元件混联系统可靠性指标的估计问题。将Bayes方法和极大似然法相结合,在平方损失下,获得部件失效率、系统可靠度和平均寿命的经验Bayes估计。最后给出随机模拟例子,说明该方法的正确性。结果表明可靠性指标的经验Bayes估计值精度较高。     

累积损伤下Lomax分布产品序进应力加速试验分析

首先,给出了在累积损伤模型下,Lomax分布产品分别在序进应力加速寿命试验和多组序进应力加速寿命试验下的失效模式;然后,给出了参数的极大似然估计和基于渐进正态性的近似区间估计;最后,利用Monte Carlo法模拟数据,通过牛顿迭代法求出了不同情况下参数的极大似然估计和近似区间估计.     

累积损伤模型下Lomax分布产品序进-恒定加速试验分析

首先,给出了在累积损伤模型下服从,Lomax分布的产品在序进——恒定应力加速寿命试验下的失效模式;然后,讨论了参数的极大似然估计和基于渐进正态的近似区间估计;最后,利用Monte Carlo法模拟数据,通过牛顿迭代法解方程组,求出了参数的极大似然估计,并通过计算fisher信息阵求得了参数的近似区间估计.     

半导体器件并联系统可靠性评估的经验Bayes方法

为了提高机载开关电源中半导体器件并联系统可靠性评估的准确性,运用经验 Bayes 法和经典的统计方法,研究了该系统的可靠性评估问题。分别给出了系统可靠性指标的经验 Bayes 估计,极大似然估计。利用 Monte-Carlo方法,对两种估计结果进行了比较,结果表明,经验 Bayes 估计的最大绝对误差为 0.07,它小于极大似然估计的最大绝对误差 0.368。     

基于EM算法的极大似然分布式量化估计融合新方法

该文针对水下目标探测中的多传感器分布式量化估计融合问题,建立了分布式量化估计融合模型,在考虑信道噪声且其统计特性不完全已知条件下,充分利用EM算法在观测数据缺失时参数估计的优越性,提出了一种基于期望极大化(EM)算法的极大似然分布式量化估计融合新方法。该方法将未知的水声信道噪声参数以及局部量化器量化概率建模为EM算法中二元高斯混合模型参数,利用极大似然估计方法的估计不变性得到目标参数的估计融合结果。仿真实验表明:该方法在局部传感器观测样本数目大于5000和信噪比大于6 dB时与已有理想信道条件下的估计方法性能相当,该方法为水下目标探测中分布式量化估计融合系统的工程实现提供了理论依据。     

从极大似然原理到单脉冲公式

本文首先推导得到大信噪比条件下单脉冲比的近似公式;然后根据和、差通道噪声特性求得单脉冲比的统计特性;最后利用极大似然估计原理推导单脉冲公式,同时得出了信噪比的极大似然估计.研究建立了单脉冲与极大似然估计的理论联系.该研究不仅有利于“雷达原理”课程教学,同时对雷达工程实践也有参考价值.     

广义Pareto分布海杂波模型参数的组合双分位点估计方法

广义Pareto分布的复合高斯模型可以很好地描述高分辨低擦地角对海探测场景中海杂波的重拖尾特性,实现该杂波模型下双参数的有效估计对雷达检测性能具有重要意义。对此,该文提出一种双参数的组合双分位点(CBiP)估计方法。该估计方法基于低阶多项式方程的显式求根表达式,充分组合利用回波中的样本信息,旨在实现高精度的双参数估计过程。此外,考虑到实际雷达工作中存在岛礁、渔船等造成的功率异常大的野点样本时,不同于传统的矩估计、最大似然(ML)估计等方法,组合双分位点估计方法仍可保持估计性能的鲁棒性。仿真及实测数据实验表明,在纯杂波环境中,组合双分位点估计方法可以实现与最大似然估计方法近似的估计精度,若存在异常样本,组合双分位点估计方法的估计性能优于上述几种传统估计方法。     

利用极大似然法统计回波信号实现光束闭环瞄准实验研究

瞄准偏差和光束抖动是光束瞄准系统中的两个最重要的误差。对以高斯光束和高斯抖动为基础的光束瞄准目标时产生的光回波信号进行数学建模;结合极大似然估计算法理论,建立并完善了基于回波信号的光束瞄准误差估计模型;并编写程序实现了Monte Carlo模型仿真,搭建了实验室平台。仿真和实验结果表明,极大似然估计算法表现出了优良的性能,能够同时准确地估计出瞄准偏差和光束抖动,信号样本越大其估计精度越高,且实验结果和仿真结果吻合得很好。在此基础上,根据实时偏差估计实现了实验室光束闭环瞄准实验。     

基于最大似然估计的16APSK载波捕获算法研究

针对在低信噪比情况下16APSK信号频偏捕获困难的问题,讨论了一种结合Kalman滤波和最大似然估计的非数据辅助频偏估计算法。该算法对包含载波频偏信息的相位序列进行最大似然频偏估计,通过Kalman滤波,降低了噪声对估计精度的影响。对结合Kalman滤波和最大似然估计的16APSK频偏估计算法进行了仿真。仿真结果表明,在低信噪比条件下,结合Kalman滤波的最大似然频偏估计算法可获得良好的性能。     

利用广义内积值迭代加权的空时协方差矩阵估计方法

精确估计协方差矩阵是空时自适应处理(STAP)的核心问题,基于最大似然的样本协方差矩阵估计方法仅适用于均匀检测环境。为了提高非均匀场景下协方差矩阵的估计精度,该文提出迭代加权的空时协方差矩阵估计方法。该方法依据广义内积值(GIP)与其统计均值的距离确定样本的加权系数,并通过建立广义内积直方图及迭代处理的方式进一步提高协方差矩阵的估计精度。仿真结果表明,该方法能够提高非均匀环境下协方差矩阵的估计性能。     

二进制移相键控信号信噪比估计研究

通过使用最大似然估计算法,对二相移相键控（2PSK）信号在高斯白噪声条件下的信噪比估计进行了研究,推导出信号功率、噪声功率的估计公式,依据该公式构建了2PSK的信噪比估计仿真模型,并在Matlab中进行了仿真计算,仿真结果表明在数据长度达到2000个左右时,信噪比的估计性能较好。     

基于最大似然估计的机载双基地雷达距离模糊杂波抑制方法

针对存在距离模糊情况下机载双基地雷达杂波抑制的难题,本文分析了存在距离模糊时机载双基地雷达的杂波特性,提出了一种基于最大似然估计的机载双基地雷达距离模糊杂波抑制方法.该方法首先通过最大似然估计对训练样本所包含的各个模糊距离环的杂波幅度进行估计,然后通过非均匀采样重构待检测距离门及其模糊距离环的杂波数据,再由重构的杂波数...     

Testing Goodness-of-Fit for the Singly Truncated Normal Distribution Using the Kolmogorov-Smirnov Statistic

This paper proposes the singly truncated normal distribution as a model for estimating radiance measurements from satellite-borne infrared sensors. These measurements are made in order to estimate sea-surface temperatures which can be related to radiances. Maximum-likelihood estimation is used to provide estimates for the unknown parameters. In particular, a procedure is described for estimating clear radiances in the presence of clouds and the Kolmogorov-Smirnov statistic is used to test goodness-of-fit of the measurements to the singly truncated normal distribution. Tables of quantile values of the Kolmogorov-Smirnov statistic for several values of the truncation point are generated from Monte Carlo experiments. Finally a numerical example using satellite data is presented to illustrate the application of the procedures.     

基于蒙特卡罗的电子产品可靠性分析

由于产品技术性能和结构要求等方面的提高,可靠性问题愈显突出,文章对电子元器件的可靠性进行了分析。寿命试验是可靠性试验中最重要最基本的项目之一,它是将产品放在特定的试验条件下考察其失效(损坏)随时间变化规律,寿命测试分析方法采用威布尔型分析方法,根据极大似然估计的不变原则,统计出元件的平均寿命的极大似然估计,另外采用指数分布,属于伽玛分布和威布尔分布的特殊情况,统计产品的偶然失效。实验仿真给出了数据,其目的在于提高电子产品的可靠性。     

Improved percentile estimation for the two-parameter Weibull distribution

This paper presents an improvement of a technique recently published to estimate the parameters of the two-parameter Weibull distribution. A simple percentile method is used to estimate the two parameters. Computer simulation is employed to compare the proposed method with the maximum likelihood estimation and graphical methods results. A set of frequently-used and newer expressions for estimating the cumulative density are examined. Comparisons are made with both complete and censored data. The primary advantage of the method is its computational simplicity. Results indicate that with respect to Mean Square Error and estimation of the characteristic value with complete data, the percentile method cannot outperform the maximum likelihood method, although differences are minor in many instances. However, with censored data, improvements over the maximum likelihood are observed. When the shape parameter is estimated, the percentile method is quite competitive with that of maximum likelihood for both complete and censored data under a variety of conditions.     

On maximum likelihood estimation for the two parameter Weibull distribution

This paper examines recent results presented on maximum likelihood estimation for the two parameter Weibull distribution. In particular, we seek to explain some recently reported values for estimator bias when the data for analysis contains both times to failure and censored times in operation; our discussion centres on the generation of sample data sets. We conclude that, under appropriate conditions, estimators are asymptotically unbiased, with relatively low bias in small to moderate samples. We then present the results of some further experiments which suggest that the previously reported values for estimator bias can be attributed to the method of generating sample data sets in simulation experiments.     

Interval estimation from censored and masked system-failure data

The author addresses confidence interval (CI) estimation in a competing risk (or multiple failure mode) framework where sample data are singly time-censored on the right and partially masked. A three-component series system with exponentially-distributed component-failure times is considered in order to represent cases involving full as well as partial masking. The approximate CIs considered are based on: asymptotic-normal theory for maximum likelihood estimators; cube-root transformation of the exponential distribution rate parameter; and inverted likelihood ratio tests. The small-sample coverage properties of these approximate CIs are estimated via computer simulation. These results also apply to models where component-failure times are Weibull distributed with known shape parameters     

Computationally efficient maximum likelihood estimation ofstructured covariance matrices

By invoking the extended invariance principle (EXIP), we present herein a computationally efficient method that provides asymptotic (for large samples) maximum likelihood (AML) estimation for structured covariance matrices and is referred to as the AML algorithm. A closed-form formula for estimating the Hermitian Toeplitz covariance matrices that makes AML computationally simpler than most existing Hermitian Toeplitz matrix estimation algorithms is derived. Although the AML covariance matrix estimator can be used in a variety of applications, we focus on array processing. Our simulation study shows that AML enhances the performance of angle estimation algorithms, such as MUSIC, by making them very close to the corresponding Cramer-Rao bound (CRB) for uncorrelated signals. Numerical comparisons with several structured and unstructured covariance matrix estimators are also presented     

MLSE equalization and decoding for multipath-fading channels

The performance of a receiver using a combined MLSE (maximum likelihood sequence estimation) equalizer/decoder and D-diversity reception is analyzed for multipath Rayleigh fading channels. An upper bound on the (decoded) bit error probability is derived. Comparisons to simulation results show that this upper bound is quite tight when the system has a high signal-to-noise ratio or when diversity reception is used. The upper bound involves an infinite series that must be truncated at a point where the remainder can be safely assumed to be small. An algorithm based on a one-directional stack algorithm is proposed for this calculation because it makes efficient use of computer memory