IEEE Transactions on Signal Processing最新文献_第5页

Theory and Practice of Light-Weight Sequential SBL Algorithm: An Alternative to OMP 轻量级顺序SBL算法的理论与实践：一种替代OMP的算法

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-08-23 DOI: 10.1109/tsp.2025.3600492

Rohan R. Pote, Bhaskar D. Rao

引用次数: 0

Interleaved Hidden Markov Processes Inference for Deinterleaving Radar Pulse Sequences 去交错雷达脉冲序列的交错隐马尔可夫过程推理

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-08-23 DOI: 10.1109/tsp.2025.3597790

Jiadi Bao, Mengtao Zhu, Yunjie Li, Shafei Wang

引用次数: 0

Heterogeneous Time Resource Arrangement and Refined Tracking for Phased Array Radar Within Complex Target Environment 复杂目标环境下相控阵雷达异构时间资源安排与精细跟踪

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-08-14 DOI: 10.1109/TSP.2025.3599241

Hao Jiao;Junkun Yan;Wenqiang Pu;Tiancheng Li;Lin Ma;Hongwei Liu

{"title":"Heterogeneous Time Resource Arrangement and Refined Tracking for Phased Array Radar Within Complex Target Environment","authors":"Hao Jiao;Junkun Yan;Wenqiang Pu;Tiancheng Li;Lin Ma;Hongwei Liu","doi":"10.1109/TSP.2025.3599241","DOIUrl":"https://doi.org/10.1109/TSP.2025.3599241","url":null,"abstract":"Complex target environments present characteristics of saturation, high speed, and high maneuverability, posing increasingly challenging demands for target tracking. In this context, traditional phased-array radar (PAR) faces the dilemma of limited tracking resources and filter model mismatch. To address these issues, this paper proposes a heterogeneous time resource arrangement (HTRA) and refined tracking (RT) method. Firstly, to mitigate the impact of maneuvering model mismatch, we modify the traditional strong tracking filter by considering the effect of different measurements on the correction of the maneuvering model, and formulate the RT method as an optimization problem according to the residual consistency criterion. Then, to properly allocate and arrange limited time resources, by defining a multidimensional time resource vector, we adopt the posterior estimate covariance from RT as a performance metric, and design a performance-driven HTRA framework to achieve time assignment under model mismatch conditions. Simulation results demonstrate that, compared to traditional approaches, the joint HTRA and RT strategy significantly enhance the tracking performance of complex targets within a given time resource budget.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3362-3377"},"PeriodicalIF":5.8,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100415","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

Fundamental limits via CRB of semi-blind channel estimation in Massive MIMO systems 大规模MIMO系统中半盲信道估计的CRB基本限制

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-08-13 DOI: 10.1109/tsp.2025.3598515

Xue Zhang, Abla Kammoun, Mohamed-Slim Alouini

引用次数: 0

Constrained Stochastic Recursive Momentum Successive Convex Approximation 约束随机递推动量连续凸逼近

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-08-13 DOI: 10.1109/TSP.2025.3595590

Basil M. Idrees;Lavish Arora;Ketan Rajawat

{"title":"Constrained Stochastic Recursive Momentum Successive Convex Approximation","authors":"Basil M. Idrees;Lavish Arora;Ketan Rajawat","doi":"10.1109/TSP.2025.3595590","DOIUrl":"10.1109/TSP.2025.3595590","url":null,"abstract":"We consider stochastic optimization problems with non-convex functional constraints, such as those arising in trajectory generation, sparse approximation, and robust classification. To this end, we put forth a recursive momentum-based accelerated successive convex approximation (SCA) algorithm. At each iteration, the proposed algorithm entails constructing convex surrogates of the stochastic objective and the constraint functions, and solving the resulting convex optimization problem. A recursive update rule is employed to track the gradient of the stochastic objective function, which contributes to variance reduction and hence accelerates the algorithm convergence. A key ingredient of the proof is a new parameterized version of the standard Mangasarian-Fromowitz Constraints Qualification, that allows us to bound the dual variables and hence obtain problem-dependent bounds on the rate at which the iterates approach an <inline-formula><tex-math>$epsilon$</tex-math></inline-formula>-stationary point. Remarkably, the proposed algorithm achieves near-optimal stochastic first-order (SFO) complexity with adaptive step sizes closely matching that achieved by state-of-the-art stochastic optimization algorithms for solving unconstrained problems. As an example, we detail an obstacle-avoiding trajectory optimization problem that can be solved using the proposed algorithm and show that its performance is superior to that of the existing algorithms used for trajectory optimization. The performance of the proposed algorithm is also shown to be comparable to that of a specialized sparse classification algorithm applied to a binary classification problem.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3331-3345"},"PeriodicalIF":5.8,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850545","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

Edge-PRUNE: A Dataflow-Based Framework for Distributed Signal Processing and Machine Learning 边缘修剪：基于数据流的分布式信号处理和机器学习框架

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-08-13 DOI: 10.1109/TSP.2025.3598453

Jani Boutellier;Bo Tan;Jari Nurmi;Shuvra S. Bhattacharyya

{"title":"Edge-PRUNE: A Dataflow-Based Framework for Distributed Signal Processing and Machine Learning","authors":"Jani Boutellier;Bo Tan;Jari Nurmi;Shuvra S. Bhattacharyya","doi":"10.1109/TSP.2025.3598453","DOIUrl":"10.1109/TSP.2025.3598453","url":null,"abstract":"Distributed sensing through video, audio, radar and other sensors is strongly growing with application areas such as smart homes and Internet of Things. The concept of edge computing proposes shifting signal and data analysis from centralized servers close to the sensors, providing reduction in data communication bandwidth requirements and centralized server computation load as well as improving data privacy. Previous works in the domain of edge computing have paid little attention to formal modeling of computing across devices. This work proposes the VR-PRUNE-E model of computation that is based on the well-known dataflow abstraction. Within VR-PRUNE-E, a specific type of resilient network graph is introduced, which allows the distributed system to continue its operation after the failure of any single node or connection. Besides the formal model, the manuscript introduces the Edge-PRUNE software framework that supports the proposed dataflow abstraction, as well as concrete experimental results on real edge computing scenarios. The explored setups cover networks with up to 128 endpoint nodes and two servers. Application examples cover popular machine learning applications of image classification, object detection and radar signal processing, built on CNN and transformer architectures, extended with redundant system configurations that provide fault tolerance. The proposed work is also benchmarked in terms of processing time and shown to outperform previous work by 34% in computation efficiency.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3302-3315"},"PeriodicalIF":5.8,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11123706","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Coverage Analysis for Digital Cousin Selection - Improving Multi-Environment Q-Learning 数字表兄弟选择的覆盖分析——改进多环境Q-Learning

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-08-13 DOI: 10.1109/tsp.2025.3598253

Talha Bozkus, Tara Javidi, Urbashi Mitra

引用次数: 0

Robust Activity Detection for Massive Random Access 大规模随机访问的鲁棒活动检测

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-08-12 DOI: 10.1109/tsp.2025.3597931

Xinjue Wang, Esa Ollila, Sergiy A. Vorobyov

引用次数: 0

One-Bit Quantization and Sparsification for Multiclass Linear Classification With Strong Regularization 强正则化多类线性分类的一比特量化和稀疏化

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-08-12 DOI: 10.1109/TSP.2025.3598246

Reza Ghane;Danil Akhtiamov;Babak Hassibi

{"title":"One-Bit Quantization and Sparsification for Multiclass Linear Classification With Strong Regularization","authors":"Reza Ghane;Danil Akhtiamov;Babak Hassibi","doi":"10.1109/TSP.2025.3598246","DOIUrl":"10.1109/TSP.2025.3598246","url":null,"abstract":"We study the use of linear regression for multiclass classification in the over-parametrized regime where some of the training data is mislabeled. In such scenarios it is necessary to add an explicit regularization term, <inline-formula><tex-math>$lambda f(cdot)$</tex-math></inline-formula>, for some convex function <inline-formula><tex-math>$f(cdot)$</tex-math></inline-formula>, to avoid overfitting the mislabeled data. In our analysis, we assume that the data is sampled from a Gaussian Mixture Model with equal class sizes, and that a proportion of the training labels is corrupted for each class. Under these assumptions, we prove that the best classification performance is achieved when <inline-formula><tex-math>$f(cdot)=|cdot|^{2}_{2}$</tex-math></inline-formula> and <inline-formula><tex-math>$lambdatoinfty$</tex-math></inline-formula>. We then proceed to analyze the classification errors for <inline-formula><tex-math>$f(cdot)=|cdot|_{1}$</tex-math></inline-formula> and <inline-formula><tex-math>$f(cdot)=|cdot|_{infty}$</tex-math></inline-formula> in the large <inline-formula><tex-math>$lambda$</tex-math></inline-formula> regime and notice that it is often possible to find sparse and one-bit solutions, respectively, that perform almost as well as the one corresponding to <inline-formula><tex-math>$f(cdot)=|cdot|_{2}^{2}$</tex-math></inline-formula>.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3270-3285"},"PeriodicalIF":5.8,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850659","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 Globally Convergent Algorithm for Linear Minimax Regret Estimation of Deterministic Parameters with Bounded Data Uncertainties 具有有界数据不确定性的确定性参数线性极小极大遗憾估计的全局收敛算法

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-08-11 DOI: 10.1109/tsp.2025.3596096

Zhujun Cao, Enbin Song, Zhi Li, Dunbiao Niu, Jilong Lyu, Juping Gu, Qingjiang Shi

引用次数: 0