IEEE Transactions on Signal Processing最新文献_第3页

Robust 3-D AOA Localization Based on Maximum Correntropy Criterion With Variable Center 基于可变中心最大熵准则的鲁棒三维自动对地仪定位系统

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-10-28 DOI: 10.1109/TSP.2024.3486817

Keyuan Hu;Wenxin Xiong;Zhi-Yong Wang;Hing Cheung So;Chi-Sing Leung

{"title":"Robust 3-D AOA Localization Based on Maximum Correntropy Criterion With Variable Center","authors":"Keyuan Hu;Wenxin Xiong;Zhi-Yong Wang;Hing Cheung So;Chi-Sing Leung","doi":"10.1109/TSP.2024.3486817","DOIUrl":"10.1109/TSP.2024.3486817","url":null,"abstract":"This contribution investigates the problem of three-dimensional (3-D) angle-of-arrival (AOA) source localization (SL) in the presence of symmetric \u0000<inline-formula><tex-math>$alpha$</tex-math></inline-formula>\u0000-stable (\u0000<inline-formula><tex-math>$mathcal{Salpha S}$</tex-math></inline-formula>\u0000) impulsive noise for \u0000<inline-formula><tex-math>$alphain(0,2]$</tex-math></inline-formula>\u0000. The azimuth and elevation angle measurements are initially rewritten into a pseudolinear form using spherical coordinate conversion, thereby making them more manageable. Subsequently, we adopt the maximum correntropy criterion with variable center (MCC-VC) to devise a robust 3-D AOA location estimator that functions effectively without the prior knowledge of parameters governing the impulsiveness and dispersion of \u0000<inline-formula><tex-math>$mathcal{Salpha S}$</tex-math></inline-formula>\u0000 noise distributions. While it gives rise to a straightforward alternating minimization algorithmic framework, our analysis reveals that solely embracing MCC-VC leads to bias issues stemming from the correlation between the measurement matrix and noise. Aiming at addressing such a challenge, we introduce instrumental variables (IVs) to develop a bias-reduced maximum correntropy criterion (MCC) estimator, termed MCC with IV (MCC-IV). Simulation results illustrate a considerable performance enhancement of MCC-IV compared to existing schemes for 3-D AOA SL, particularly in achieving mean square error much closer to the Cramér–Rao lower bound and mitigating bias substantially.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5021-5035"},"PeriodicalIF":4.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Spectrum Sensing and DOA Estimation Based on A Resource-Efficient Sub-Nyquist Array Receiver 基于资源节约型亚奈奎斯特阵列接收器的联合频谱传感和 DOA 估算

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-10-28 DOI: 10.1109/tsp.2024.3487256

Liang Liu, Zhan Zhang, Xinyun Zhang, Ping Wei, Jiancheng An, Hongbin Li

引用次数: 0

Stochastic Bandits With Non-Stationary Rewards: Reward Attack and Defense 非固定奖励的随机强盗：奖励攻防

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-10-25 DOI: 10.1109/TSP.2024.3486240

Chenye Yang;Guanlin Liu;Lifeng Lai

引用次数: 0

A Coordinate Descent Approach to Atomic Norm Denoising 原子规范去噪的坐标后裔方法

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-10-25 DOI: 10.1109/TSP.2024.3486533

Ruifu Li;Danijela Cabric

{"title":"A Coordinate Descent Approach to Atomic Norm Denoising","authors":"Ruifu Li;Danijela Cabric","doi":"10.1109/TSP.2024.3486533","DOIUrl":"10.1109/TSP.2024.3486533","url":null,"abstract":"Atomic norm minimization is of great interest in various applications of sparse signal processing including super-resolution line-spectral estimation and signal denoising. In practice, atomic norm minimization (ANM) is formulated as semi-definite programming (SDP) that is generally hard to solve. This work introduces a low-complexity solver for a type of ANM known as atomic norm soft thresholding (AST). The proposed method uses the framework of coordinate descent and exploits the sparsity-inducing nature of atomic norm regularization. Specifically, this work first provides an equivalent, non-convex formulation of AST. It is then proved that applying a coordinate descent algorithm on the non-convex formulation leads to convergence to the global solution. For the case of a single measurement vector of length \u0000<inline-formula><tex-math>$N$</tex-math></inline-formula>\u0000 and complex exponential basis, the complexity of each step in the coordinate descent procedure is \u0000<inline-formula><tex-math>$mathcal{O}(Nlog N)$</tex-math></inline-formula>\u0000, rendering the method efficient for large-scale problems. Through simulations, for sparse problems the proposed solver is shown to be faster than alternating direction method of multiplier (ADMM) or customized interior point SDP solver. Numerical simulations demonstrate that the coordinate descent solver can be modified for AST with multiple dimensions and multiple measurement vectors as well as a variety of other continuous basis.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5077-5090"},"PeriodicalIF":4.6,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142490473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model Pruning for Distributed Learning Over the Air 空中分布式学习的模型剪枝

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-10-24 DOI: 10.1109/tsp.2024.3486169

Zhongyuan Zhao, Kailei Xu, Wei Hong, mugen peng, Zhiguo Ding, Tony Q.S. Quek, Howard H. Yang

引用次数: 0

Towards Inversion-Free Sparse Bayesian Learning: A Universal Approach 实现无反转稀疏贝叶斯学习：通用方法

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-10-23 DOI: 10.1109/TSP.2024.3484908

Yuhui Song;Zijun Gong;Yuanzhu Chen;Cheng Li

{"title":"Towards Inversion-Free Sparse Bayesian Learning: A Universal Approach","authors":"Yuhui Song;Zijun Gong;Yuanzhu Chen;Cheng Li","doi":"10.1109/TSP.2024.3484908","DOIUrl":"10.1109/TSP.2024.3484908","url":null,"abstract":"Sparse Bayesian Learning (SBL) has emerged as a powerful tool for sparse signal recovery, due to its superior performance. However, the practical implementation of SBL faces a significant computational complexity associated with matrix inversion. Despite numerous efforts to alleviate this issue, existing methods are often limited to specifically structured sparse signals. This paper aims to provide a universal inversion-free approach to accelerate existing SBL algorithms. We unify the optimization of SBL variants with different priors within the expectation-maximization (EM) framework, where a lower bound of the likelihood function is maximized. Due to the linear Gaussian model foundation of SBL, updating this lower bound requires maximizing a quadratic function, which involves matrix inversion. Thus, we employ the minorization-maximization (MM) framework to derive two novel lower bounds that diagonalize the quadratic coefficient matrix, thereby eliminating the need for any matrix inversions. We further investigate their properties, including convergence guarantees under the MM framework and the slow convergence rate due to reduced curvature. The proposed approach is applicable to various types of structured sparse signals, such as row-sparse, block-sparse, and burst-sparse signals. Our simulations on synthetic and real data demonstrate remarkably shorter running time compared to state-of-the-art methods while achieving comparable recovery performance.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4992-5006"},"PeriodicalIF":4.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142488489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

New Insights Into Widely Linear MMSE Receivers for Communication Networks Using Data-Like Rectilinear or Quasi-Rectilinear Signals 使用数据类直角坐标或准直角坐标信号的通信网络广泛线性 MMSE 接收器的新见解

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-10-23 DOI: 10.1109/TSP.2024.3479875

Pascal Chevalier;Jean-Pierre Delmas;Roger Lamberti

{"title":"New Insights Into Widely Linear MMSE Receivers for Communication Networks Using Data-Like Rectilinear or Quasi-Rectilinear Signals","authors":"Pascal Chevalier;Jean-Pierre Delmas;Roger Lamberti","doi":"10.1109/TSP.2024.3479875","DOIUrl":"10.1109/TSP.2024.3479875","url":null,"abstract":"Widely linear (WL) processing has been of great interest these last two decades for multi-user (MUI) interference mitigation in radiocommunications networks using rectilinear (R) or quasi-rectilinear (QR) signals in particular. Despite numerous papers on the subject, this topic remains of interest for several current and future applications which use R or QR signals, described hereafter. In this context, using a continuous time approach, it is shown in this paper the sub-optimality of most of the WL MMSE receivers of the literature, which are implemented at the symbol rate after a matched filtering operation to the pulse shaping filter, and the necessity to know the MUI channels, always cumbersome in practice, to implement the optimal WL MMSE receiver. Then, the main challenge addressed in the paper is to propose new WL MMSE receivers able to implement the optimal one without requiring the MUI channels knowledge. For this purpose, two new WL MMSE receivers, a two-input one and a three-input one, are proposed and analyzed in this paper for R and QR signals corrupted by data-like MUI. The two-input and three-input receivers are shown to be quasi-optimal respectively for R signals using Square Root Raised Cosine (SRRC) filters with a low roll-off and for R and QR signals whatever the pulse shaping filter, showing in particular the non-equivalence of R and QR signals for WL MMSE receivers. These two new receivers open new perspectives for the implementation of the optimal WL MMSE receiver in the presence of data-like MUI from the only knowledge of the SOI channel.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5156-5173"},"PeriodicalIF":4.6,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142488491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Prior-Based Channel Estimation And Activity Detection in Cell-Free mMTC Systems 无小区 mMTC 系统中基于先验的新型信道估计和活动检测方法

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-10-23 DOI: 10.1109/tsp.2024.3484949

Anupama Rajoriya, Nakul Singh, Rohit Budhiraja, Ajit K Chaturvedi

引用次数: 0

Modeling Sparse Graph Sequences and Signals Using Generalized Graphons 使用广义图元对稀疏图序列和信号建模

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-10-22 DOI: 10.1109/TSP.2024.3482350

Feng Ji;Xingchao Jian;Wee Peng Tay

引用次数: 0

Towards Applicable Unsupervised Signal Denoising via Subsequence Splitting and Blind Spot Network 通过后续分裂和盲点网络实现适用的无监督信号去噪

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-10-18 DOI: 10.1109/TSP.2024.3483453

Ziqi Wang;Zihan Cao;Julan Xie;Huiyong Li;Zishu He

{"title":"Towards Applicable Unsupervised Signal Denoising via Subsequence Splitting and Blind Spot Network","authors":"Ziqi Wang;Zihan Cao;Julan Xie;Huiyong Li;Zishu He","doi":"10.1109/TSP.2024.3483453","DOIUrl":"10.1109/TSP.2024.3483453","url":null,"abstract":"Denoising is a significant preprocessing process, garnering substantial attention across various signal-processing domains. Many traditional denoising methods assume signal stationary and adherence of noise to Gaussian distribution, thereby limiting their practical applicability. Despite significant advancements in machine learning and deep learning methods, machine learning-based (ML-based) approaches still require manual feature engineering and intricate parameter tuning, and deep learning-based (DL-based) methods, remain largely constrained by supervised denoising techniques. In this paper, we propose an unsupervised denoising approach that addresses the shortcomings of previous methods. Our proposed method uses subsequence splitting and blind spot network to adaptively learn the signal characteristics in different scenarios, so as to achieve the purpose of denoising. The experimental results show that our method performs satisfactorily on both single-sensor and array signal denoising problems under Gaussian white noise and Impulsive noise. Moreover, our method is also verified to be effective on some array signal processing problems of Direction of Arrival (DOA) estimation, Estimated Number of Sources, and Spatial Spectrum estimation. Finally, in the discussion experiments and generalization experiments, we demonstrate that our method performs well across a wide variety of array forms and degrees of signal correlation, and has good generalization. Our code will be released after possible acceptance.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"4967-4982"},"PeriodicalIF":4.6,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142449499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0