IEEE Transactions on Signal Processing最新文献_第7页

Subspace Representation Learning for Sparse Linear Arrays to Localize More Sources Than Sensors: A Deep Learning Methodology 稀疏线性阵列的子空间表示学习以定位比传感器更多的源：一种深度学习方法

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-21 DOI: 10.1109/TSP.2025.3544170

Kuan-Lin Chen;Bhaskar D. Rao

{"title":"Subspace Representation Learning for Sparse Linear Arrays to Localize More Sources Than Sensors: A Deep Learning Methodology","authors":"Kuan-Lin Chen;Bhaskar D. Rao","doi":"10.1109/TSP.2025.3544170","DOIUrl":"10.1109/TSP.2025.3544170","url":null,"abstract":"Localizing more sources than sensors with a sparse linear array (SLA) has long relied on minimizing a distance between two covariance matrices and recent algorithms often utilize semidefinite programming (SDP). Although deep neural network (DNN)-based methods offer new alternatives, they still depend on covariance matrix fitting. In this paper, we develop a novel methodology that estimates the co-array subspaces from a sample covariance for SLAs. Our methodology trains a DNN to learn signal and noise subspace representations that are invariant to the selection of bases. To learn such representations, we propose loss functions that gauge the separation between the desired and the estimated subspace. In particular, we propose losses that measure the length of the shortest path between subspaces viewed on a union of Grassmannians, and prove that it is possible for a DNN to approximate signal subspaces. The computation of learning subspaces of different dimensions is accelerated by a new batch sampling strategy called consistent rank sampling. The methodology is robust to array imperfections due to its geometry-agnostic and data-driven nature. In addition, we propose a fully end-to-end gridless approach that directly learns angles to study the possibility of bypassing subspace methods. Numerical results show that learning such subspace representations is more beneficial than learning covariances or angles. It outperforms conventional SDP-based methods such as the sparse and parametric approach (SPA) and existing DNN-based covariance reconstruction methods for a wide range of signal-to-noise ratios (SNRs), snapshots, and source numbers for both perfect and imperfect arrays.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1293-1308"},"PeriodicalIF":4.6,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470804","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

Rate-Matching Framework for RSMA-Enabled Multibeam LEO Satellite Communications 支持rsma的多波束LEO卫星通信速率匹配框架

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-20 DOI: 10.1109/TSP.2025.3543753

Jaehyup Seong;Juha Park;Juhwan Lee;Jungwoo Lee;Jung-Bin Kim;Wonjae Shin;H. Vincent Poor

{"title":"Rate-Matching Framework for RSMA-Enabled Multibeam LEO Satellite Communications","authors":"Jaehyup Seong;Juha Park;Juhwan Lee;Jungwoo Lee;Jung-Bin Kim;Wonjae Shin;H. Vincent Poor","doi":"10.1109/TSP.2025.3543753","DOIUrl":"10.1109/TSP.2025.3543753","url":null,"abstract":"With the goal of ubiquitous global connectivity, multibeam low Earth orbit (LEO) satellite communications (SATCOM) has attracted significant attention in recent years. The traffic demands of users are heterogeneous within the broad coverage of SATCOM due to different geological conditions and user distributions. Motivated by this, this paper proposes a novel rate-matching (RM) framework based on rate-splitting multiple access (RSMA) that minimizes the difference between the traffic demands and offered rates while simultaneously minimizing transmit power for power-hungry satellite payloads. Moreover, channel phase perturbations arising from channel estimation and feedback errors are considered to capture realistic multibeam LEO SATCOM scenarios. To tackle the non-convexity of the RSMA-based RM problem under phase perturbations, we convert it into a tractable convex form via the successive convex approximation method and present an efficient algorithm to solve the RM problem. Through the extensive numerical analysis across various traffic demand distribution and channel state information accuracy at LEO satellites, we demonstrate that RSMA flexibly allocates the power between common and private streams according to different traffic patterns across beams, thereby efficiently satisfying users’ non-uniform traffic demands. In particular, the use of common messages plays a vital role in overcoming the limited spatial dimension available at LEO satellites, enabling it to manage inter-/intra-beam interference effectively in the presence of phase perturbation.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1426-1443"},"PeriodicalIF":4.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462556","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

D2CN: Distributed Deep Convolutional Network D2CN：分布式深度卷积网络

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-20 DOI: 10.1109/TSP.2025.3534177

Yi Ding;Junpeng Shi;Zai Yang;Zhiyuan Zhang;Yongxiang Liu;Xiang Li

{"title":"D2CN: Distributed Deep Convolutional Network","authors":"Yi Ding;Junpeng Shi;Zai Yang;Zhiyuan Zhang;Yongxiang Liu;Xiang Li","doi":"10.1109/TSP.2025.3534177","DOIUrl":"10.1109/TSP.2025.3534177","url":null,"abstract":"With the rapid growth of distributed systems, deep learning-based multi-source data processing has drawn extensive attention, especially for the multi-channel networks. However, the conventional ones lack a strong theoretical foundation and the data in each channel lack necessary interactions, giving rise to insufficient robustness. Here we derive a network termed as distributed deep convolutional network (D<inline-formula><tex-math>${}^{2}$</tex-math></inline-formula>CN) to overcome this issue, which is explained by integrating generalized singular value decomposition (GSVD) with the principles of Hankel convolution framelet. Specifically, we employ the feature extraction capability of GSVD to perform data interactions by forward/backward propagation, where numerous inputs are designed using the common bases and reliable performance is achieved by training a shared set of right bases. We go over the network's scalability to show its benefits in performance and robustness. Moreover, we show that the encoder-decoder scheme allows the network suitable for a wide range of inverse situations. Finally, we demonstrate the superiority of the D<inline-formula><tex-math>${}^{2}$</tex-math></inline-formula>CN over other fundamental networks through numerical experiments conducted on classical image denoising.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1309-1322"},"PeriodicalIF":4.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143462558","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 Statistic Detector for Structural Image Similarity 一种新的结构图像相似度统计检测器

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-18 DOI: 10.1109/TSP.2025.3543207

Moustapha Diaw;Florent Retraint;Frédéric Morain-Nicolier;Agnès Delahaies;Jérôme Landré

{"title":"New Statistic Detector for Structural Image Similarity","authors":"Moustapha Diaw;Florent Retraint;Frédéric Morain-Nicolier;Agnès Delahaies;Jérôme Landré","doi":"10.1109/TSP.2025.3543207","DOIUrl":"10.1109/TSP.2025.3543207","url":null,"abstract":"Social networks like LinkedIn, Facebook, and Instagram contribute significantly to the rise of image prevalence in daily life, with numerous images posted in everyday. Detecting image similarity is crucial for many applications. While deep learning methods like Learned Perceptual Image Patch Similarity (LPIPS) are popular, they often overlook image structure. An alternative method involves using pre-trained models (<inline-formula><tex-math>$e.g.$</tex-math></inline-formula>, LeNet-<inline-formula><tex-math>$5$</tex-math></inline-formula> and VGG-<inline-formula><tex-math>$16$</tex-math></inline-formula>) to extract features and employing classifiers. However, deep learning methods demand substantial computational resources and they also suffer from uncontrolled false alarms. This paper proposes a novel Generalized Likelihood Ratio Test (GLRT) detector based on a hypothesis testing framework to identify the similarity of structural image pairs. The proposed approach minimizes the need for extensive computational resources, and false alarms can be regulated by employing a threshold. The detector is applied to Local Dissimilarity Maps (LDM), with gray-level values modeled by a statistical distribution. Experimental results on simulated and real data confirm its effectiveness for structural similarity detection. Additionally, a Simple Likelihood Ratio Test (SLRT) is tested on simulated data. Comparisons with deep learning and classical measures like Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) show the proposed detector performs comparably or better in terms of Area Under the Curve (AUC) with less computing time, especially for structural similarity.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1168-1183"},"PeriodicalIF":4.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443547","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

Approximating Multi-Dimensional and Multiband Signals 多维和多波段信号的逼近

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-14 DOI: 10.1109/TSP.2025.3541872

Yuhan Li;Tianyao Huang;Lei Wang;Yimin Liu;Xiqin Wang

引用次数: 0

Theoretical Bounds in Decentralized Hypothesis Testing 分散假设检验的理论界限

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-13 DOI: 10.1109/TSP.2025.3541569

Gökhan Gül

{"title":"Theoretical Bounds in Decentralized Hypothesis Testing","authors":"Gökhan Gül","doi":"10.1109/TSP.2025.3541569","DOIUrl":"https://doi.org/10.1109/TSP.2025.3541569","url":null,"abstract":"Three fundamental problems are addressed for distributed detection networks regarding the maximum of performance/detection loss. The losses obtained are, first, due to the choice of decision rule in parallel sensor networks (general-case vs identical decisions), second, due to the choice of network architecture (serial vs parallel), and third, due to the choice of quantization rule (centralized vs decentralized). Previous results, if available, for all these three problems are restricted to the statement that the loss is “small” over some specific examples. The key principles underlying this study are delineated as follows. First, there is a surjection from all simple hypothesis tests to the receiver operating characteristic (ROC) curve. Second, the ROC can be well modeled with linear splines. Third, considering splines with only a finite number of line segments, in fact, on the order of the total number of sensors, is sufficient to determine the maximum loss. Leveraging these principles, infinite-dimensional optimization problems are reduced to their finite-dimensional equivalent forms. The equivalent problems are then numerically solved to obtain the theoretical bounds.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1110-1121"},"PeriodicalIF":4.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10884703","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553368","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

A Method to Obtain Non-Power-of-Two FFT Flow Graphs Based on a New Prime Factor Algorithm 基于一种新的素数因子算法的非二次幂FFT流图获取方法

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-12 DOI: 10.1109/TSP.2025.3540561

VÍctor Manuel Bautista;Mario Garrido

{"title":"A Method to Obtain Non-Power-of-Two FFT Flow Graphs Based on a New Prime Factor Algorithm","authors":"VÍctor Manuel Bautista;Mario Garrido","doi":"10.1109/TSP.2025.3540561","DOIUrl":"10.1109/TSP.2025.3540561","url":null,"abstract":"This paper presents a novel method to obtain non-power-of-two (NP2) fast Fourier transform (FFT) flow graphs based on a new prime factor algorithm (PFA). The FFT flow graph is crucial for designing FFT architectures but previous works only provide systematic approaches to build flow graphs for power-of-two sizes (P2). Thus, the derivation of NP2 flow graphs is an important step towards the design of efficient NP2 FFT architectures. The proposed approach consists of two independent parts. On the one hand, it obtains all the possible index mappings that lead to a flow graph with no rotations between butterflies. On the other hand, it determines the permutations between butterflies in the flow graph. By combining these two parts, the order of the inputs and outputs is derived. As a result, the entire flow graph is obtained systematically. Additionally, the proposed approach generates all the possible flow graphs for a given factorization of the FFT size. The reduction in operations for NP2 FFTs using the proposed approach leads to a significant reduction in area and power consumption concerning P2 FFTs with similar sizes after implementing the proposed flow graphs directly in hardware. Particularly, there is a significant improvement between the proposed 30-point and 60-point FFT and previous efficient P2 FFTs. This remarkable fact sets NP2 at the forefront of FFT research after being in second place behind P2 FFTs for decades.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1004-1017"},"PeriodicalIF":4.6,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10883046","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143401804","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

MAML-KalmanNet: A Neural Network-Assisted Kalman Filter Based on Model-Agnostic Meta-Learning 基于模型不可知元学习的神经网络辅助卡尔曼滤波

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-12 DOI: 10.1109/TSP.2025.3540018

Shanli Chen;Yunfei Zheng;Dongyuan Lin;Peng Cai;Yingying Xiao;Shiyuan Wang

{"title":"MAML-KalmanNet: A Neural Network-Assisted Kalman Filter Based on Model-Agnostic Meta-Learning","authors":"Shanli Chen;Yunfei Zheng;Dongyuan Lin;Peng Cai;Yingying Xiao;Shiyuan Wang","doi":"10.1109/TSP.2025.3540018","DOIUrl":"10.1109/TSP.2025.3540018","url":null,"abstract":"Neural network-assisted (NNA) Kalman filters provide an effective solution to addressing the filtering issues involving partially unknown system information by incorporating neural networks to compute the intermediate values influenced by unknown data, such as the Kalman gain in the filtering process. However, whenever there are slight changes in the state-space model (SSM), previously trained networks used in NNA Kalman filters become outdated, necessitating extensive time and data for retraining. Furthermore, obtaining sufficient labeled data for supervised learning is costly, and the effectiveness of unsupervised learning can be inconsistent. To this end, to address the inflexibility of neural network architecture and the scarcity of training data, we propose a model-agnostic meta-learning based neural network-assisted Kalman filter in this paper, called MAML-KalmanNet, by employing a limited amount of labeled data and training rounds to achieve desirable outcomes comparable to the supervised NNA Kalman filters with sufficient training. MAML-KalmanNet utilizes a pre-training approach based on specifically tailored meta-learning, enabling the network to adapt to model changes with minimal data and time without the requirement of retraining. Simultaneously, by fully leveraging the information from the SSM, MAML-KalmanNet eliminates the requirement of a large amount of labeled data to train the meta-learning initialization network. Simulations show that MAML-KalmanNet can mitigate the shortcomings existing in NNA Kalman filters regarding the requirements of abundant training data and sensitive network architecture, while providing real-time state estimation across a range of noise distributions.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"988-1003"},"PeriodicalIF":4.6,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143401957","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

Communication-Efficient Vertical Federated Learning via Compressed Error Feedback 基于压缩误差反馈的高效通信垂直联邦学习

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-11 DOI: 10.1109/TSP.2025.3540655

Pedro Valdeira;João Xavier;Cláudia Soares;Yuejie Chi

{"title":"Communication-Efficient Vertical Federated Learning via Compressed Error Feedback","authors":"Pedro Valdeira;João Xavier;Cláudia Soares;Yuejie Chi","doi":"10.1109/TSP.2025.3540655","DOIUrl":"10.1109/TSP.2025.3540655","url":null,"abstract":"Communication overhead is a known bottleneck in federated learning (FL). To address this, lossy compression is commonly used on the information communicated between the server and clients during training. In horizontal FL, where each client holds a subset of the samples, such communication-compressed training methods have recently seen significant progress. However, in their vertical FL counterparts, where each client holds a subset of the features, our understanding remains limited. To address this, we propose an error feedback compressed vertical federated learning (<monospace>EF-VFL</monospace>) method to train split neural networks. In contrast to previous communication-compressed methods for vertical FL, <monospace>EF-VFL</monospace> does not require a vanishing compression error for the gradient norm to converge to zero for smooth nonconvex problems. By leveraging error feedback, our method can achieve a <inline-formula><tex-math>$mathcal{O}({1}/{T})$</tex-math></inline-formula> convergence rate for a sufficiently large batch size, improving over the state-of-the-art <inline-formula><tex-math>$mathcal{O}({1}/{sqrt{T}})$</tex-math></inline-formula> rate under <inline-formula><tex-math>$mathcal{O}({1}/{sqrt{T}})$</tex-math></inline-formula> compression error, and matching the rate of uncompressed methods. Further, when the objective function satisfies the Polyak-Łojasiewicz inequality, our method converges linearly. In addition to improving convergence, our method also supports the use of private labels. Numerical experiments show that <monospace>EF-VFL</monospace> significantly improves over the prior art, confirming our theoretical results.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1065-1080"},"PeriodicalIF":4.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143392952","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

Collaborative Trajectory Optimization for Multitarget Tracking in Airborne Radar Network With Missing Data 缺失数据下机载雷达网络多目标跟踪协同轨迹优化

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-02-11 DOI: 10.1109/TSP.2025.3540798

Juan Hu;Lei Zuo;Pramod K. Varshney;Zhengyu Lan;Yongchan Gao

{"title":"Collaborative Trajectory Optimization for Multitarget Tracking in Airborne Radar Network With Missing Data","authors":"Juan Hu;Lei Zuo;Pramod K. Varshney;Zhengyu Lan;Yongchan Gao","doi":"10.1109/TSP.2025.3540798","DOIUrl":"10.1109/TSP.2025.3540798","url":null,"abstract":"In this paper, an effective collaborative trajectory optimization (CTO) strategy is proposed for multitarget tracking in airborne radar networks with missing data. Missing data may occur during data exchange between radar nodes and a fusion center (FC) due to unreliability of communication channels. The CTO strategy aims to enhance the overall multi-target tracking performance by collaboratively optimizing the trajectories of airborne radars and the FC. In this paper, we derive the posterior Cramér-Rao lower bound (PCRLB) with missing data to evaluate the target tracking performance. On this basis, to maximize the target tracking performance while considering dynamics, collision avoidance, and communication distance constraints, we formulate the CTO optimization problem. The formulated problem is non-convex and internally coupled, which is challenging to solve directly. We decompose the CTO problem into two subproblems and devise an alternating optimization method. Specifically, approximation, and successive convex approximation are applied to make the subproblems solvable. Then, the two subproblems are solved alternately to realize the collaborative trajectory optimization of radars and the FC. Simulation results demonstrate that the proposed CTO strategy achieves better target tracking performance as compared with other benchmark strategies.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1048-1064"},"PeriodicalIF":4.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143392950","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