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

Shuffled Linear Regression via Spectral Matching 基于谱匹配的洗牌线性回归

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-21 DOI: 10.1109/TSP.2025.3590466

Hang Liu;Anna Scaglione

{"title":"Shuffled Linear Regression via Spectral Matching","authors":"Hang Liu;Anna Scaglione","doi":"10.1109/TSP.2025.3590466","DOIUrl":"10.1109/TSP.2025.3590466","url":null,"abstract":"Shuffled linear regression (SLR) seeks to estimate latent features through a linear transformation, complicated by unknown permutations in the measurement dimensions. This problem extends traditional least-squares (LS) and Least Absolute Shrinkage and Selection Operator (LASSO) approaches by jointly estimating the permutation, resulting in shuffled LS and shuffled LASSO formulations. Existing methods, constrained by the combinatorial complexity of permutation recovery, often address small-scale cases with limited measurements. In contrast, we focus on large-scale SLR, particularly suited for environments with abundant measurement samples. We propose a spectral matching method that efficiently resolves permutations by aligning spectral components of the measurement and feature covariances. Rigorous theoretical analyses demonstrate that our method achieves accurate estimates in both shuffled LS and shuffled LASSO settings, given a sufficient number of samples. Furthermore, we extend our approach to address simultaneous pose and correspondence estimation in image registration tasks. Experiments on synthetic datasets and real-world image registration scenarios show that our method outperforms existing algorithms in both estimation accuracy and registration performance.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3014-3028"},"PeriodicalIF":5.8,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144677242","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

Robust Two-Tier Beamforming for Distributed Signal Sensing 分布式信号感知的鲁棒两层波束形成

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-18 DOI: 10.1109/TSP.2025.3590769

Zhixing Chen;Wenqiang Pu;Licheng Zhao;Qingjiang Shi

引用次数: 0

Optimal Symbol-Length Filter Design for Sidelobe Suppression in Filter Bank Based Orthogonal Time Frequency Space (FB-OTFS) Systems 基于正交时频空间滤波器组（FB-OTFS）系统旁瓣抑制的最佳符号长度滤波器设计

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-17 DOI: 10.1109/TSP.2025.3590021

Da Chen;Yixuan Zhan;Yuting Chen;Kai Luo;Wei Peng;Wei Wang

{"title":"Optimal Symbol-Length Filter Design for Sidelobe Suppression in Filter Bank Based Orthogonal Time Frequency Space (FB-OTFS) Systems","authors":"Da Chen;Yixuan Zhan;Yuting Chen;Kai Luo;Wei Peng;Wei Wang","doi":"10.1109/TSP.2025.3590021","DOIUrl":"https://doi.org/10.1109/TSP.2025.3590021","url":null,"abstract":"In this paper, we propose symbol-length transceive filter optimization methods for sidelobe suppression in filter bank based orthogonal time frequency space (FB-OTFS) systems. Specifically, we firstly establish the FB-OTFS system model with fast implementation for transceive filters. Then, we analyze the impact of the transceive filters on the orthogonal transmission and derive the constraints for symbol-length transceive filters to achieve the orthogonal transmission. Moreover, the complexity analysis is provided. With the derived orthogonal conditions as constraints, we formulate a transceive filter optimization problem to minimize the stopband energy (a commonly used sidelobe suppression criterion), and derive the theoretically optimal solutions. To further achieve flexible suppression of the spectral sidelobes within specific frequency intervals, we formulate a transceive filter optimization to minimize the weighted stopband energy by designing adjustable frequency domain weights, and also obtain the optimal solutions. Numerical results demonstrate that: 1) The proposed transceive filters have the lowest spectral sidelobes compared with the commonly used rectangular pulse and the Gaussian filter; 2) The sidelobe suppression effects within specific frequency intervals are successfully controlled by designing the frequency domain weights; 3) All proposed transceive filters are verified to satisfy the orthogonal conditions.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3094-3106"},"PeriodicalIF":5.8,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144853161","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

Radar Code Design for the Joint Optimization of Detection Performance and Measurement Accuracy in Track Maintenance 轨道维修中探测性能与测量精度联合优化的雷达代码设计

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-16 DOI: 10.1109/TSP.2025.3587522

Tao Fan;Augusto Aubry;Vincenzo Carotenuto;Antonio De Maio;Xianxiang Yu;Guolong Cui

{"title":"Radar Code Design for the Joint Optimization of Detection Performance and Measurement Accuracy in Track Maintenance","authors":"Tao Fan;Augusto Aubry;Vincenzo Carotenuto;Antonio De Maio;Xianxiang Yu;Guolong Cui","doi":"10.1109/TSP.2025.3587522","DOIUrl":"https://doi.org/10.1109/TSP.2025.3587522","url":null,"abstract":"This paper deals with the design of slow-time coded waveforms which jointly optimize the detection probability and the measurements accuracy for track maintenance in the presence of colored Gaussian interference. The output signal-to-interference-plus-noise ratio (SINR) and Cramér Rao bounds (CRBs) on time delay and Doppler shift are used as figures of merit to accomplish reliable detection as well as accurate measurements. The transmitted code is subject to radar power budget requirements and a similarity constraint. To tackle the resulting non-convex multi-objective optimization problem, a polynomial-time algorithm that integrates scalarization and tensor-based relaxation methods is developed. The corresponding relaxed multi-linear problems are solved by means of the maximum block improvement (MBI) framework, where the optimal solution at each iteration is obtained in closed form. Numeral results demonstrate the trade-off between the detection and the estimation performance, along with the acceptable Doppler robustness achieved by the proposed algorithm.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3173-3186"},"PeriodicalIF":5.8,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880485","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

DeepFRI: A Deep Plug-and-Play Technique for Finite-Rate-of-Innovation Signal Reconstruction DeepFRI：一种用于有限创新率信号重建的深度即插即用技术

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-15 DOI: 10.1109/TSP.2025.3589394

Abijith Jagannath Kamath;Sharan Basav Patil;Chandra Sekhar Seelamantula

{"title":"DeepFRI: A Deep Plug-and-Play Technique for Finite-Rate-of-Innovation Signal Reconstruction","authors":"Abijith Jagannath Kamath;Sharan Basav Patil;Chandra Sekhar Seelamantula","doi":"10.1109/TSP.2025.3589394","DOIUrl":"10.1109/TSP.2025.3589394","url":null,"abstract":"The finite-rate-of-innovation (FRI) sampling framework is a sample-efficient and power-efficient model for analog-to-digital conversion. It can be interpreted as a framework for performing continuous-domain sparse deconvolution starting from discrete measurements. The promise of the FRI framework is its ability to resolve time delays beyond conventional theoretical limits, while acquiring measurements at the rate of innovation. In the current state-of-the-art, application of the FRI framework to real-world problems is challenging due to its limited performance in the presence of noise. In this paper, we consider signal reconstruction in the Fourier domain and propose a new optimization formulation that solves for the Fourier coefficients. We employ the proximal gradient method, and analyze the role of the denoiser in a plug-and-play (PnP) setting. Within the proposed framework, it is sufficient for the denoiser to be Lipschitz continuous, thus motivating the application of a deep PnP denoising neural network with a continuous piecewise-linear architecture. Such a neural network is interpretable and possesses similar theoretical guarantees as model-based techniques, while obtaining superior performance in the estimation of signal parameters when the signal-to-noise ratio (SNR) is low. Since the technique is derived from an optimization algorithm, we use the ensemble strategy to combine the Cadzow denoiser, which is widely used in FRI problems, and the deep PnP denoiser in order to achieve perfect reconstruction in the high SNR regime. The resulting method is called <italic>DeepFRI</i>. On synthetically generated signals, the proposed technique offers up to one order of improvement in estimating the signal parameters in the low SNR regime compared with the benchmark techniques, while performing on par with them in the high SNR regime. We demonstrate an application to real-world ultrasound signals and show that the proposed technique offers superior reconstruction performance with respect to the benchmarks.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2998-3013"},"PeriodicalIF":5.8,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144639855","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

Low Tensor-Rank Adaptation of Kolmogorov–Arnold Networks Kolmogorov-Arnold网络的低张量秩自适应

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-14 DOI: 10.1109/TSP.2025.3588910

Yihang Gao;Michael K. Ng;Vincent Y. F. Tan

{"title":"Low Tensor-Rank Adaptation of Kolmogorov–Arnold Networks","authors":"Yihang Gao;Michael K. Ng;Vincent Y. F. Tan","doi":"10.1109/TSP.2025.3588910","DOIUrl":"10.1109/TSP.2025.3588910","url":null,"abstract":"Kolmogorov–Arnold networks (KANs) have demonstrated their potential as an alternative to multi-layer perceptrons (MLPs) in various domains, especially for science-related tasks. However, transfer learning of KANs remains a relatively unexplored area. In this paper, inspired by Tucker decomposition of tensors and evidence on the low tensor-rank structure in KAN parameter updates, we develop low tensor-rank adaptation (LoTRA) for fine-tuning KANs. We study the expressiveness of LoTRA based on Tucker decomposition approximations. Furthermore, we provide a theoretical analysis to select the learning rates for each LoTRA component to enable efficient training. Our analysis also shows that using identical learning rates across all components leads to inefficient training, highlighting the need for an adaptive learning rate strategy. Beyond theoretical insights, we explore the application of LoTRA for efficiently solving various partial differential equations (PDEs) by fine-tuning KANs. Additionally, we propose Slim KANs that incorporate the inherent low-tensor-rank properties of KAN parameter tensors to reduce model size while maintaining superior performance. Experimental results validate the efficacy of the proposed learning rate selection strategy and demonstrate the effectiveness of LoTRA for transfer learning of KANs in solving PDEs. Further evaluations on Slim KANs for function representation and image classification tasks highlight the expressiveness of LoTRA and the potential for parameter reduction through low tensor-rank decomposition.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3107-3123"},"PeriodicalIF":5.8,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144629702","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

Block Tensor Ring Decomposition: Theory and Application 块张量环分解：理论与应用

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-14 DOI: 10.1109/TSP.2025.3589059

Sheng Liu;Xi-Le Zhao;Hao Zhang

{"title":"Block Tensor Ring Decomposition: Theory and Application","authors":"Sheng Liu;Xi-Le Zhao;Hao Zhang","doi":"10.1109/TSP.2025.3589059","DOIUrl":"10.1109/TSP.2025.3589059","url":null,"abstract":"Recently, tensor decompositions have received significant attention for processing multi-dimensional signals, especially representative by the block-term decomposition (BTD) family and the tensor network decomposition (TND) family. However, these two families have long been isolated from each other, with their respective wisdom neither inspiring nor benefiting each other. To address this dilemma, we propose a block tensor ring decomposition (BTRD), which decomposes an <inline-formula><tex-math>$N$</tex-math></inline-formula>th-order tensor into a sum of outer products between basic vector factors and the <inline-formula><tex-math>$(N-1)$</tex-math></inline-formula>th-order coefficient tensors, which are further represented using a tensor ring. The benefit of the BTRD is that it can better explore outer multiple components structure of the tensor and inner tensor topology of each component. To examine the potential of the proposed BTRD, we apply it to a low-rank tensor completion model as a representative task and prove a theoretical generalization error bound which provides a theoretical perspective to support the advantages of the proposed model for higher-order tensors. To address the resulting optimization problem, we apply an efficient proximal alternating minimization (PAM)-based algorithm with a theoretical convergence guarantee. Extensive experimental results on real-world signal data (color videos and light field images) demonstrate the superiority of the proposed model against the state-of-the-art baseline models.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3029-3043"},"PeriodicalIF":5.8,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144629704","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

Adaptive Federated Learning Over the Air 空中自适应联邦学习

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-14 DOI: 10.1109/TSP.2025.3585002

Chenhao Wang;Zihan Chen;Nikolaos Pappas;Howard H. Yang;Tony Q. S. Quek;H. Vincent Poor

{"title":"Adaptive Federated Learning Over the Air","authors":"Chenhao Wang;Zihan Chen;Nikolaos Pappas;Howard H. Yang;Tony Q. S. Quek;H. Vincent Poor","doi":"10.1109/TSP.2025.3585002","DOIUrl":"10.1109/TSP.2025.3585002","url":null,"abstract":"We propose a federated version of adaptive gradient methods, particularly AdaGrad and Adam, within the framework of over-the-air model training. This approach capitalizes on the inherent superposition property of wireless channels, facilitating fast and scalable parameter aggregation. Meanwhile, it enhances the robustness of the model training process by dynamically adjusting the stepsize in accordance with the global gradient update. We derive the convergence rate of the training algorithms for a broad spectrum of nonconvex loss functions, encompassing the effects of channel fading, and interference that follows a heavy-tailed distribution. Our analysis shows that the AdaGrad-based algorithm converges to a stationary point at the rate of <inline-formula><tex-math>$mathcal{O}(ln{(T)}/{T^{1-frac{1}{alpha}}})$</tex-math></inline-formula>, where <inline-formula><tex-math>$alpha$</tex-math></inline-formula> represents the tail index of the electromagnetic interference. This result indicates that the level of heavy-tailedness in interference distribution plays a crucial role in the training efficiency: the heavier the tail, the slower the algorithm converges. In contrast, an Adam-like algorithm converges at the <inline-formula><tex-math>$mathcal{O}(1/T)$</tex-math></inline-formula> rate, demonstrating its advantage in expediting the model training process. We conduct extensive experiments that corroborate our theoretical findings and affirm the practical efficacy of our proposed federated adaptive gradient methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3187-3202"},"PeriodicalIF":5.8,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144629703","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

Compressed Sensor Caching and Collaborative Sparse Data Recovery with Anchor Alignment 压缩传感器缓存与锚对齐协同稀疏数据恢复

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-11 DOI: 10.1109/tsp.2025.3588354

Yi-Jen Yang, Ming-Hsun Yang, Jwo-Yuh Wu, Y.-W. Peter Hong

引用次数: 0

Kalman Filter Aided Federated Koopman Learning 卡尔曼滤波辅助联邦库普曼学习

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-10 DOI: 10.1109/TSP.2025.3587329

Yutao Chen;Wei Chen

{"title":"Kalman Filter Aided Federated Koopman Learning","authors":"Yutao Chen;Wei Chen","doi":"10.1109/TSP.2025.3587329","DOIUrl":"10.1109/TSP.2025.3587329","url":null,"abstract":"Real-time control and estimation are pivotal for applications such as industrial automation and future healthcare. The realization of this vision relies heavily on efficient interactions with nonlinear systems. Therefore, Koopman learning, which leverages the power of deep learning to linearize nonlinear systems, has been one of the most successful examples of mitigating the complexity inherent in nonlinearity. However, the existing literature assumes access to accurate system states and abundant high-quality data for Koopman analysis, which is usually impractical in real-world scenarios. To fill this void, this paper considers the case where only observations of the system are available and where the observation data is insufficient to accomplish an independent Koopman analysis. To this end, we propose Kalman Filter aided Federated Koopman Learning (KF-FedKL), which pioneers the combination of Kalman filtering and federated learning with Koopman analysis. By doing so, we can achieve collaborative linearization with privacy guarantees. Specifically, we employ a straightforward yet efficient loss function to drive the training of a deep Koopman network for linearization. To obtain system information devoid of individual information from observation data, we leverage the unscented Kalman filter and the unscented Rauch-Tung-Striebel smoother. To achieve collaboration between clients, we adopt the federated learning framework and develop a modified FedAvg algorithm to orchestrate the collaboration. A convergence analysis of the proposed framework is also presented. Finally, through extensive numerical simulations, we showcase the performance of KF-FedKL under various situations.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2879-2895"},"PeriodicalIF":5.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144603498","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