检索结果-内蒙古大学图书馆

expectation maximization algorithm for GPS Positioning in Multipath Environments Based on Volterra Series

CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2023年第10期42卷 6278-6295页

作者： Cheng, Lianyuan Chen, Jing Mao, Yawen Liao, Cuicui Zhu, Quanmin Jiangnan Univ Sch Sci Wuxi 214122 Jiangsu Peoples R China Univ West England Dept Engn Design & Math Bristol BS16 1QY England

The multipath effect error (MEE) is typically not taken into account by the RTKLIB localization method, and this may lead to poor positioning accuracy. This paper proposes an expectation maximization (EM) algorithm for GPS positioning based on Volterra series, and the pseudoranges contaminated by MEE are considered as missing data. Firstly, the Volterra series is introduced to linearize the pseudorange equation. Then, the EM algorithm is used to iteratively update the user location and missing data. Compared with the RTKLIB method, the proposed algorithm has more accurate positioning accuracy. The simulation example shows the effectiveness of the proposed algorithm.

关键词： Parameter estimation GPS position Multipath effect Volterra series expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Composite likelihood expectation–maximization algorithm for the first-order multivariate integer-valued autoregressive model with multivariate mixture distributions

引用

Mathematics and Computers in Simulation 2025年 237卷 167-187页

作者： Yu, Weiyang Zheng, Haitao School of Mathematics Southwest Jiaotong University Chengdu611756 China

In this paper, we develops a estimation framework for first-order multivariate integer-valued autoregressive (MINAR(1)) models with multivariate Poisson-lognormal (MPL) or multivariate geometric-logitnormal (MGL) innovations. Owing to the structural complexity of the MINAR(1) framework and the latent dependence in MPL/MGL innovation processes, the likelihood functions involve summations and integrals. Traditional expectation–maximization (EM) algorithms face prohibitive computational demands as model dimensionality increases. To address this, we propose a composite likelihood expectation–maximization (CLEM) algorithm that strategically combines composite likelihood with the EM algorithm, reducing the computational burden. Furthermore, we implement a Cholesky parameterization for the covariance matrix to ensure positive definiteness. Through comprehensive Monte Carlo simulations, we demonstrate the performance of CLEM algorithm. Finally, we validate the method's practical utility by analyzing a real-world dataset. © 2025 International Association for Mathematics and Computers in Simulation (IMACS)

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Unrolled expectation maximization algorithm for radio interferometric imaging in presence of non Gaussian interferences

引用

Signal Processing 2025年 237卷

作者： Arab, Nawel Mhiri, Yassine Vin, Isabelle El Korso, Mohammed Nabil Larzabal, Pascal SATIE ENS Paris-Saclay Université Paris-Saclay Gif-sur-Yvette91190 France MAP5 Université Paris Cité Paris75006 France L2S CentraleSupelec Université Paris-Saclay Gif-sur-Yvette91190 France

This paper proposes an unrolled expectation maximization (EM) algorithm tailored for robust radio interferometric imaging in the presence of non-Gaussian radio interferences. We introduce a compound Gaussian model for the observation noise and derive an unrolled neural architecture based on the EM algorithm to tackle the reconstruction problem in a robust manner. This innovative approach aims to enhance image reconstruction by simultaneously incorporating model information and generalization for the case of non-Gaussian heavy-tailed noise distribution, while leveraging the benefits of deep learning. Our experiments demonstrate significant improvements over state-of-the-art methods, highlighting the efficacy of our proposed scheme in handling the complexities of radiofrequency interference and improving image reconstruction accuracy. © 2025 Elsevier B.V.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

expectation maximization algorithm over Fourier series (EMoFS)

引用

SIGNAL PROCESSING 2022年 194卷 108453-108453页

作者： Yildiz, Mursel Continental AG Hannover Germany

The application of expectation maximization (EM) algorithm for the univariate problems in the literature suffers mainly from the requirement of the prior information on parametrized probability distribution function (pdf) and its family. For the highly dynamic environments, however, there is a high potential of mismatch between the domain characteristics and the pre-assumed distribution. The observed data may have been drawn based on an unknown pdf, which can additionally be combination of discontinuous functions with different types. For such cases, it is very likely that an arbitrary selection of a mixture distribution would yield worse performance. Even if the domain characteristics are captured correctly, i.e. the pdf family is known, another complexity may arise due to the fact that a tractable and a closed form cannot always be obtained. Addressing these two problems, we present the EM over Fourier Series (EMoFS) approach for univariate problems to be solved with EM. Our solution produces the true pdf ap-proximately;thus sidesteps the necessity of a prior assumption. Additionally it guarantees a tractable and closed form for E-step. We verify and evaluate our model via comparison with state of the art solutions, theoretical experiments and real world problems. (c) 2021 Elsevier B.V. All rights reserved.

关键词： expectation maximization algorithm Fourier series Maximum likelihood

来源：评论

学校读者我要写书评

暂无评论

Multiple point sets registration based on expectation maximization algorithm

引用

COMPUTERS & ELECTRICAL ENGINEERING 2018年 70卷 1-11页

作者： Zhou, Yiqiong Xu, Siyu Jin, Congcong Guo, Ziyi Xi An Jiao Tong Univ Sch Software Engn Xian Shaanxi Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Sichuan Peoples R China

Many methods have been proposed and improved to deal with multi-view point cloud registration. Most of them are based on the classical method, namely Iterative Closest Point(ICP), which is fast and accurate in most cases. However in the presence of noise and outliers, the equal weights ICP assigned to all correspondences would lead to unsatisfactory registration results. To address this issue, this paper proposes a new automatic multi-view registration method based on expectation maximization(EM). A Gaussian distribution on representing the relationship between point-sets according to their distance was introduced first. Then EM is brought in to optimize the likelihood function formulated with above Gaussian distribution. Through an iterative method the multi-view registration results can be obtained at last. The experimental results demonstrate accuracy and robustness of our methods over four state-of-the-art algorithms, especially when noisy data exist.

关键词： Multi-view registration Noise Gaussian distribution expectation maximization algorithm Iterative Closest Point

来源：评论

学校读者我要写书评

暂无评论

Bayesian expectation maximization algorithm by using B-splines functions: Application in image segmentation

引用

MATHEMATICS AND COMPUTERS IN SIMULATION 2016年 120卷 50-63页

作者： Hadrich, Atizez Zribi, Mourad Masmoudi, Afif ULCO Lab Informat Signal & Image Cote Opale LISIC EA 4 F-62228 Calais France Univ Sfax Fac Sci Sfax Lab Probabil & Stat Sfax Tunisia

The Bayesian implementation of finite mixtures of distributions has been an area of considerable interest within the literature. Given a sample of independent identically distributed real-valued random variables with a common unknown probability density function f, the considered problem here is to estimate the probability density function f from the sample set. In our work, we suppose that the density f is expressed as a finite linear combination of second order B-splines functions. The problem of estimating the density f leads to the estimation of the coefficients of B-splines. In order to solve this problem, we suppose that the prior distribution of the B-splines coefficients is a Dirichlet distribution. The estimation of these coefficients allowed us to introduce a new algorithm called Bayesian expectation maximization. In fact, this algorithm, which is the combination of the Bayesian approach and the expectation maximization algorithm, attempts to directly optimize the posterior Bayesian distribution. This algorithm has been generalized to the case of mixing distributions. We have studied the asymptotic properties of the Bayesian estimator. Then, the performance of our algorithm has been evaluated and compared by making a simulation study, followed by a real image segmentation. In both cases, our proposed Bayesian algorithm is shown to give better results. (C) 2015 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Mixture density B-splines functions expectation maximization algorithm Bayesian estimator Image segmentation

来源：评论

学校读者我要写书评

暂无评论

A review of the expectation maximization algorithm in data-driven process identification

引用

JOURNAL OF PROCESS CONTROL 2019年 73卷 123-136页

作者： Sammaknejad, Nima Zhao, Yujia Huang, Biao Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada

The expectation maximization (EM) algorithm has been widely used for parameter estimation in data driven process identification. EM is an algorithm for maximum likelihood estimation of parameters and ensures convergence of the likelihood function. In presence of missing variables and in ill conditioned problems, EM algorithm greatly assists the design of more robust identification algorithms. Such situations frequently occur in industrial environments. Missing observations due to sensor malfunctions, multiple process operating conditions and unknown time delay information are some of the examples that can resort to the EM algorithm. In this article, a review on applications of the EM algorithm to address such issues is provided. Future applications of EM algorithm as well as some open problems are also provided. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： expectation maximization algorithm Data-driven process identification Multiple models Switching State space Time delay Hidden Markov Models Latent variable models Outlier treatment Missing data

来源：评论

学校读者我要写书评

暂无评论

Non-negative variance component estimation for the partial EIV model by the expectation maximization algorithm

引用

GEOMATICS NATURAL HAZARDS & RISK 2020年第1期11卷 1278-1298页

作者： Wang, Leyang Wu, Qiwen East China Univ Technol Fac Geomat Nanchang Jiangxi Peoples R China Shandong Jianzhu Univ Sch Surveying & Geoinformat Jinan Peoples R China

A difficulty in variance component estimation (VCE) is that the estimates may become negative, which is not acceptable in practice. This article presents two new methods for non-negative VCE that utilize the expectation maximization algorithm for the partial errors-in-variables model. The former searches for the desired solutions with unconstrained estimation criterion and concludes statistically that the variance components have indeed moved to the edge of the parameter space when negative estimates appear implemented by the other existing VCE methods. We concentrate on the formulation and provide non-negative analysis of this estimator. In particularly, the latter approach, which has greater computational efficiency, would be a practical alternative to the existing VCE-type algorithms. Additionally, this approach is easy to implement, the non-negative variance components are automatically supported by introducing non-negativity constraints. Both algorithms are free from a complex matrix inversion and reduce computational complexity. The results show that our algorithms retrieve well to achieve identical estimates over the other VCE methods, the latter approach can quickly estimate parameters and has practical aspects for the large volume and multisource data processing.

关键词： Partial errors-in-variables model expectation maximization algorithm Fisher-Score algorithm Variance component estimation

来源：评论

学校读者我要写书评

暂无评论

An expectation-maximization algorithm for Spectral Reconstruction Under the Spectral Hard Model

SSRN

引用

SSRN 2025年

作者： Kaufmann, Lea Kasterke, Marvin Steven Meißner, Jan Kateri, Maria Koß, Hans-Jürgen Institute of Statistics RWTH Aachen University Germany Institute of Technical Theormodynamics RWTH Aachen University Germany

Indirect Hard Modeling (IHM) is a physics-based evaluation method for the quantitative analysis of fluid compositions using spectroscopic techniques such as Raman spectroscopy. In this approach, mixture spectra are represented as a superposition of pure substance models, with each component described by a sum of parameterized peak functions. Nevertheless, the accuracy of the composition predictions depends critically on user decisions regarding both the number of peak functions and the specific parameter adjustments employed. In this paper, we propose an expectation-maximization (EM) based algorithm for generating spectral reconstructions of pure substance models that does not require the prespecification of the number of peaks or any initial values. The idea is based on the fact that the pure substance model has a strong connection to probabilistic Voigt mixture models, with the probabilistic mixing proportions being unknown and interpreted as hidden variables. To obtain maximum likelihood estimates for the model parameters in such settings, the EM algorithm is employed. Since the choice of initial values for an algorithm can be very challenging in complex applications, and may affect the results, our algorithm does not require any specification of initial values by the user. The efficient and fast performance of the employed EM algorithm enables the fit of a given spectrum for an unknown number of peaks, based on a model selection criterion. In simulation studies, we demonstrate that our approach can recognize the true underlying function in settings of high noise, different baselines, as well as low data availability, yielding reliable *** a validation study, the proposed algorithm was tested using experimental data. It was integrated into an Indirect Hard Modeling (IHM) framework and applied to the toluene-cyclohexane chemical system. We introduce an efficient approach to IHM pure substance modeling that operates without user interaction. The obtained res

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

An approach to operational modal analysis using the expectation maximization algorithm

引用

MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2012年 31卷 109-129页

作者： Cara, F. Javier Carpio, Jaime Juan, Jesus Alarcon, Enrique Univ Politecn Madrid Stat Lab ETS Ingenieros Ind E-28006 Madrid Spain Univ Politecn Madrid Dept Struct Mech ETS Ingenieros Ind E-28006 Madrid Spain

This paper presents the expectation maximization algorithm (EM) applied to operational modal analysis of structures. The EM algorithm is a general-purpose method for maximum likelihood estimation (MLE) that in this work is used to estimate state space models. As it is well known, the MLE enjoys some optimal properties from a statistical point of view, which make it very attractive in practice. However, the EM algorithm has two main drawbacks: its slow convergence and the dependence of the solution on the initial values used. This paper proposes two different strategies to choose initial values for the EM algorithm when used for operational modal analysis: to begin with the parameters estimated by Stochastic Subspace Identification method (SSI) and to start using random points. The effectiveness of the proposed identification method has been evaluated through numerical simulation and measured vibration data in the context of a benchmark problem. Modal parameters (natural frequencies, damping ratios and mode shapes) of the benchmark structure have been estimated using SSI and the EM algorithm. On the whole, the results show that the application of the EM algorithm starting from the solution given by SSI is very useful to identify the vibration modes of a structure, discarding the spurious modes that appear in high order models and discovering other hidden modes. Similar results are obtained using random starting values, although this strategy allows us to analyze the solution of several starting points what overcome the dependence on the initial values used. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Structural dynamics Operational modal analysis System identification Stochastic subspace identification method Maximum likelihood estimation expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：