基于进化规划的Markov随机场参数的估计

在应用Markov随机场作为先验模型对图像进行贝叶斯估计时,配分函数的难以计算使得对Markov随机场参数的估计存在着很大困难.为此,本文提出一种新的基于进化规划的参数估计法.该方法采用进化规划来寻求合适的参数,使得由该参数得到的生成图像和原始图像间的差异最小.该方法不仅可避免配分函数计算上的困难,而且从该参数出发还可得到最相似于(可完全吻合)原始图像的生成图像.在这一点上,该方法要明显优于以往传统的基于似然函数的参数估计法,如极大伪似然法.最终的实验结果也证实了该方法的可行性.     

基于加性噪声模型的缺失数据因果推断 *

推断数据间存在的因果关系是很多科学领域中的一个基础问题.然而现在暂时还没有快速有效的方法对缺失数据进行因果推断。为此,文中提出一种基于加性噪声模型下适应缺失数据的因果推断算法.该算法是基于加性噪声模型下利用最大似然估计法结合加权样本修复数据的思想构造以似然函数形式的模型评分函数,并以此度量模型相对于缺失数据集的优劣程度,通过迭代学习确定因果方向.每次迭代学习包括使用参数修复数据和在修复后的完整数据集下估计参数.该方法既解决了加性噪声模型中映射函数的参数学习困难性问题,又避免了现有学习方法所存在的主要问题。实验表明,在数据缺失比例扩大的情况下该算法仍具有较高识别能力.     

基于粒子滤波的分布式目标跟踪算法

根据无线传感器网络分布式目标跟踪的特性,提出一种改进粒子滤波算法。将簇内各节点最新的观测数据用极大似然估计法得到目标的状态信息,该信息作为参考分布更换粒子滤波部分粒子,引入模糊推理的数据融合方法为各个节点滤波结果分配不同权值,通过加权平均法得到目标的状态信息。仿真实验表明该算法能有效提高目标跟踪的精度。     

基于无线传感器网络的声音目标定位算法

无线传感器网络中基于声音能量的多目标定位常采用最大似然估计法,但该定位方法未考虑目标能量相互混合的影响.利用独立分量分析方法求得目标之间的能量相互混合的比例,在此基础上建立声音能量模型,并利用最大似然估计法对目标进行定位.实验结果表明,与已有的最大似然多目标定位方法相比,该方法具有更高的定位精度和抗噪性能.     

数据融合方法在单传感器系统中的应用

为了提高数据处理的精度,提出了一种适用于单传感器系统的数据融合方法。该方法先将来自单传感器的n个样本观测值细分成k组,并把各细分组所对应的样本均值假想为来自k个不同传感器的样本观测值,借助于极大似然函数估计法导出数据融合公式。基于该融合公式,证明了在单传感器系统中,当样本观测值一定时,分组数据融合的估计效果优于单组算术平均的估计效果;若再细分原有的分组,则细分后的融合估计效果优于细分前的融合估计效果。实例的数据分析验证了结论的正确性。     

多径相干信号到达角和衰落系数的最大似然估计

针对多径相干信号到达角(DOA)和衰落系数(FC)的估计问题,在已知信号波形条件下,提出了一种联合估计DOA和衰落系数的改进最大似然估计方法。通过改进最大似然估计求极值获得DOA和衰落系数的解,在联合估计参数时,使用交替极大值技术,降低了估计算法的复杂度,并且推导了最大似然估计的Cramér-Rao界。理论分析和实验表明,该方法与现有方法相比具有更好的估计性能,当来自不同辐射源的多径信号中存在相同DOA时,仍然可以得到很好的估计效果。     

基于PCA和广义高斯建模的多聚焦图像融合

为了提高多聚焦图像的融合性能,针对统计图像融合方法中像素间融合参数估计的不足,提出一种基于小波变换域主成分分析（PCA）和广义高斯建模的多聚焦图像融合方法。将高频子带系数建模为广义高斯分布,并通过改进的最大似然估计法获取融合参数。结合低频子带系数的区域PCA融合方法,最终实现有效的图像融合。实验结果表明,该方法与传统的多聚焦图像融合方法相比,可使融合图像的信息量更丰富,具有更佳的视觉效果。     

基于加性协同的离散贝叶斯网络参数学习

针对小数据集条件下离散BN参数学习的问题,为了将加性协同约束融入到BN参数学习过程中,通过借鉴经典保序回归算法的思想,提出四种处理加性协同约束的方法,进而利用经典的草地湿润模型对改进算法进行仿真,并与最大似然估计算法进行对比,仿真结果表明,改进算法在精度上有一定优势,能够很好的对最大似然估计算法进行修正,得到相对准确的参数,然而时效性则劣于最大似然估计算法.进一步将改进算法应用到弹道导弹突防模型的参数学习中,通过推理分析验证算法的有效性.     

基于多传感器数据融合的管道机器人精确定位技术

针对自主管道检测机器人的管内定位问题,提出了一种能够提高定位精度的多里程仪测量数据一致性融合方法.首先基于置信距离测度概念构造了多里程仪测量数据之间的置信距离矩阵和关系矩阵,然后利用有向图方法剔除含有较大误差的或错误的测量数据,最后采用极大似然估计法求解多里程仪测量数据的最优融合值.在管道机器人智能控制器上使用C语言编程实现文中算法.在机器人的自主爬行实验过程中,通过测量模拟油气管道的焊缝间距验证了该算法的有效性.     

一种新的软件可靠性模型参数估计方法

一个软件可靠性模型建立以后,需要对模型的参数进行估计,而参数估计的准确程度将直接影响到模型的预测能力.现在对参数的估计一般采用极大似然估计法.文中提出一种加权最小二乘法,根据各故障数据点对预测贡献值的不同,给与相应的权重.并以一种重要的GO软件可靠性模型为例进行分析,实验表明,该方法获得的参数模型具有更好的预测能力.     

带测量误差的非线性退化过程建模与剩余寿命估计

剩余寿命(Remaining useful lifetime, RUL)估计是设备视情维护和预测与健康管理(Prognostics and health management, PHM)中的一项关键问题. 采用退化过程建模进行剩余寿命估计的研究中,现有方法仅考虑了具有线性或可以线性化的退化轨迹的问题.本 文提出了一种基于扩散过程的非线性退化过程建模方法,在首达时间的意义下,推导出了剩余寿命的分布.该方法可以描述一般的非线性退化轨迹, 现有的线性退化建模方法是其特例.在参数的推断中,考虑到真实的退化过程受到测量误差的影响,难以直接测量得到, 因此,在退化建模的过程中引入了测量误差对退化观测数据的影响,通过观测数据,提出了一种退化模型未知参数的极大似然估计方法. 最后,通过激光发生器和陀螺仪的退化测量数据验证了本文方法明显优于线性建模方法,具有潜在的工程应用价值.     

Averaging, maximum penalized likelihood and Bayesian estimation forimproving Gaussian mixture probability density estimates

We apply the idea of averaging ensembles of estimators to probability density estimation. In particular, we use Gaussian mixture models which are important components in many neural-network applications. We investigate the performance of averaging using three data sets. For comparison, we employ two traditional regularization approaches, i.e., a maximum penalized likelihood approach and a Bayesian approach. In the maximum penalized likelihood approach we use penalty functions derived from conjugate Bayesian priors such that an expectation maximization (EM) algorithm can be used for training. In all experiments, the maximum penalized likelihood approach and averaging improved performance considerably if compared to a maximum likelihood approach. In two of the experiments, the maximum penalized likelihood approach outperformed averaging. In one experiment averaging was clearly superior. Our conclusion is that maximum penalized likelihood gives good results if the penalty term in the cost function is appropriate for the particular problem. If this is not the case, averaging is superior since it shows greater robustness by not relying on any particular prior assumption. The Bayesian approach worked very well on a low-dimensional toy problem but failed to give good performance in higher dimensional problems.     

Jointly registering and fusing images from multiple sensors

In this paper, a novel approach is proposed for jointly registering and fusing a multisensor ensemble of images. Based on the idea that both groupwise registration and fusion can be treated as estimation problems, the proposed approach simultaneously models the mapping from the fused image to the source images and the joint intensity of all images with motion parameters at first, and then combines these models into a maximum likelihood function. The relevant parameters are determined through employing an expectation maximization algorithm. To evaluate the performance of the proposed approach, some representative image registration and fusion approaches are compared on different multimodal image datasets. The criterion, which is defined as the average pixel displacement from its true registered position, is used to compare the performances of registration approaches. As for evaluating the fusion performance, three fusion quality measures which are metric Q^ab/f, mutual information and average gradient are employed. The experimental results show that the proposed approach has improved performance compared to conventional approaches.     

一种改进的多无线传感器数据分批估计自适应加权融合算法

为解决温室大棚中多无线传感器监测数据融合精度低的问题,提出了一种改进型的分批估计自适应加权融合算法。算法首先对单个无线温度传感节点一段时间内所采集的数据根据容许函数阈值剔除误差较大的数据,然后对该温度传感器的数据进行分批估计得出该节点某一段时间内的最优估计值,以此得到该区域所有无线温度传感节点最优估计值后,依据权值最优分配原则对每组传感器数据进行组内自适应加权融合,从而计算得到该段时间内温室大棚的温度精确值。通过实验验证得出：相对于平均值法与传统的分批估计算法,本算法数据融合易于实现,融合值相对误差值更低,稳健性更好。     

A maximum likelihood approach to density estimation with semidefinite programming

Density estimation plays an important and fundamental role in pattern recognition, machine learning, and statistics. In this article, we develop a parametric approach to univariate (or low-dimensional) density estimation based on semidefinite programming (SDP). Our density model is expressed as the product of a nonnegative polynomial and a base density such as normal distribution, exponential distribution, and uniform distribution. When the base density is specified, the maximum likelihood estimation of the polynomial is formulated as a variant of SDP that is solved in polynomial time with the interior point methods. Since the base density typically contains just one or two parameters, computation of the maximum likelihood estimate reduces to a one- or two-dimensional easy optimization problem with this use of SDP. Thus, the rigorous maximum likelihood estimate can be computed in our approach. Furthermore, such conditions as symmetry and unimodality of the density function can be easily handled within this framework. AIC is used to choose the best model. Through applications to several instances, we demonstrate flexibility of the model and performance of the proposed procedure. Combination with a mixture approach is also presented. The proposed approach has possible other applications beyond density estimation. This point is clarified through an application to the maximum likelihood estimation of the intensity function of a nonstationary Poisson process.     

基于混合高斯模型的窄带目标跟踪方法

基于混合高斯模型的轨迹分布融合方法适用于窄带目标跟踪系统.这种算法针对宽带跟踪结果的不精确,目标模糊,窄带跟踪需要依赖人工实现的问题,提出了一种基于混合高斯模型的自动窄带目标跟踪技术.该方法首先将目标方位分布看做是混合高斯模型,利用期望最大化算法估计混合高斯模型中的参数,然后利用混合高斯模型对目标方位进行聚类,最后利用平均加权法对目标方位进行融合,得到清晰稳定的目标跟踪结果.     

Maximum margin Bayesian network classifiers

We present a maximum margin parameter learning algorithm for Bayesian network classifiers using a conjugate gradient (CG) method for optimization. In contrast to previous approaches, we maintain the normalization constraints on the parameters of the Bayesian network during optimization, i.e., the probabilistic interpretation of the model is not lost. This enables us to handle missing features in discriminatively optimized Bayesian networks. In experiments, we compare the classification performance of maximum margin parameter learning to conditional likelihood and maximum likelihood learning approaches. Discriminative parameter learning significantly outperforms generative maximum likelihood estimation for naive Bayes and tree augmented naive Bayes structures on all considered data sets. Furthermore, maximizing the margin dominates the conditional likelihood approach in terms of classification performance in most cases. We provide results for a recently proposed maximum margin optimization approach based on convex relaxation. While the classification results are highly similar, our CG-based optimization is computationally up to orders of magnitude faster. Margin-optimized Bayesian network classifiers achieve classification performance comparable to support vector machines (SVMs) using fewer parameters. Moreover, we show that unanticipated missing feature values during classification can be easily processed by discriminatively optimized Bayesian network classifiers, a case where discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.     

无下采样轮廓小波在图像融合中的应用

在无下采样Contourlet变换和小波变换的基础上,针对低、高频系数的特点,提出一种新的基于无下采样轮廓小波变换的图像融合算法。该算法对无下采样轮廓小波分解后的低频部分采用了选择与加权平均的融合规则进行融合,而对各层高频系数采用局部区域加权平均融合规则进行融合。实验结果表明,该方法在包含信息量、清晰度上都有明显的提高。     

A multi-temporal multi-sensor circular fusion filter

In many multi-sensor applications, noisy angular data are modeled as random variables which are commonly defined on the linear domain while the “classical fusion and filtering” are used to process their realizations. Weighted sum and Kalman filter are indeed the common approaches used to fuse and filter the data in most cases. These approaches are limited by the periodic nature of the angular data. In this article, the error of angular measurements is assumed to follow a von Mises distribution. Under this assumption, a multi-sensor fusion operator is proposed. Under the same assumption, a recursive circular filter that provides estimates of the parameters of a model state is proposed. The proposed methodology is assessed using both synthetic and real data. The real data are obtained from a magnetometer and a gyroscope.