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Adaptive Radar Detection in joint Range and Azimuth based on the Hierarchical Latent Variable Model

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arXiv 2025年

作者： Yan, Linjie Hao, Chengpeng Han, Sudan Ricci, Giuseppe Hu, Zhanhao Orlando, Danilo Institute of Acoustics Chinese Academy of Sciences Beijing100864 China National Innovation Institute of Defense Technology Beijing China Dipartimento di Ingegneria dell’Innovazione Università del Salento Via Monteroni Lecce73100 Italy Dipartimento di Ingegneria dell’ Informazione Università di Pisa Via Caruso 16 Pisa56122 Italy

This paper focuses on the design of a robust decision scheme capable of operating in target-rich scenarios with unknown signal signatures (including their range positions, angles of arrival, and number) in a background of Gaussian disturbance. To solve the problem at hand, a novel estimation procedure is conceived resorting to the expectation-maximization algorithm in conjunction with the hierarchical latent variable model that are exploited to come up with a maximum a posteriori rule for reliable signal classification and angle of arrival estimation. The estimates returned by the procedure are then used to build up an adaptive detection architecture in range and azimuth based on the likelihood ratio test with enhanced detection performance. Remarkably, it is shown that the new decision scheme can maintain constant the false alarm rate when the interference parameters vary in the considered range of values. The performance assessment, conducted by means of Monte Carlo simulation, highlights that the proposed detector exhibits superior detection performance in comparison with the existing GLRT-based competitors. Copyright © 2025, The Authors. All rights reserved.

关键词： expectation maximization algorithm

Unified Statistical Channel Model for Turbulence-Induced Fading in Underwater Wireless Optical Communication Systems

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IEEE TRANSACTIONS ON COMMUNICATIONS 2019年第4期67卷 2893-2907页

作者： Zedini, Emna Oubei, Hassan Makine Kammoun, Abla Hamdi, Mounir Ooi, Boon S. Alouini, Mohamed-Slim Hamad Bin Khalifa Univ Coll Sci & Engn Doha Qatar King Abdullah Univ Sci & Technol Comp Elect & Math Sci & Engn Div Thuwal 23955 Saudi Arabia

A unified statistical model is proposed to characterize turbulence-induced fading in underwater wireless optical communication (UWOC) channels in the presence of air bubbles and temperature gradient for fresh and salty waters, based on experimental data. In this model, the channel irradiance fluctuations are characterized by the mixture exponential-generalized gamma (EGG) distribution. We use the expectation-maximization algorithm to obtain the maximum likelihood parameter estimation of the new model. Interestingly, the proposed model is shown to provide a perfect fit with the measured data under all channel conditions for both types of water. The major advantage of the new model is that it has a simple mathematical form making it attractive from a performance analysis point of view. Indeed, we show that the application of the EGG model leads to closed-form and analytically tractable expressions for key UWOC system performance metrics such as the outage probability, the average bit-error rate, and the ergodic capacity. To the best of our knowledge, this is the first-ever comprehensive channel model addressing the statistics of optical beam irradiance fluctuations in underwater wireless optical channels due to both air bubbles and temperature gradient.

关键词： Underwater wireless optical communication (UWOC) channel modeling distribution fitting maximum likelihood estimation expectation maximization algorithm mixture models performance analysis outage probability bit-error rate (BER) ergodic capacity

Structural network inference from time-series data using a generative model and transfer entropy

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PATTERN RECOGNITION LETTERS 2019年 125卷 357-363页

作者： Zhang, Zhihong Zhang, Genzhou Zhang, Zhonghao Chen, Guo Zeng, Yangbin Wang, Beizhan Hancock, Edwin R. Xiamen Univ Xiamen Fujian Peoples R China State Grid Shaanxi Elect Power Co Xian Shaanxi Peoples R China State Grid Shaanxi Informat & Telecommun Co LTD Xian Shaanxi Peoples R China Univ York York N Yorkshire England

In this paper we develop a novel framework for inferring a generative model of network structure representing the causal relations between data for a set of objects characterized in terms of time series. To do this we make use of transfer entropy as a means of inferring directed information transfer between the time-series data. Transfer entropy allows us to infer directed edges representing the causal relations between pairs of time series, and has thus been used to infer directed graph representations of causal networks for time-series data. We use the expectation maximization algorithm to learn a generative model which captures variations in the causal network over time. We conduct experiments on fMRI brain connectivity data for subjects in different stages of the development of Alzheimer's disease (AD). Here we use the technique to learn class exemplars for different stages in the development of the disease, together with a normal control class, and demonstrate its utility in both graph multi-class and binary classifications. These experiments are showing the effectiveness of our proposed framework when the amounts of training data are relatively small. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Transfer entropy Supergraph Time series Network inference expectation maximization algorithm

Empirical Welfare maximization with Constraints

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arXiv 2021年

作者： Sun, Liyang UCL United Kingdom CEMFI Spain

Empirical Welfare maximization (EWM) is a framework that can be used to select welfare program eligibility policies based on data. This paper extends EWM by allowing for uncertainty in estimating the budget needed to implement the selected policy, in addition to its welfare. Due to the additional estimation error, I show there exist no rules that achieve the highest welfare possible while satisfying a budget constraint uniformly over a wide range of DGPs. This differs from the setting without a budget constraint where uniformity is achievable. I propose an alternative trade-off rule and illustrate it with Medicaid expansion, a setting with imperfect take-up and varying program costs. © 2021, CC BY.

关键词： expectation maximization algorithm

Design and analysis of direction of arrival using hybrid expectation-maximization and MUSIC for wireless communication

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OPTIK 2018年 170卷 48-55页

作者： Malla, Prince Priya Mukherjee, Amrit Routray, Sidheswar Palai, G. KL Univ Dept Elect & Commun Engn Guntur Andhra Prades India KIIT Univ Sch Elect Engn Bhubaneswar Odisha India GITA Dept Elect & Commun Engn Bhubaneswar Odisha India

As multiple signal classification (MUSIC) algorithm is unable to estimate the direction of arrival (DOA) of highly correlated or coherent signal, based on array processing and decomposition of covariance matrix of incident signals due to its lack of source identification. In the proposed hybrid model, we have considered expectation maximization (EM) algorithm for precise identification of the DOA of highly correlated signals in wireless communication applications. The proposed model is analysed, simulated and verified using two different source signals. The mathematical analysis shows substantial closeness with the simulated results. The robustness of the proposed algorithm is further verified by adding noise to the incident signals. The utilization of the EM algorithm in the proposed hybrid approach reduces the time requirement and mathematical complexity of MUSIC algorithm for DOA estimation. By exploiting the EM algorithm, the original transmitted signal and its arriving angle on the antenna element are estimated from the mixture of interferer's signal, transmitted signal, multipath component and noise. Hence it is straightforward to recognise the desired signal.

关键词： Direction of arrival Music algorithm expectation maximization algorithm Wireless communication

Cumulative Logit Ordinal Regression with Proportional Odds under Nonignorable Missing Responses – Application to Phase III Trial

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arXiv 2025年

作者： Maity, Arnab Kumar Tan, Huaming Pradhan, Vivek Bandyopadhyay, Soutir Boehringer Ingelheim Pharmaceuticals Inc. RidgefieldCT United States Pfizer Inc. GrotonCT United States Pfizer Inc. CambridgeMA United States Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO United States

Missing data are inevitable in clinical trials, and trials that produce categorical ordinal responses are not exempted from this. Typically, missing values in the data occur due to different missing mechanisms, such as missing completely at random, missing at random, and missing not at random. Under a specific missing data regime, when the conditional distribution of the missing data is dependent on the ordinal response variable itself along with other predictor variables, then the missing data mechanism is called nonignorable. In this article we propose an expectation maximization based algorithm for fitting a proportional odds regression model when the missing responses are nonignorable. We report results from an extensive simulation study to illustrate the methodology and its finite sample properties. We also apply the proposed method to a recently completed Phase III psoriasis study using an investigational compound. The corresponding SAS program is provided. © 2025, CC BY.

关键词： expectation maximization algorithm

Constellation Learning-Based Signal Detection for Ambient Backscatter Communication Systems

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2019年第2期37卷 452-463页

作者： Zhang, Qianqian Guo, Huayan Liang, Ying-Chang Yuan, Xiaojun Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu 611731 Sichuan Peoples R China Univ Elect Sci & Technol China Ctr Intelligent Networking & Commun Chengdu 611731 Sichuan Peoples R China

Ambient backscatter communication (AmBC) is a promising solution to energy-efficient and spectrum-efficient Internet of Things with stringent power and cost constraints. In an AmBC system, recovering the tag information at the reader, however, is a challenging task due to the difficulty in acquiring the relevant channel-state information (CSI). To eliminate the need to estimate the CSI, in this paper, we propose a label-assisted transmission framework, in which two known labels are transmitted from the tag before data transmission. By exploring the received signal constellation information, we propose modulation-constrained expectation maximization algorithm, based on which two detection methods are developed. One method, referred to as constellation learning with labeled signals, learns the parameters by clustering the labeled signals and recovers the unlabeled signals by the learnt parameters. The other method, referred to as constellation learning with labeled and unlabeled signals, uses all received signals in clustering. Efficient initialization techniques are provided for the two clustering algorithms. Finally, extensive simulation results show that the proposed constellation learning methods achieve comparable performance as the optimal detector with perfect CSI.

关键词： Ambient backscatter communication (AmBC) constellation learning signal detection clustering expectation maximization algorithm

Jointly Modeling and Clustering Tensors in High Dimensions

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arXiv 2021年

作者： Cai, Biao Zhang, Jingfei Sun, Will Wei Department of Management Sciences City University of Hong Kong Hong Kong Goizueta Business School Emory University United States Daniels School of Business Purdue University United States

We consider the problem of jointly modeling and clustering populations of tensors by introducing a high-dimensional tensor mixture model with heterogeneous covariances. To effectively tackle the high dimensionality of tensor objects, we employ plausible dimension reduction assumptions that exploit the intrinsic structures of tensors such as low-rankness in the mean and separability in the covariance. In estimation, we develop an efficient high-dimensional expectation-conditional-maximization (HECM) algorithm that breaks the intractable optimization in the M-step into a sequence of much simpler conditional optimization problems, each of which is convex, admits regularization and has closed-form updating formulas. Our theoretical analysis is challenged by both the non-convexity in the EM-type estimation and having access to only the solutions of conditional maximizations in the M-step, leading to the notion of dual non-convexity. We demonstrate that the proposed HECM algorithm, with an appropriate initialization, converges geometrically to a neighborhood that is within statistical precision of the true parameter. The efficacy of our proposed method is demonstrated through comparative numerical experiments and an application to a medical study, where our proposal achieves an improved clustering accuracy over existing benchmarking methods. © 2021, CC BY.

关键词： expectation maximization algorithm

A Note on Parameters Estimation for Nonlinear Wiener Processes With Measurement Errors

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IEEE ACCESS 2019年 7卷 176756-176766页

作者： Tang, Shengjin Yu, Chuanqiang Sun, Xiaoyan Fan, Hongdong Si, Xiaosheng High Tech Inst Xian Xian 710025 Peoples R China

The nonlinear Wiener process has been widely used as a model for the degradation process. This note concerns parameters estimation of nonlinear Wiener processes with measurement errors (WPME) by the maximum likelihood estimation method. Firstly, we prove a rule that the estimated results based on the sample likelihood function developed through observations at each point are equal to the results from the first differences of the observations. This rule indicates that for reducing computation complexity the first differences of the observations may develop the sample likelihood function. Then we present a simple method to calculate the determinant and the inverse matrix of the covariance matrix of the WPME. This simple method could avoid the overflow error when calculating the determinant of the covariance matrix and the case that the inverse matrix is close to be singular, which could result in wrong estimation results. Secondly, we highlight the unit-specific assumption, which has a significant impact on parameters estimation but has been neglected in many papers. Then, we propose a modified expectation maximization algorithm for parameters estimation with random effects. Finally, to demonstrate the application and superiority of the proposed method, we provide a numerical example and a case study with comparison to several representative methods in the literature.

关键词： Degradation model maximum likelihood estimation Wiener process measurement error nonlinear expectation maximization algorithm