IEEE Transactions on Signal Processing最新文献_第4页

SINR Maximizing Distributionally Robust Adaptive Beamforming 最大SINR分布鲁棒自适应波束形成

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-24 DOI: 10.1109/TSP.2025.3582396

Kiarash Hassas Irani;Yongwei Huang;Sergiy A. Vorobyov

{"title":"SINR Maximizing Distributionally Robust Adaptive Beamforming","authors":"Kiarash Hassas Irani;Yongwei Huang;Sergiy A. Vorobyov","doi":"10.1109/TSP.2025.3582396","DOIUrl":"10.1109/TSP.2025.3582396","url":null,"abstract":"This paper addresses the robust adaptive beamforming (RAB) problem via the worst-case signal-to-interference-plus-noise ratio (SINR) maximization over distributional uncertainty sets for the random interference-plus-noise covariance (INC) matrix and desired signal steering vector. Our study explores two distinct uncertainty sets for the INC matrix and three for the steering vector. The uncertainty sets of the INC matrix account for the support and the positive semidefinite (PSD) mean of the distribution, as well as a similarity constraint on the mean. The uncertainty sets for the steering vector consist of the constraints on the first- and second-order moments of its associated probability distribution. The RAB problem is formulated as the minimization of the worst-case expected value of the SINR denominator over any distribution within the uncertainty set of the INC matrix, subject to the condition that the expected value of the numerator is greater than or equal to one for every distribution within the uncertainty set of the steering vector. By leveraging the strong duality of linear conic programming, this RAB problem is reformulated as a quadratic matrix inequality problem. Subsequently, it is addressed by iteratively solving a sequence of linear matrix inequality relaxation problems, incorporating a penalty term for the rank-one PSD matrix constraint. We further analyze the convergence of the iterative algorithm. The proposed robust beamforming approach is validated through simulation examples, which illustrate improved performance in terms of the array output SINR.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2542-2557"},"PeriodicalIF":4.6,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11048751","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144479200","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

Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filter 贝叶斯卡尔曼网：深度学习增强卡尔曼滤波中的不确定性量化

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-23 DOI: 10.1109/TSP.2025.3581703

Yehonatan Dahan;Guy Revach;Jindrich Dunik;Nir Shlezinger

{"title":"Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filter","authors":"Yehonatan Dahan;Guy Revach;Jindrich Dunik;Nir Shlezinger","doi":"10.1109/TSP.2025.3581703","DOIUrl":"10.1109/TSP.2025.3581703","url":null,"abstract":"Recent years have witnessed a growing interest in tracking algorithms that augment Kalman filters (KFs) with deep neural networks (DNNs). By transforming KFs into trainable deep learning models, one can learn from data to reliably track a latent state in complex and partially known dynamics. However, unlike classic KFs, conventional DNN-based systems do not naturally provide an uncertainty measure, such as error covariance, alongside their estimates, which is crucial in various applications that rely on KF-type tracking. This work bridges this gap by studying error covariance extraction in DNN-aided KFs. We begin by characterizing how uncertainty can be extracted from existing DNN-aided algorithms and distinguishing between approaches by their ability to associate internal features with meaningful KF quantities, such as the Kalman gain and prior covariance. We then identify that uncertainty extraction from existing architectures necessitates additional domain knowledge not required for state estimation. Based on this insight, we propose <italic>Bayesian KalmanNet</i>, a novel DNN-aided KF that integrates Bayesian deep learning techniques with the recently proposed KalmanNet and transforms the KF into a <italic>stochastic</i> machine learning architecture. This architecture employs sampling techniques to predict error covariance reliably without requiring additional domain knowledge, while retaining KalmanNet’s ability to accurately track in partially known dynamics. Our numerical study demonstrates that Bayesian KalmanNet provides accurate and reliable tracking in various scenarios representing partially known dynamic systems.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2558-2573"},"PeriodicalIF":4.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11048390","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144370737","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

Distributionally Robust Receive Combining 分布式鲁棒接收组合

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-23 DOI: 10.1109/tsp.2025.3582082

Shixiong Wang, Wei Dai, Geoffrey Ye Li

引用次数: 0

Boosting Restoration of Turbulence-Degraded Images With State Space Conditional Diffusion 状态空间条件扩散增强湍流退化图像的恢复

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-23 DOI: 10.1109/TSP.2025.3580723

Yubo Wu;Kuanhong Cheng;Tingting Chai;Gengyu Lyu;Shuping Zhao;Wei Jia

{"title":"Boosting Restoration of Turbulence-Degraded Images With State Space Conditional Diffusion","authors":"Yubo Wu;Kuanhong Cheng;Tingting Chai;Gengyu Lyu;Shuping Zhao;Wei Jia","doi":"10.1109/TSP.2025.3580723","DOIUrl":"10.1109/TSP.2025.3580723","url":null,"abstract":"Recovering fine details from turbulence-distorted images is highly challenging due to the complex, spatially varying, and stochastic nature of the distortion process. Conventional multi-frame methods rely on extracting and averaging clear regions from pre-aligned frames, but their effectiveness is limited due to the rarity of “lucky regions”. In contrast, learning based methods have shown superior performance across various vision tasks. However, existing deep learning approaches still face key limitations: (1) they struggle to efficiently model the global context required for correcting pixel dispersion caused by spatially varying Point Spread Functions (PSFs); (2) they often overlook the physical formation of turbulence, particularly the spatial-frequency relationship between phase distortions and PSFs; and (3) they rely on deterministic architectures that fail to capture the inherent uncertainty in turbulence, leading to visually implausible outputs. To address these issues, we propose the Two-Stage Turbulence Removal Network (TSTRNet). The first stage uses a UNet-based generator built on the State Space Model to perform efficient, coarse global restoration. The second stage refines the output through a Denoising Diffusion Probabilistic Model, introducing stochasticity and edge-guided conditioning for detail enhancement and realism. Both stages incorporate frequency-domain processing to align with the physical characteristics of turbulence. Experimental results on multiple benchmark datasets demonstrate that TSTRNet achieves superior restoration performance compared to state-of-the-art methods, with strong generalization from synthetic to real-world scenarios.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2631-2645"},"PeriodicalIF":4.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144370736","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

Monotone Lipschitz-Gradient Denoiser: Explainability of Operator Regularization Approaches Free From Lipschitz Constant Control 单调Lipschitz梯度去噪：不受Lipschitz常数控制的算子正则化方法的可解释性

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-18 DOI: 10.1109/tsp.2025.3580667

Masahiro Yukawa, Isao Yamada

引用次数: 0

Transdimensional Model Learning with Online Feature Selection Based on Predictive Least Squares 基于预测最小二乘的在线特征选择的跨维模型学习

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-17 DOI: 10.1109/tsp.2025.3580315

Marija Iloska, Mónica Bugallo, Petar M. Djurić

引用次数: 0

Uncovering the Iceberg in the Sea: Fundamentals of Pulse Shaping and Modulation Design for Random ISAC Signals 揭示海中的冰山：随机ISAC信号的脉冲整形和调制设计的基础

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-17 DOI: 10.1109/TSP.2025.3580596

Fan Liu;Yifeng Xiong;Shihang Lu;Shuangyang Li;Weijie Yuan;Christos Masouros;Shi Jin;Giuseppe Caire

{"title":"Uncovering the Iceberg in the Sea: Fundamentals of Pulse Shaping and Modulation Design for Random ISAC Signals","authors":"Fan Liu;Yifeng Xiong;Shihang Lu;Shuangyang Li;Weijie Yuan;Christos Masouros;Shi Jin;Giuseppe Caire","doi":"10.1109/TSP.2025.3580596","DOIUrl":"10.1109/TSP.2025.3580596","url":null,"abstract":"Integrated Sensing and Communications (ISAC) is expected to play a pivotal role in future 6G networks. To maximize time-frequency resource utilization, 6G ISAC systems must exploit data payload signals, that are inherently random, for both communication and sensing tasks. This paper provides a comprehensive analysis of the sensing performance of such communication-centric ISAC signals, with a focus on modulation and pulse shaping design to reshape the statistical properties of their auto-correlation functions (ACFs), thereby improving the target ranging performance. We derive a closed-form expression for the expectation of the squared ACF of random ISAC signals, considering arbitrary modulation bases and constellation mappings within the Nyquist pulse shaping framework. The structure is metaphorically described as an “iceberg hidden in the sea”, where the “iceberg” represents the squared mean of the ACF of random ISAC signals, that is determined by the pulse shaping filter, and the “sea level” characterizes the corresponding variance, and account for the randomness of data payloads. Our analysis shows that, for QAM/PSK constellations with Nyquist pulse shaping, Orthogonal Frequency Division Multiplexing (OFDM) achieves the lowest ranging sidelobe level across all lags. Building on these insights, we propose a novel Nyquist pulse shaping design to enhance the sensing performance of random ISAC signals. Numerical results validate our theoretical findings, showing that the proposed pulse shaping significantly reduces ranging sidelobes compared to conventional root-raised cosine (RRC) pulse shaping, thereby improving the ranging performance.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2511-2526"},"PeriodicalIF":4.6,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11037613","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144311308","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

Enhancing 1-Bit Compressive Sensing With Support Estimation in Noisy Wireless Sensor Networks 基于支持度估计的噪声无线传感器网络1位压缩感知增强

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-16 DOI: 10.1109/TSP.2025.3579610

Ming-Hsun Yang;Liang-Chi Huang

{"title":"Enhancing 1-Bit Compressive Sensing With Support Estimation in Noisy Wireless Sensor Networks","authors":"Ming-Hsun Yang;Liang-Chi Huang","doi":"10.1109/TSP.2025.3579610","DOIUrl":"10.1109/TSP.2025.3579610","url":null,"abstract":"One-bit compressive sensing (1-bit CS) is an attractive low-bit-resolution signal processing technique that has been successfully applied to the design of large-scale wireless networks. In this work, we consider the problem of 1-bit CS in wireless sensor networks (WSNs), where the fusion center (FC) aims to recover a sparse signal based on a few binary measurements received from local sensor nodes and corrupted by channel-induced bit-flipping errors. Here, neither the signal support nor its cardinality is assumed to be known. The proposed signal processing protocol for distributed sparse signal recovery consists of the following three steps: (i) each local sensor employs a sparse sensing vector to efficiently compress its observation into a scalar, (ii) to conserve energy and bandwidth, only sensors with informative scalar measurements will quantize their real-valued compressed measurements into one bit, and (iii) these sensors then forward their quantized data to the FC for global signal recovery. In contrast to most existing 1-bit CS methods, which rely fully on the sign message of measurements, we propose a new amplitude-assisted signal retrieval scheme to enhance robustness against bit-flipping errors. In our algorithm, we first identify the signal support using a simple energy detector and derive an analytical performance guarantee for perfect support recovery. After obtaining the support knowledge, we then analytically derive the optimal representation level of local 1-bit quantizers in closed-form by minimizing the mean square error, resulting from quantization error, local sensing noise, and bit-flipping errors, at the FC. With the aid of the optimal representation level and the support estimate, we develop a modified single-sided <inline-formula><tex-math>$ell_{1}$</tex-math></inline-formula>-minimization based algorithm to enhance signal reconstruction performance. A theoretical analysis of the convergence of the proposed algorithm is also provided. Computer simulations are used to illustrate the effectiveness of the proposed scheme.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2660-2675"},"PeriodicalIF":4.6,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144304467","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

Straggler-Resilient Asynchronous ADMM for Distributed Consensus Optimization 分布式一致性优化的离散-弹性异步ADMM

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-16 DOI: 10.1109/TSP.2025.3579628

Jeannie He;Ming Xiao;Mikael Skoglund;Harold Vincent Poor

{"title":"Straggler-Resilient Asynchronous ADMM for Distributed Consensus Optimization","authors":"Jeannie He;Ming Xiao;Mikael Skoglund;Harold Vincent Poor","doi":"10.1109/TSP.2025.3579628","DOIUrl":"10.1109/TSP.2025.3579628","url":null,"abstract":"For its simplicity, well-established convergence properties, and applicability to various optimization problems, the alternating direction method of multipliers (ADMM) has been widely used in several fields. However, when applied in distributed systems, the method may encounter the challenges of stragglers (nodes with significantly longer response time than others) and single points of failure (a single node causing the failure of the entire system). To address these problems, we propose three straggler-resilient ADMM algorithms. The first one is a centralized straggler-resilient ADMM algorithm achieving straggler-resilience by allowing the nodes to proceed to the next iteration even when one or more nodes have not provided an update for one or more iterations. The second one is an extension of the first one achieving single-point-of-failure resilience and fast convergence through decentralized, asynchronous, and concurrent operations. The third one is an extension of the second one to also achieve robustness against uncertainties with the help of a time-tracking scheme. Through theoretical analyses, we establish the convergence properties of our algorithms and show that our algorithms achieve a computational complexity of <inline-formula><tex-math>$mathcal{O}(1)$</tex-math></inline-formula> for each worker node - excluding the central node in the centralized algorithm, where the workload complexity is <inline-formula><tex-math>$mathcal{O}(N)$</tex-math></inline-formula>. By numerical simulations with various settings, we show that our algorithms have converged significantly faster than several state-of-the-art ADMM algorithms with well-established convergence properties.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2496-2510"},"PeriodicalIF":4.6,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144304466","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

Slim Is Better: Transform-Based Tensor Robust Principal Component Analysis 越瘦越好：基于变换的张量鲁棒主成分分析

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-12 DOI: 10.1109/TSP.2025.3577762

Lin Chen;Li Ge;Xue Jiang;Hongbin Li;Martin Haardt

{"title":"Slim Is Better: Transform-Based Tensor Robust Principal Component Analysis","authors":"Lin Chen;Li Ge;Xue Jiang;Hongbin Li;Martin Haardt","doi":"10.1109/TSP.2025.3577762","DOIUrl":"10.1109/TSP.2025.3577762","url":null,"abstract":"This paper addresses the tensor robust principal component analysis (RPCA) by employing linear slim transforms along the mode-3 of the tensor. Previous works have empirically shown the superiority of slim transforms over traditional square ones in low-rank tensor recovery. However, the recovery guarantee for the slim transform-based tensor RPCA (SRPCA) remains an unresolved issue, as existing guarantees are only applicable to invertible, inner product preserving, and self-adjoint transforms. In contrast, we establish the recovery guarantee for SRPCA that is applicable to any mode-3 linear slim transform under certain conditions. Specifically, new tensor incoherence conditions are deduced to accommodate slim transforms and can also be simplified to the existing conditions pertaining to the discrete Fourier transform. Our theoretical analysis reveals that the slim transform with a condition number of 1 enjoys an averaging effect on tensor incoherence parameters through its composing square transforms, thus leading to a more relaxed recovery bound for SRPCA compared to its square counterparts. This insight is validated through experimental results on both synthetic and real data, which demonstrate the improved performance of SRPCA over traditionally square transform-based tensor RPCA.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2320-2335"},"PeriodicalIF":4.6,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144278208","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