IEEE Transactions on Signal and Information Processing over Networks最新文献_第10页

Asymptotically Efficient Moving Target Localization in Distributed Radar Networks 分布式雷达网络中渐进有效的运动目标定位

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-08-17 DOI: 10.1109/TSIPN.2023.3306099

Mohammad Reza Jabbari;Mohammad Reza Taban;Saeed Gazor;Mehrdad Kaimasi

{"title":"Asymptotically Efficient Moving Target Localization in Distributed Radar Networks","authors":"Mohammad Reza Jabbari;Mohammad Reza Taban;Saeed Gazor;Mehrdad Kaimasi","doi":"10.1109/TSIPN.2023.3306099","DOIUrl":"10.1109/TSIPN.2023.3306099","url":null,"abstract":"In this article, we investigate the joint estimation of the position and velocity of a moving target in distributed networks of moving radars using Time Of Arrival (TOA) and Doppler Shift (DS) measurements. In contrast to most of the existing/recent methods, we avoid the use of Nuisance Variables (NVs) by employing algebraic manipulations. We reformulate a new set of equations that are linear with respect to the target's position and velocity, resulting in a significant performance improvement. Subsequently, we propose a Two-Stage Weighted Least Squares (TSWLS) estimator and recommend two alternative algorithms to reduce computational complexity while preserving the accuracy by selecting either a transmitter or receiver as the reference sensor. We implement the proposed method over fully and partially connected networks. Our theoretical derivations and numerical simulations reveal that the proposed estimators are asymptotically efficient, i.e., they attain the CRLB, at relatively high noise levels. Moreover, the simulation results show that the proposed methods outperform state-of-the-art algorithms.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"569-580"},"PeriodicalIF":3.2,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62684554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Game-Theoretic Distributed Empirical Risk Minimization With Strategic Network Design 基于策略网络设计的博弈论分布式经验风险最小化

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-08-17 DOI: 10.1109/TSIPN.2023.3306106

Shutian Liu;Tao Li;Quanyan Zhu

{"title":"Game-Theoretic Distributed Empirical Risk Minimization With Strategic Network Design","authors":"Shutian Liu;Tao Li;Quanyan Zhu","doi":"10.1109/TSIPN.2023.3306106","DOIUrl":"10.1109/TSIPN.2023.3306106","url":null,"abstract":"This article considers a game-theoretic framework for distributed empirical risk minimization (ERM) problems over networks where the information acquisition at a node is modeled as a rational choice of a player. In the proposed game, players decide both the learning parameters and the network structure. The Nash equilibrium (NE) characterizes the tradeoff between the local performance and the global agreement of the learned classifiers. We first introduce an interleaved approach that features a joint learning process that integrates the iterative learning at each node with the network formation. We show that our game is equivalent to a generalized potential game in the setting of undirected networks. We study the convergence of the proposed interleaved algorithm, analyze the network structures determined by our game, and show the improvement of social welfare compared to a standard distributed ERM over fixed networks. To adapt our framework to streaming data, we derive a distributed Kalman filter. A concurrent algorithm based on the online mirror descent algorithm is also introduced to solve for NE in a holistic manner. In the case study, we use data from telemonitoring of Parkinson's disease to corroborate the results.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"542-556"},"PeriodicalIF":3.2,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62684566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A Nonconvex Low Rank and Sparse Constrained Multiview Subspace Clustering via $l_{frac{1}{2}}$-Induced Tensor Nuclear Norm 基于$l_｛frac｛1｝｛2｝｝$诱导张量核范数的非凸低秩稀疏约束多视图子空间聚类

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-08-17 DOI: 10.1109/TSIPN.2023.3306098

Jobin Francis;Baburaj Madathil;Sudhish N. George;Sony George

{"title":"A Nonconvex Low Rank and Sparse Constrained Multiview Subspace Clustering via $l_{frac{1}{2}}$-Induced Tensor Nuclear Norm","authors":"Jobin Francis;Baburaj Madathil;Sudhish N. George;Sony George","doi":"10.1109/TSIPN.2023.3306098","DOIUrl":"10.1109/TSIPN.2023.3306098","url":null,"abstract":"In the realm of clustering of multi-view data, many of the clustering methods, generate view-specific representations for individual views and conjoin them for final grouping. However, in most of the cases,such methods fail to effectively discover the underlying complementary information and higher order correlations present in a multi-view data. Unlike many of the existing works, this paper proposes a nonconvex low rank tensor approximation based clustering framework for multi-view data, relying on the self-expressiveness property of free submodules. Instead of creating individual representation for each view, the proposed method creates a single optimal representation tensor for all the submodules, with a low tensor rank and an f-diagonal structure. The \u0000<inline-formula><tex-math>$l_{frac{1}{2}}$</tex-math></inline-formula>\u0000-induced Tensor Nuclear Norm (TNN) incorporated as a low tensor rank constraint, improves the low rankness of the representation tensor. In addition, a structural constraint is integrated into the proposed method by means of a dissimilarity matrix with \u0000<inline-formula><tex-math>$l_{frac{1}{2}}$</tex-math></inline-formula>\u0000 regularization. Furthermore, the proposed dissimilarity matrix is capable of extracting complementary information and higher order correlations underneath each lateral slice more effectively. The clustering efficiency of the proposed method was evaluated using popular evaluation measures on several challenging multi-view datasets. Experimental results of the proposed method were compared to state-of-the-art single-view and multi-view clustering methods. The compared results demonstrate the improved performance of the proposed method over the existing clustering methods.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"612-625"},"PeriodicalIF":3.2,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62684492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multivariate Time Series Forecasting With GARCH Models on Graphs 基于图上GARCH模型的多变量时间序列预测

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-08-10 DOI: 10.1109/TSIPN.2023.3304142

Junping Hong;Yi Yan;Ercan Engin Kuruoglu;Wai Kin Chan

{"title":"Multivariate Time Series Forecasting With GARCH Models on Graphs","authors":"Junping Hong;Yi Yan;Ercan Engin Kuruoglu;Wai Kin Chan","doi":"10.1109/TSIPN.2023.3304142","DOIUrl":"10.1109/TSIPN.2023.3304142","url":null,"abstract":"Data that house topological information is manifested as relationships between multiple variables via a graph formulation. Various methods have been developed for analyzing time series on the nodes of graphs but research works on graph signals with volatility are limited. In this article, we propose a graph framework of multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models from the spectral perspective with the Laplacian matrix. We introduce three graphical GARCH models: one symmetric Graph GARCH model and two asymmetric models namely Graph Exponential GARCH and Graph GJR-GARCH. Assuming that graph signals and their residuals are \u0000<italic>graph stationary</i>\u0000, this framework can decompose the multivariate GARCH models into a linear combination of several univariate GARCH processes in the graph spectral domain. Moreover, it is possible to reduce the number of parameters with the graph topology information and further reduce the estimation cost by utilizing the principal components of the graph signal in the frequency domain. These proposed models are tested on synthetic data and on two real applications for weather prediction and wind power forecasting. With the data and GARCH model residuals being graph stationary, the experiment results demonstrate that these three graphical models can make multi-step predictions more accurately than non-graph GARCH models and Graph Vector Autoregressive Moving Average model.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"557-568"},"PeriodicalIF":3.2,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62684485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Linearly Convergent Optimization Framework for Learning Graphs From Smooth Signals 从光滑信号学习图的线性收敛优化框架

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-08-10 DOI: 10.1109/TSIPN.2023.3295770

Xiaolu Wang;Chaorui Yao;Anthony Man-Cho So

{"title":"A Linearly Convergent Optimization Framework for Learning Graphs From Smooth Signals","authors":"Xiaolu Wang;Chaorui Yao;Anthony Man-Cho So","doi":"10.1109/TSIPN.2023.3295770","DOIUrl":"10.1109/TSIPN.2023.3295770","url":null,"abstract":"Learning graph structures from a collection of smooth graph signals is a fundamental problem in data analysis and has attracted much interest in recent years. Although various optimization formulations of the problem have been proposed in the literature, existing methods for solving them either are not practically efficient or lack strong convergence guarantees. In this article, we consider a unified graph learning formulation that captures a wide range of static and time-varying graph learning models and develop a first-order method for solving it. By showing that the set of Karush-Kuhn-Tucker points of the formulation possesses a so-called \u0000<italic>error bound property</i>\u0000, we establish the linear convergence of our proposed method. Moreover, through extensive numerical experiments on both synthetic and real data, we show that our method exhibits sharp linear convergence and can be substantially faster than a host of other existing methods. To the best of our knowledge, our work is the first to develop a first-order method that not only is practically efficient but also enjoys a linear convergence guarantee when applied to a large class of graph learning models.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"490-504"},"PeriodicalIF":3.2,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62684843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Decentralized Eigendecomposition for Online Learning Over Graphs With Applications 分布式特征分解在图上的在线学习与应用

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-08-07 DOI: 10.1109/TSIPN.2023.3302658

Yufan Fan;Minh Trinh-Hoang;Cemil Emre Ardic;Marius Pesavento

{"title":"Decentralized Eigendecomposition for Online Learning Over Graphs With Applications","authors":"Yufan Fan;Minh Trinh-Hoang;Cemil Emre Ardic;Marius Pesavento","doi":"10.1109/TSIPN.2023.3302658","DOIUrl":"10.1109/TSIPN.2023.3302658","url":null,"abstract":"In this article, the problem of decentralized eigenvalue decomposition of a general symmetric matrix that is important, e.g., in Principal Component Analysis, is studied, and a decentralized online learning algorithm is proposed. Instead of collecting all information in a fusion center, the proposed algorithm involves only local interactions among adjacent agents. It benefits from the representation of the matrix as a sum of rank-one components which makes the algorithm attractive for online eigenvalue and eigenvector tracking applications. We examine the performance of the proposed algorithm in two types of important application examples: First, we consider the online eigendecomposition of a sample covariance matrix over the network, with application in decentralized Direction-of-Arrival (DoA) estimation and DoA tracking applications. Then, we investigate the online computation of the spectra of the graph Laplacian that is important in, e.g., Graph Fourier Analysis and graph dependent filter design. We apply our proposed algorithm to track the spectra of the graph Laplacian in static and dynamic networks. Simulation results reveal that the proposed algorithm outperforms existing decentralized algorithms both in terms of estimation accuracy as well as communication cost.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"505-520"},"PeriodicalIF":3.2,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44835367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Information Fusion via Importance Sampling 通过重要度采样进行信息融合

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-08-07 DOI: 10.1109/TSIPN.2023.3299512

Augustin A. Saucan;Víctor Elvira;Pramod K. Varshney;Moe Z. Win

{"title":"Information Fusion via Importance Sampling","authors":"Augustin A. Saucan;Víctor Elvira;Pramod K. Varshney;Moe Z. Win","doi":"10.1109/TSIPN.2023.3299512","DOIUrl":"10.1109/TSIPN.2023.3299512","url":null,"abstract":"Information fusion is a procedure that merges information locally contained at the nodes of a network. Of high interest in the field of distributed estimation is the fusion of local probability distributions via a weighted geometrical average criterion. In numerous practical settings, the local distributions are only known through particle approximations, i.e., sets of samples with associated weights, such as obtained via importance sampling (IS) methods. Thus, prohibiting any closed-form solution to the aforementioned fusion problem. This article proposes a family of IS methods—called particle geometric–average fusion (PGAF)—that lead to consistent estimators for the geometrically-averaged density. The advantages of the proposed methods are threefold. First, the methods are agnostic of the mechanisms used to generate the local particle sets and, therefore, allow for the fusion of heterogeneous nodes. Second, consistency of estimators is guaranteed under generic conditions when the agents use IS-generated particles. Third, a low-communication overhead and agent privacy are achieved since local observations are not shared with the fusion center. Even more remarkably, for a sub-family of the proposed PGAF methods, the fusion center does not require the knowledge of the local priors used by the nodes. Implementation guidelines for the proposed methods are provided and theoretical results are numerically verified.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"10 ","pages":"376-389"},"PeriodicalIF":3.2,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62684474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CBDS2R: A Cluster-Based Depth Source Selection Routing for Underwater Wireless Sensor Network CBDS2R:一种基于集群的水下无线传感器网络深度源选择路由

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-07-28 DOI: 10.1109/TSIPN.2023.3299108

Shahrokh Vahabi;Ali Daneshvar;Mohammadreza Eslaminejad;Seyed Ebrahim Dashti

{"title":"CBDS2R: A Cluster-Based Depth Source Selection Routing for Underwater Wireless Sensor Network","authors":"Shahrokh Vahabi;Ali Daneshvar;Mohammadreza Eslaminejad;Seyed Ebrahim Dashti","doi":"10.1109/TSIPN.2023.3299108","DOIUrl":"10.1109/TSIPN.2023.3299108","url":null,"abstract":"Underwater wireless sensor network (UWSN) is one of the kinds of wireless sensor network (WSN). This type of network is suitable for underwater areas such as pools, rivers, seas, and oceans. In UWSN, the energy of nodes is more depletes compared to WSN. As more energy in nodes depletes for transmitting data, therefore routing is the most important issue for UWSN. Sensor nodes in water use acoustic waves to transmit data packets contrary to sensor nodes in WSN which are used radio waves for this purpose, hence the link quality of the acoustic and radio waves is different. Therefore, it is impossible to use routing methods and protocols based on WSN for UWSN. This article focuses on routing in UWSN and proposes a depth source selection phase via link quality between sensor nodes with mobile sink(s) for improving energy-saving and network lifetime. The new proposed algorithm contains six phases are as follows: network architecture, calculating link quality, clustering, source selection, mobile sink mechanism, and transmitting data packets phase. Also, the new approach is suitable for small and large networks. Results of experimental simulation clearly show that this new proposed algorithm improves residual energy and network lifetime by at least 40.28% and 58.88% respectively.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"468-476"},"PeriodicalIF":3.2,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62684890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SVD-Based Graph Fourier Transforms on Directed Product Graphs 基于SVD的有向积图的图傅立叶变换

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-07-27 DOI: 10.1109/TSIPN.2023.3299511

Cheng Cheng;Yang Chen;Yeon Ju Lee;Qiyu Sun

引用次数: 0

Fault-Tolerant Finite-Time Consensus of Multi-Agent Systems Under Asynchronous Self-Sensing Function Failures 异步自感功能失效下多Agent系统的容错有限时间一致性

IF 3.2 3区计算机科学

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2023-07-26 DOI: 10.1109/TSIPN.2023.3299079

Zhihai Wu;Shun Jiang;Linbo Xie

{"title":"Fault-Tolerant Finite-Time Consensus of Multi-Agent Systems Under Asynchronous Self-Sensing Function Failures","authors":"Zhihai Wu;Shun Jiang;Linbo Xie","doi":"10.1109/TSIPN.2023.3299079","DOIUrl":"10.1109/TSIPN.2023.3299079","url":null,"abstract":"This article investigates fault-tolerant finite-time consensus (FTC) problems of single/double-integrator multi-agent systems (MASs) with partial agents subject to asynchronous self-sensing function failures (SSFFs). First, the strategy named DRMNNS is developed to recover the connectivity of network topology among normal agents by converting asynchronous SSFFs into multiple piecewise synchronous SSFFs and using multi-hop communication (MHC) together with agents subject to SSFFs as routing nodes. Second, by employing the state and input information of all agents in minimum-hop normal neighbor set (MHNNS) of an agent subject to SSFF and utilizing the history information of the agent subject to SSFF for computing its state information at the instants when its MHNNS changes, two switching fault-tolerant FTC protocols with single/double time-varying gains are designed, respectively, for single/double-integrator MASs. Third, convergence analysis is carried out by separately investigating the closed-loop dynamics of normal agents and the open-loop dynamics of agents subject to SSFFs, and convergence conditions in terms of time-varying gains are derived. It turns out that single/double-integrator MASs under asynchronous SSFFs using the proposed DRMNNS strategy and two fault-tolerant FTC protocols with proper time-varying gains can reach FTC/finite-time dynamical consensus (FTDC), respectively. Finally, comparison numerical simulations are provided to illustrate the effectiveness of the theoretical results.","PeriodicalId":56268,"journal":{"name":"IEEE Transactions on Signal and Information Processing over Networks","volume":"9 ","pages":"477-489"},"PeriodicalIF":3.2,"publicationDate":"2023-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62684852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0