样式: 排序: IF: - GO 导出 标记为已读
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Social Learning in Community Structured Graphs IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-05-15 Valentina Shumovskaia, Mert Kayaalp, Ali H. Sayed
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Sequential Joint Detection and Estimation: Optimal Average Stopping Time IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-05-15 Jilong Lyu, Enbin Song, Zhi Li, Jinming Liu, Yiguang Liu, Juping Gu, Qingjiang Shi
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Event-triggered Proximal Online Gradient Descent Algorithm for Parameter Estimation IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-05-13 Yaoyao Zhou, Gang Chen
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Velocity Profiling of a Distributed Target in Fluctuating SNR Environments IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-05-13 Akankshya Bhatta, Sibi Raj B. Pillai, T. V. C. Sarma, Satish Mulleti
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On optimal tracking of rapidly varying telecommunication channels IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-05-13 Maciej Niedźwiecki, Artur Gańcza
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Convergence analysis of an adaptively regularized natural gradient method IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-05-09 Jiayuan Wu, Jiang Hu, Hongchao Zhang, Zaiwen Wen
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Multiple-level Green Noise Mask Design for Practical Fourier Phase Retrieval IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-05-09 Qiuliang Ye, Bingo Wing-Kuen Ling, Li-Wen Wang, Daniel Pak-Kong Lun
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Localization of a Passive Object in 3-D by Propagation Time Delays to A Rigid Body Receiver of Unknown Location IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-05-08 Xiaochuan Ke, K. C. Ho
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Adaptive Binning Coincidence Test for Uniformity Testing IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-05-07 Sudeep Salgia, Xinyi Wang, Qing Zhao, Lang Tong
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An efficient analytic solution for joint blind source separation IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-05-07 Ben Gabrielson, M. A. B. S. Akhonda, Isabell Lehmann, Tülay Adali
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Robust Multidimentional Chinese Remainder Theorem for Integer Vector Reconstruction IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-05-03 Li Xiao, Haiye Huo, Xiang-Gen Xia
The problem of robustly reconstructing an integer vector from its erroneous remainders appears in many applications in the field of multidimensional (MD) signal processing. To address this problem, a robust MD Chinese remainder theorem (CRT) was recently proposed for a special class of moduli, where the remaining integer matrices left-divided by a greatest common left divisor (gcld) of all the moduli
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Inverse Unscented Kalman Filter IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-05-03 Himali Singh, Kumar Vijay Mishra, Arpan Chattopadhyay
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Barycenter Calibration with High Order Spectra of Windowed Delay-Doppler Signals for OTFS based ISAC Systems IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-05-01 Yiran Yang, Yuchen Pan, Xiqing Liu, Mugen Peng
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Learning Sparse High-Dimensional Matrix-Valued Graphical Models From Dependent Data IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-05-01 Jitendra K. Tugnait
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Kernel Based Reconstruction for Generalized Graph Signal Processing IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-30 Xingchao Jian, Wee Peng Tay, Yonina C. Eldar
In generalized graph signal processing (GGSP), the signal associated with each vertex in a graph is an element from a Hilbert space. In this paper, we study GGSP signal reconstruction as a kernel ridge regression (KRR) problem. By devising an appropriate kernel, we show that this problem has a solution that can be evaluated in a distributed way. We interpret the problem and solution using both deterministic
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Locally Stationary Graph Processes IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-29 Abdullah Canbolat, Elif Vural
Stationary graph process models are commonly used in the analysis and inference of data sets collected on irregular network topologies. While most of the existing methods represent graph signals with a single stationary process model that is globally valid on the entire graph, in many practical problems, the characteristics of the process may be subject to local variations in different regions of the
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Enhancing the Accuracy of 6T SRAM-based In-Memory Architecture via Maximum Likelihood Detection IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-29 Hyungyo Kim, Naresh Shanbhag
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Measuring Strength of Joint Causal Effects IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-29 Kurt Butler, Guanchao Feng, Petar M. Djurić
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Outliers Detection by Signal Subspace Matching IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-29 Mati Wax, Amir Adler
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Cluster Head Detection for Hierarchical UAV Swarm With Graph Self-supervised Learning IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-29 Zhiyu Mou, Feifei Gao, Jun Liu, Xiang Yun, Qihui Wu
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Favor the Tortoise over the Hare: An Efficient Detection Algorithm for Cooperative Networks IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-29 Allan E. Feitosa, Vítor H. Nascimento, Cassio G. Lopes
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Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-Offs IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-25 Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
This paper studies the relationship between a graph neural network (GNN) and a manifold neural network (MNN) when the graph is constructed from a set of points sampled from the manifold, thus encoding geometric information. We consider convolutional MNNs and GNNs where the manifold and the graph convolutions are respectively defined in terms of the Laplace-Beltrami operator and the graph Laplacian
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Constant Modulus Waveform Estimation and Interference Suppression via Two-stage Fractional Program-based Beamforming IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-25 Junli Liang, Tao Wang, Wei Liu, H. C. So, Yongwei Huang, Bo Tang
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Explainable Gated Bayesian Recurrent Neural Network for Non-Markov State Estimation IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-24 Shi Yan, Yan Liang, Le Zheng, Mingyang Fan, Xiaoxu Wang, Binglu Wang
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Novel KLD-based Resource Allocation for Integrated Sensing and Communication IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-23 Yousef Kloob, Mohammad Al-Jarrah, Emad Alsusa, Christos Masouros
In this paper, we introduce a novel resource allocation approach for integrated sensing-communication (ISAC) using the Kullback–Leibler divergence (KLD) metric. Specifically, we consider a base-station with limited power and antenna resources serving a number of communication users and detecting multiple targets simultaneously. First, we analyse the KLD for two possible antenna deployments, which are
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New Penalized Criteria for Smooth Non-Negative Tensor Factorization With Missing Entries IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-23 Amaury Durand, François Roueff, Jean-Marc Jicquel, Nicolas Paul
Tensor factorization models are widely used in many applied fields such as chemometrics, psychometrics, computer vision or communication networks. Real life data collection is often subject to errors, resulting in missing data. Here we focus in understanding how this issue should be dealt with for non-negative tensor factorization. We investigate several criteria used for non-negative tensor factorization
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Joint Network Topology Inference in the Presence of Hidden Nodes IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-23 Madeline Navarro, Samuel Rey, Andrei Buciulea, Antonio G. Marques, Santiago Segarra
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A Communication-Efficient Algorithm for Federated Multilevel Stochastic Compositional Optimization IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-23 Shuoguang Yang, Fengpei Li
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Online Optimization Under Randomly Corrupted Attacks IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-23 Zhihai Qu, Xiuxian Li, Li Li, Xinlei Yi
Existing algorithms in online optimization usually rely on trustful information, e.g., reliable knowledge of gradients, which makes them vulnerable to attacks. To take into account the security issue in online optimization, this paper investigates the effect of randomly corrupted attacks, which can corrupt gradient information arbitrarily. To conquer the randomly corrupted attack, an on L ine mu L
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Multi-dimensional Resource Management Scheme for Multiple Target Tracking under Dynamic Electromagnetic Environment IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-18 Peng Zhang, Junkun Yan, Wenqiang Pu, Hongwei Liu, Maria S. Greco
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Near-Field Wideband Secure Communications: An Analog Beamfocusing Approach IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-17 Yuchen Zhang, Haiyang Zhang, Sa Xiao, Wanbin Tang, Yonina C. Eldar
In the rapidly advancing landscape of 6G, characterized by ultra-high-speed wideband transmission in millimeter-wave and terahertz bands, our paper addresses the pivotal task of enhancing physical layer security (PLS) within near-field wideband communications. We introduce true-time delayer (TTD)-incorporated analog beamfocusing techniques designed to address the interplay between near-field propagation
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6-DoF Location-and-Pose Estimation towards Integrated Visible Light Communication and Sensing: Algorithm Design and Performance Limits IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-16 Bingpeng Zhou, Xin Wang, Yuan Shen, Pingzhi Fan
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Matrix Approximation With Side Information: When Column Sampling Is Enough IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-15 Jeongmin Chae, Praneeth Narayanamurthy, Selin Bac, Shaama Mallikarjun Sharada, Urbashi Mitra
A novel matrix approximation problem is considered herein: observations based on a few fully sampled columns and quasi-polynomial structural side information are exploited. The framework is motivated by quantum chemistry problems wherein full matrix computation is expensive, and partial computations only lead to column information. The proposed algorithm successfully estimates the column and row space
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Source Enumeration Utilizing Adaptive Diagonal Loading and Linear Shrinkage Coefficients IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-11 Ye Tian, Zhicheng Zhang, Wei Liu, Hua Chen, Gang Wang
Source enumeration is typically studied under the assumption of white noise, which may not be suitable for real-world applications. In this article, a source enumeration algorithm robust against both white and colored noises is presented, where the adaptive diagonal loading (ADL) technique combined with linear shrinkage (LS) coefficients are employed. First, a proper loading level is adaptively determined
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Radar Waveform Design based on Target Pattern Separability via Fractional Programming IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-11 Jiahang Wang, Junli Liang, Zhiwei Cheng, Hing Cheung So, Shengqi Zhu, Jingwei Xu
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ROCS: Robust One-Bit Compressed Sensing with Application to Direction of Arrival IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-11 Xiao-Peng Li, Zhang-Lei Shi, Lei Huang, Anthony Man-Cho So, Hing Cheung So
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On Properties and Structure of the Analytic Singular Value Decomposition IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-11 Stephan Weiss, Ian K. Proudler, Giovanni Barbarino, Jennifer Pestana, John G. McWhirter
We investigate the singular value decomposition (SVD) of a rectangular matrix $\boldsymbol{\mathit{A}}(z)$ of functions that are analytic on an annulus that includes at least the unit circle. Such matrices occur, e.g., as matrices of transfer functions representing broadband multiple-input multiple-output systems. Our analysis is based on findings for the analytic SVD applicable to continuous time
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Adaptive Local Modularity Learning for Efficient Multilayer Graph Clustering IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-10 Danyang Wu, Penglei Wang, Junjie Liang, Jitao Lu, Jin Xu, Rong Wang, Feiping Nie
Existing multilayer graph clustering models focus on integrating the monolithic structure of each layer, but the local preference between layers and clusters has not been fully exploited. To alleviate this problem, this paper proposes a novel multilayer graph clustering model with Adaptive Local Modularity Learning (ALML), which mines truncated layer-cluster relationships adaptively with graph modularity
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BayGO: Decentralized Bayesian Learning and Information-Aware Graph Optimization Framework IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-10 Tamara AlShammari, Chathuranga Weeraddana, Mehdi Bennis
Multi-agent Decentralized Learning (MADL) is a scalable approach that enables agents to learn based on their local datasets. However, it presents significant challenges related to the impact of dataset heterogeneity and the communication graph structure on learning speed, as well as the lack of a robust method for quantifying prediction uncertainty. To address these challenges, we propose BayGO, a
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Accounting for Vibration Noise in Stochastic Measurement Errors of Inertial Sensors IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-10 Mucyo Karemera, Lionel Voirol, Davide A. Cucci, Wenfei Chu, Roberto Molinari, Stéphane Guerrier
The measurement of data over time and/or space is of utmost importance in a wide range of domains from engineering to physics. Devices that perform these measurements, such as inertial sensors, need to be extremely precise to obtain correct system diagnostics and accurate predictions, consequently requiring a rigorous calibration procedure before being employed. Most of the research over the past years
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Variance Reduced Random Relaxed Projection Method for Constrained Finite-Sum Minimization Problems IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-09 Zhichun Yang, Fu-quan Xia, Kai Tu, Man-Chung Yue
For many applications in signal processing and machine learning, we are tasked with minimizing a large sum of convex functions subject to a large number of convex constraints. In this paper, we devise a new random projection method (RPM) to efficiently solve this problem. Compared with existing RPMs, our proposed algorithm features two useful algorithmic ideas. First, at each iteration, instead of
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Neural Augmented Kalman Filtering With Bollinger Bands for Pairs Trading IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-08 Amit Milstein, Guy Revach, Haoran Deng, Hai Morgenstern, Nir Shlezinger
Pairs trading is a family of trading techniques that determine their policies based on monitoring the relationships between pairs of assets. A common pairs trading approach relies on describing the pairwise relationship as a linear Space State (SS) model with Gaussian noise. This representation facilitates extracting financial indicators with low complexity and latency using a Kalman Filter (KF), which
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Distributed Policy Gradient for Linear Quadratic Networked Control With Limited Communication Range IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-08 Yuzi Yan, Yuan Shen
This paper proposes a scalable distributed policy gradient method and proves its convergence to near-optimal solution in multi-agent linear quadratic networked systems. The agents engage within a specified network under local communication constraints, implying that each agent can only exchange information with a limited number of neighboring agents. On the underlying graph of the network, each agent
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Non-Uniform Array and Frequency Spacing for Regularization-Free Gridless DOA IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-08 Yifan Wu, Michael B. Wakin, Peter Gerstoft
Gridless direction-of-arrival (DOA) estimation with multiple frequencies can be applied in acoustics source localization problems. We formulate this as an atomic norm minimization (ANM) problem and derive an equivalent regularization-free semi-definite program (SDP) thereby avoiding regularization bias. The DOA is retrieved using a Vandermonde decomposition on the Toeplitz matrix obtained from the
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Distributed Inference With Variational Message Passing in Gaussian Graphical Models: Tradeoffs in Message Schedules and Convergence Conditions IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-08 Bin Li, Nan Wu, Yik-Chung Wu
Message passing algorithms on graphical models offer a low-complexity and distributed paradigm for performing marginalization from a high-dimensional distribution. However, the convergence behaviors of message passing algorithms can be heavily affected by the adopted message update schedule. In this paper, we focus on the variational message passing (VMP) applied to Gaussian graphical models and its
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Adaptive Step-Size Methods for Compressed SGD with Memory Feedback IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-05 Adarsh M. Subramaniam, Akshayaa Magesh, Venugopal V. Veeravalli
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Detection of Ghost Targets for Automotive Radar in the Presence of Multipath IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-04 Le Zheng, Jiamin Long, Marco Lops, Fan Liu, Xueyao Hu, Chuanhao Zhao
Colocated multiple-input multiple-output (MIMO) technology has been widely used in automotive radars as it provides accurate angular estimation of the objects with a relatively small number of transmitting and receiving antennas. Since the Direction Of Departure (DOD) and the Direction Of Arrival (DOA) of line-of-sight targets coincide, MIMO signal processing allows for the formation of a larger virtual
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Multi-Resolution Model Compression for Deep Neural Networks: A Variational Bayesian Approach IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-02 Chengyu Xia, Huayan Guo, Haoyu Ma, Danny H. K. Tsang, Vincent K. N. Lau
The continuously growing size of deep neural networks (DNNs) has sparked a surge in research on model compression techniques. Among these techniques, multi-resolution model compression has emerged as a promising approach which can generate multiple DNN models with shared weights and different computational complexity (resolution) through a single training. However, in most existing multi-resolution
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Spatial Registration of Heterogeneous Sensors on Mobile Platforms IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-01 Yajun Zeng, Jun Wang, Shaoming Wei, Jinping Sun, Peng Lei, Yvon Savaria, Chi Zhang
Accurate georegistration is required in multi-sensor data fusion, since even minor biases in spatial registration can result in large errors in the converted target geolocation. This paper addresses the problem of estimating and correcting sensor biases in target geolocation. Aiming to solve the spatial registration problem in the case where heterogeneous measurements are provided by mobile sensor
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Set-Type Belief Propagation With Applications to Poisson Multi-Bernoulli SLAM IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-04-01 Hyowon Kim, Ángel F. García-Fernández, Yu Ge, Yuxuan Xia, Lennart Svensson, Henk Wymeersch
Belief propagation (BP) is a useful probabilistic inference algorithm for efficiently computing approximate marginal probability densities of random variables. However, in its standard form, BP is only applicable to the vector-type random variables with a fixed and known number of vector elements, while certain applications rely on random finite sets (RFSs) with an unknown number of vector elements
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DANSE: Data-Driven Non-Linear State Estimation of Model-Free Process in Unsupervised Learning Setup IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-29 Anubhab Ghosh, Antoine Honoré, Saikat Chatterjee
We address the tasks of Bayesian state estimation and forecasting for a model-free process in an unsupervised learning setup. For a model-free process, we do not have any a-priori knowledge of the process dynamics. In the article, we propose DANSE – a Da ta-driven N onlinear S tate E stimation method. DANSE provides a closed-form posterior of the state of the model-free process, given linear measurements
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A New Statistic for Testing Covariance Equality in High-Dimensional Gaussian Low-Rank Models IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-28 Rémi Beisson, Pascal Vallet, Audrey Giremus, Guillaume Ginolhac
In this paper, we consider the problem of testing equality of the covariance matrices of $L$ complex Gaussian multivariate time series of dimension $M$ . We study the special case where each of the $L$ covariance matrices is modeled as a rank $K$ perturbation of the identity matrix, corresponding to a signal plus noise model. A new test statistic based on the estimates of the eigenvalues of the different
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Ultimately Bounded State Estimation for Nonlinear Networked Systems With Constrained Average Bit Rate: A Buffer-Aided Strategy IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-28 Jie Sun, Bo Shen, Lei Zou
This article investigates the state estimation issue for a nonlinear networked system with network-based communication, where the measurement signals of the system are transmitted in an intermittent manner under the effects of unreliable communication. For the sake of enhancing the utilization efficiency of measurement signals, a buffer-aided strategy is employed here by storing historical measurement
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Blind Graph Matching Using Graph Signals IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-28 Hang Liu, Anna Scaglione, Hoi-To Wai
Classical graph matching aims to find a node correspondence between two unlabeled graphs of known topologies. This problem has a wide range of applications, from matching identities in social networks to identifying similar biological network functions across species. However, when the underlying graphs are unknown, the use of conventional graph matching methods requires inferring the graph topologies
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Samplet Basis Pursuit: Multiresolution Scattered Data Approximation With Sparsity Constraints IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-28 Davide Baroli, Helmut Harbrecht, Michael Multerer
We consider scattered data approximation in samplet coordinates with $\ell_{1}$ -regularization. The application of an $\ell_{1}$ -regularization term enforces sparsity of the coefficients with respect to the samplet basis. Samplets are wavelet-type signed measures, which are tailored to scattered data. Therefore, samplets enable the use of well-established multiresolution techniques on general scattered
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Optimal Bayesian Regression With Vector Autoregressive Data Dependency IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-27 Samira Reihanian, Edward R. Dougherty, Amin Zollanvari
In this study, we derive a closed-form analytic representation of the optimal Bayesian regression when the data are generated from $\text{VAR}(p)$ , which is a multidimensional vector autoregressive process of order $p$ . Given the covariance matrix of the underlying Gaussian white-noise process, the developed regressor reduces to the conventional optimal regressor for a non-informative prior and setting
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Topology Inference of Directed Graphs by Gaussian Processes With Sparsity Constraints IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-26 Chen Cui, Paolo Banelli, Petar M. Djurić
In machine learning applications, data are often high-dimensional and intricately related. It is often of interest to find the underlying structure and Granger causal relationships among the data and represent these relationships with directed graphs. In this paper, we study multivariate time series, where each series is associated with a node of a graph, and where the objective is to estimate the
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Ziv-Zakai Bound for 2D-DOAs Estimation IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-25 Zongyu Zhang, Zhiguo Shi, Cunqi Shao, Jiming Chen, Maria Sabrina Greco, Fulvio Gini
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Sparse Modeling for Spectrometer Based on Band Measurement IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-25 Kyoya Uemura, Tomoyuki Obuchi, Toshiyuki Tanaka
In typical spectrometric measurement systems, a high-resolution spectrum is obtained directly via sequential observations with a narrow slit-like measurement window at the expense of sensitivity. In this paper, we propose a novel spectrometric method applicable to these typical spectrometric systems: a multiplexed low-resolution measurement with a wide measurement window, band measurement (BM), is
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Multivariate Selfsimilarity: Multiscale Eigen-Structures for Selfsimilarity Parameter Estimation IEEE Trans. Signal Process. (IF 5.4) Pub Date : 2024-03-25 Charles-Gérard Lucas, Gustavo Didier, Herwig Wendt, Patrice Abry
Scale-free dynamics, formalized by selfsimilarity, provides a versatile paradigm massively and ubiquitously used to model temporal dynamics in real-world data. However, its practical use has mostly remained univariate so far. By contrast, modern applications often demand multivariate data analysis. Accordingly, models for multivariate selfsimilarity were recently proposed. Nevertheless, they have remained