IEEE Transactions on Signal Processing最新文献_第6页

ALPCAH: Subspace Learning for Sample-Wise Heteroscedastic Data

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-01-31 DOI: 10.1109/TSP.2025.3537867

Javier Salazar Cavazos;Jeffrey A. Fessler;Laura Balzano

{"title":"ALPCAH: Subspace Learning for Sample-Wise Heteroscedastic Data","authors":"Javier Salazar Cavazos;Jeffrey A. Fessler;Laura Balzano","doi":"10.1109/TSP.2025.3537867","DOIUrl":"10.1109/TSP.2025.3537867","url":null,"abstract":"Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction. However, some applications involve heterogeneous data that vary in quality due to noise characteristics associated with each data sample. Heteroscedastic methods aim to deal with such mixed data quality. This paper develops a subspace learning method, named ALPCAH, that can estimate the sample-wise noise variances and use this information to improve the estimate of the subspace basis associated with the low-rank structure of the data. Our method makes no distributional assumptions of the low-rank component and does not assume that the noise variances are known. Further, this method uses a soft rank constraint that does not require subspace dimension to be known. Additionally, this paper develops a matrix factorized version of ALPCAH, named LR-ALPCAH, that is much faster and more memory efficient at the cost of requiring subspace dimension to be known or estimated. Simulations and real data experiments show the effectiveness of accounting for data heteroscedasticity compared to existing algorithms. Code available at <uri>https://github.com/javiersc1/ALPCAH</uri>.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"876-886"},"PeriodicalIF":4.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072339","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 Low-Rank Projected Proximal Gradient Method for Spectral Compressed Sensing

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-01-31 DOI: 10.1109/TSP.2025.3536846

Xi Yao;Wei Dai

{"title":"A Low-Rank Projected Proximal Gradient Method for Spectral Compressed Sensing","authors":"Xi Yao;Wei Dai","doi":"10.1109/TSP.2025.3536846","DOIUrl":"10.1109/TSP.2025.3536846","url":null,"abstract":"This paper presents a new approach to the recovery of spectrally sparse signals (SSS) from partially observed entries, addressing challenges posed by large-scale data and heavy-noise environments. The SSS reconstruction can be formulated as a non-convex low-rank Hankel recovery problem. Traditional formulations for SSS recovery often suffer from reconstruction inaccuracies due to unequally weighted norms and over-relaxation of the Hankel structure in noisy conditions. Additionally, a critical limitation of standard proximal gradient (PG) methods for solving this optimization problem is their slow convergence. We overcome this issue by introducing a more accurate formulation and proposing the Low-rank Projected Proximal Gradient (LPPG) method, designed to efficiently converge to stationary points through a two-step process. The first step involves a modified PG approach, allowing for a constant step size independent of the signal size, significantly accelerating the gradient descent phase. The second step employs a subspace projection strategy, optimizing within a low-rank matrix space to further decrease the objective function. Both steps of the LPPG method are meticulously tailored to exploit the intrinsic low-rank and Hankel structures of the problem, thereby enhancing computational efficiency. Numerical simulations demonstrate substantial improvements in both efficiency and recovery accuracy of the LPPG method compared to existing benchmark algorithms. This performance gain is particularly pronounced in scenarios with significant noise, showcasing the method's robustness and applicability to large-scale SSS recovery tasks.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"691-705"},"PeriodicalIF":4.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072422","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

Jump Plus AM-FM Mode Decomposition

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-01-31 DOI: 10.1109/TSP.2025.3535822

Mojtaba Nazari;Anders Rosendal Korshøj;Naveed ur Rehman

{"title":"Jump Plus AM-FM Mode Decomposition","authors":"Mojtaba Nazari;Anders Rosendal Korshøj;Naveed ur Rehman","doi":"10.1109/TSP.2025.3535822","DOIUrl":"10.1109/TSP.2025.3535822","url":null,"abstract":"A novel approach for decomposing a nonstationary signal into amplitude- and frequency-modulated (AM-FM) oscillations and discontinuous (jump) components is proposed. Current nonstationary signal decomposition methods are designed to either obtain constituent AM-FM oscillatory modes or the discontinuous and residual components from the data, separately. Yet, many real-world signals of interest simultaneously exhibit both behaviors i.e., jumps and oscillations. Currently, no available method can extract jumps and AM-FM oscillatory components directly from the data. In our novel approach, we design and solve a variational optimization problem to accomplish this task. The optimization formulation includes a regularization term to minimize the bandwidth of all signal modes for effective oscillation modeling, and a prior for extracting the jump component. Our approach addresses the limitations of conventional AM-FM signal decomposition methods in extracting jumps and the limitations of existing jump extraction methods in decomposing multiscale oscillations. By employing an optimization framework that accounts for both multiscale oscillatory components and discontinuities, the proposed method shows superior performance compared to existing decomposition techniques. We demonstrate the effectiveness of our approach on synthetic, real-world, single-channel, and multivariate data, highlighting its utility in three specific applications: earth's electric field signals, electrocardiograms (ECG), and electroencephalograms (EEG).","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1081-1093"},"PeriodicalIF":4.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072340","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

Jammer-Resilient Time Synchronization in the MIMO Uplink

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-01-29 DOI: 10.1109/TSP.2025.3536012

Gian Marti;Flurin Arquint;Christoph Studer

引用次数: 0

Near-Field High-Speed User Sensing in Wideband mmWave Communications: Algorithms and Bounds

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-01-29 DOI: 10.1109/TSP.2025.3535691

Hongxia Miao;Mugen Peng

{"title":"Near-Field High-Speed User Sensing in Wideband mmWave Communications: Algorithms and Bounds","authors":"Hongxia Miao;Mugen Peng","doi":"10.1109/TSP.2025.3535691","DOIUrl":"10.1109/TSP.2025.3535691","url":null,"abstract":"Integrated sensing and communications (ISAC) has been expected to be a key technique in the sixth-generation cellular networks. With the increase of carrier frequency (to millimeter-wave or Terahertz spectrum) and antenna array size (to extremely large-scale antenna) in wireless communications, the near-field area is enlarged and cannot be ignored. Accordingly, the channel model and its estimation algorithms are changed, which bring new chances in ISAC. However, the impact of both Doppler and spatial wideband effects, caused by high mobility and multicarriers, on sensing performance using communication signals is not well studied. In this study, these two effects are shown to be helpful in user sensing. First, the channel model is proposed for a high-speed moving user transmitting an orthogonal frequency division multiplex (OFDM) signal, where there are six unknown parameters. Then, the Cramer-Rao lower bounds (CRLB) for joint six parameter estimation is determined, where the impact of the near-field parameter and the velocity on the CRLB of positioning are discussed and quantified. Further, to compensate for the deficiency that the CRLB is tight only in high signal-to-noise-ratio (SNR) scenarios, we derive the Ziv-Zakai bound (ZZB) for positioning by exploiting the prior information on positioning parameters. Subsequently, a joint position and velocity parameter estimation algorithm is designed by first performing a discrete fractional Fourier transform on the received signal to obtain a coarse estimation and then refining it by Newton-based refinement. Numerical results coincide with our analysis.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"919-935"},"PeriodicalIF":4.6,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056441","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

Combating Interference for Over-the-Air Federated Learning: A Statistical Approach via RIS

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-01-29 DOI: 10.1109/TSP.2025.3536023

Wei Shi;Jiacheng Yao;Wei Xu;Jindan Xu;Xiaohu You;Yonina C. Eldar;Chunming Zhao

{"title":"Combating Interference for Over-the-Air Federated Learning: A Statistical Approach via RIS","authors":"Wei Shi;Jiacheng Yao;Wei Xu;Jindan Xu;Xiaohu You;Yonina C. Eldar;Chunming Zhao","doi":"10.1109/TSP.2025.3536023","DOIUrl":"10.1109/TSP.2025.3536023","url":null,"abstract":"Over-the-air computation (AirComp) integrates analog communication with task-oriented computation, serving as a key enabling technique for communication-efficient federated learning (FL) over wireless networks. However, owing to its analog characteristics, AirComp-enabled FL (AirFL) is vulnerable to both unintentional and intentional interference. In this paper, we aim to attain robustness in AirComp aggregation against interference via reconfigurable intelligent surface (RIS) technology to artificially reconstruct wireless environments. Concretely, we establish performance objectives tailored for interference suppression in wireless FL systems, aiming to achieve unbiased gradient estimation and reduce its mean square error (MSE). Oriented at these objectives, we introduce the concept of phase-manipulated favorable propagation and channel hardening for AirFL, which relies on the adjustment of RIS phase shifts to realize statistical interference elimination and reduce the error variance of gradient estimation. Building upon this concept, we propose two robust aggregation schemes of power control and RIS phase shifts design, both ensuring unbiased gradient estimation in the presence of interference. Theoretical analysis of the MSE and FL convergence affirms the anti-interference capability of the proposed schemes. It is observed that computation and interference errors diminish by an order of <inline-formula><tex-math>$mathbf{mathcal{O}}left(frac{textbf{1}}{boldsymbol{N}}right)$</tex-math></inline-formula> where <inline-formula><tex-math>$N$</tex-math></inline-formula> is the number of RIS elements, and the ideal convergence rate without interference can be asymptotically achieved by increasing <inline-formula><tex-math>$N$</tex-math></inline-formula>. Numerical results confirm the analytical results and validate the superior performance of the proposed schemes over existing baselines.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"936-953"},"PeriodicalIF":4.6,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143057111","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

Gaussian Multi-Target Filtering With Target Dynamics Driven by a Stochastic Differential Equation

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-01-29 DOI: 10.1109/TSP.2025.3535556

Ángel F. García-Fernández;Simo Särkkä

引用次数: 0

Gohberg-Semencul Toeplitz Covariance Estimation via Autoregressive Parameters

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-01-29 DOI: 10.1109/TSP.2025.3536101

Benedikt Böck;Dominik Semmler;Benedikt Fesl;Michael Baur;Wolfgang Utschick

{"title":"Gohberg-Semencul Toeplitz Covariance Estimation via Autoregressive Parameters","authors":"Benedikt Böck;Dominik Semmler;Benedikt Fesl;Michael Baur;Wolfgang Utschick","doi":"10.1109/TSP.2025.3536101","DOIUrl":"10.1109/TSP.2025.3536101","url":null,"abstract":"The use of prior structural knowledge is essential for the estimation of covariance matrices and their inverses when only few data samples are accessible. A well-known example is the knowledge that the covariance matrix is Toeplitz-structured, which occurs when dealing with wide-sense-stationary processes. Exploiting the close relation between autoregressive parameters and inverse covariance matrices, this paper introduces a new class of estimators for Toeplitz-structured covariance matrices and their inverses. To achieve this, we derive novel constraint sets for autoregressive parameters by leveraging their connection to the so-called Gohberg-Semencul decomposition. While these constraint sets guarantee the corresponding inverse covariance matrix to be positive definite and, thus, enable a proper estimation of the covariance matrix by inversion, they also build a means to control the estimator's performance by hyperparameter tuning. The derived constraint sets comprise simple box constraints enabling computationally cheap estimators in closed form. Due to the ensured positive definiteness, the proposed estimators perform well for both the estimation of the covariance matrix and its inverse. Extensive simulation results validate the proposed estimators’ efficacy for several standard Toeplitz-structured covariance matrices commonly employed in a wide range of applications.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"858-875"},"PeriodicalIF":4.6,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10857370","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056375","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

Asymptotic Error Rates for Point Process Classification

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-01-27 DOI: 10.1109/TSP.2025.3531373

Xinhui Rong;Victor Solo

引用次数: 0

A Distributed Multi-Objective Detection Method for Multi-Sensor Systems With Unknown Local SNR

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-01-27 DOI: 10.1109/TSP.2025.3533275

Chang Gao;Qingfu Zhang;Pramod K. Varshney;Xi Lin;Hongwei Liu

{"title":"A Distributed Multi-Objective Detection Method for Multi-Sensor Systems With Unknown Local SNR","authors":"Chang Gao;Qingfu Zhang;Pramod K. Varshney;Xi Lin;Hongwei Liu","doi":"10.1109/TSP.2025.3533275","DOIUrl":"10.1109/TSP.2025.3533275","url":null,"abstract":"Distributed detection, which fuses the preprocessed observations of the same area from local sensors, can generally improve target detection performance. For scenarios in practical applications where sensors cannot obtain the target signal-to-noise ratio (SNR) parameters in advance, non-coherent integration is mostly used for distributed detection. However, this detector is equivalent to the optimal detector only under the condition that the target SNRs of all the sensors are exactly the same. This condition is quite stringent for the observation of non-cooperative targets. This paper first compares the performance of traditional optimal detectors, the non-coherent integration (NCI) detector, and the single-sensor detector from a unified perspective based on the concept of Pareto optimality. Then, from the perspective of multi-objective optimization, the fusion rule and corresponding parameter learning method are designed. Theoretical analysis shows that the proposed non-identical SNR detection fusion rule possesses weak Pareto optimality. Simulation experiments demonstrate that the proposed method effectively achieves a trade-off between the optimal detection performance across sensors with multiple SNRs. Compared to the optimal detector in the presence of mismatch between the assumed and actual SNR of the target, the proposed method can achieve a significant improvement in detection performance. Additionally, the proposed method outperforms the NCI detector in scenarios where the SNR distributions of target observations across different sensors exhibit greater diversity.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"649-663"},"PeriodicalIF":4.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050363","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