IEEE Transactions on Signal Processing最新文献_第9页

Regret Based Bayesian Sequential Experiment Design for Sensor Management 基于后悔的传感器管理贝叶斯序列实验设计

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-10 DOI: 10.1109/TSP.2025.3587260

Nicholas R. Olson;Robert W. Heath

{"title":"Regret Based Bayesian Sequential Experiment Design for Sensor Management","authors":"Nicholas R. Olson;Robert W. Heath","doi":"10.1109/TSP.2025.3587260","DOIUrl":"10.1109/TSP.2025.3587260","url":null,"abstract":"Measurement design is an important sub-problem arising in the applications of sensing and wireless communications. Sensing systems are often capable of performing different types of mutually exclusive measurement actions. In such systems, it is important to select measurement actions so as to efficiently gather samples which are most informative about the phenomena of interest. Bayesian sequential experiment design (BSED) offers a model-based framework with which to address sequential variations of such measurement design problems. Prior applications of BSED to sensing problems often consider measurement selection policies which maximize notions of expected information gain (EIG). In certain related settings, EIG based approaches have been shown to be less performant than policies designed to minimize notions of regret with respect to the information gain afforded by an ideal policy. Motivate by this, we develop a general framework based on partially observable Markov decision processes which allows for the design of BSED policies with respect to a notion of regret. We argue for the consideration of policies based on a myopic version of posterior sampling, termed MPS, and consider the application of this framework to the problem of passive non-coherent signal source localization and detection using codebook-based receive beamforming. We further develop a general approach for approximating posterior inference based on variational inference and a power law generalization of Bayes’ rule. We conduct an empirical analysis of the application of MPS and EIG to our considered application. Our results indicate that MPS outperforms EIG while providing improved robustness.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2956-2969"},"PeriodicalIF":5.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144603589","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

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

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-10 DOI: 10.1109/TSP.2025.3587521

Shixiong Wang;Wei Dai;Geoffrey Ye Li

{"title":"Distributionally Robust Adaptive Beamforming","authors":"Shixiong Wang;Wei Dai;Geoffrey Ye Li","doi":"10.1109/TSP.2025.3587521","DOIUrl":"10.1109/TSP.2025.3587521","url":null,"abstract":"As a fundamental technique in array signal processing, beamforming plays a crucial role in amplifying signals of interest (SoI) while mitigating interference plus noise (IPN). When uncertainties exist in the signal model or the data size of snapshots is limited, the performance of beamformers significantly degrades. In this article, we comprehensively study the conceptual system, theoretical analysis, and algorithmic design for robust beamforming against uncertainties in the assumed snapshot or IPN covariances. Since such robustness is specific to the probabilistic uncertainties of snapshots or IPN signals, it is referred to as distributional robustness. Particularly, four technical approaches for distributionally robust beamforming are proposed, including locally distributionally robust beamforming, globally distributionally robust beamforming, regularized beamforming, and Bayesian-nonparametric beamforming. In addition, we investigate the equivalence among the four technical approaches and suggest a unified distributionally robust beamforming framework. Moreover, we show that the resolution of power spectra estimation using distributionally robust beamforming can be greatly refined by incorporating the characteristics of subspace methods, and hence, the accuracy of IPN covariance reconstruction can be improved, especially when the interferers are close to the SoI. As a result, the robustness of beamformers based on IPN covariance estimation can be further enhanced.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2981-2997"},"PeriodicalIF":5.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144603499","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

Sensing-Aided Precoding With High-Dynamic Moving Scatterers 基于高动态移动散射体的传感辅助预编码

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-10 DOI: 10.1109/TSP.2025.3587089

Luning Lin;Chengwei Zhou;Hang Zheng;Zhiguo Shi;Sergiy A. Vorobyov;Robert W. Heath

{"title":"Sensing-Aided Precoding With High-Dynamic Moving Scatterers","authors":"Luning Lin;Chengwei Zhou;Hang Zheng;Zhiguo Shi;Sergiy A. Vorobyov;Robert W. Heath","doi":"10.1109/TSP.2025.3587089","DOIUrl":"10.1109/TSP.2025.3587089","url":null,"abstract":"Sensing information can be leveraged to reduce the overheads associated with establishing and maintaining multiple-input and multiple-output (MIMO) communication links. Such information can be acquired from integrated sensing and communication (ISAC) capabilities. In this paper, we use sensing information to make precoding for spatial multiplexing more robust in high-dynamic environments with both quasi-static and mobile scattering objects. The significant variability of scattered paths requires frequent reconfigurations of the precoding matrices. To address this, we propose a sensing-aided precoding scheme for channels with high dynamics, leveraging the identification and localization of moving targets to concentrate energy mainly on slow-varying paths. Specifically, the geometric structure of the time-varying channel matrix is analyzed to uncover deviations in the location parameters of moving targets. A channel division criterion is then devised to partition the channel matrix into two matrices characterizing the paths of background scatterers and moving targets, respectively. Exploiting the digital-analog dual orthogonality between these matrices, precoding and combining matrices are jointly designed to suppress transmission leakage over mobile paths in both analog and digital domains. Numerical simulations validate that the proposed method achieves high spectral efficiency without requiring explicit channel tracking, thereby reducing complexity and overheads compared to conventional MIMO precoding methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3061-3078"},"PeriodicalIF":5.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144603591","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

Harnessing Common Sparsity for Enhancing AMP-Based Activity Detection and Channel Estimation 利用公共稀疏性增强基于amp的活动检测和信道估计

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-10 DOI: 10.1109/TSP.2025.3585424

Hao Zhang;Yang Li;Qingfeng Lin;Yik-Chung Wu;H. Vincent Poor

{"title":"Harnessing Common Sparsity for Enhancing AMP-Based Activity Detection and Channel Estimation","authors":"Hao Zhang;Yang Li;Qingfeng Lin;Yik-Chung Wu;H. Vincent Poor","doi":"10.1109/TSP.2025.3585424","DOIUrl":"10.1109/TSP.2025.3585424","url":null,"abstract":"Joint activity detection and channel estimation present significant challenges in massive machine-type communication under grant-free random access. Due to the intermittency of grant-free random access, these tasks can be framed as a compressive sensing problem, where approximate message passing (AMP) has been a popular algorithm to recover the sparse channels. However, existing AMP-based algorithms typically estimate the channels at each antenna independently, largely ignoring the common sparsity support among channels at different antennas and access points. This oversight leads to inadequate performance of AMP-based activity detection and channel estimation, especially under short pilot sequence lengths. To leverage the common sparsity patterns of multiple unknown channel vectors, this paper proposes a Bayesian model that induces block sparsity through a consistent activity status across all channels associated with each device. By treating the activity probability of each device as an unknown parameter, this paper derives an AMP-based expectation-maximization (EM) algorithm to jointly learn the activity probability and the channels. It is shown theoretically that under mild conditions, when the number of antennas goes to infinity, the recovered active status is guaranteed to be the ground truth. Furthermore, to overcome the limitation of the standard AMP, whose convergence to the state evolution relies on large pilot sequence lengths, another algorithm based on the vector AMP, with its convergence depending on the number of devices rather than pilot sequence length, is also proposed. Simulation results demonstrate that the proposed algorithms require a much shorter pilot length than existing state-of-the-art AMP-based methods to achieve the same performance. Notably, under the same pilot length, the vector AMP-based EM algorithm achieves even higher detection accuracy than the covariance-based method in cell-free systems.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3220-3236"},"PeriodicalIF":5.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144603588","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 $mathcal{K}$-Divergence Based Approach for Robust Regression Analysis 基于$mathcal{K}$-散度的稳健回归分析方法

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-10 DOI: 10.1109/TSP.2025.3585830

Yair Sorek;Koby Todros

{"title":"A $mathcal{K}$-Divergence Based Approach for Robust Regression Analysis","authors":"Yair Sorek;Koby Todros","doi":"10.1109/TSP.2025.3585830","DOIUrl":"10.1109/TSP.2025.3585830","url":null,"abstract":"This paper deals with the problem of robust regression analysis in the presence of outliers in <italic>both</i> input and output data sets. In this context, we infer the input-output relation of a system by minimizing a new robust loss between the outputs and a presumed parametric function of the inputs. The considered loss is derived from an empirical estimate of a non-trivially modified version of the recently developed <inline-formula><tex-math>$mathcal{K}$</tex-math></inline-formula>-divergence (adapted here for regression analysis). This modified version utilizes a model-free data-weighting mechanism based on Parzen’s non-parametric <inline-formula><tex-math>$mathcal{K}$</tex-math></inline-formula>ernel density estimator, associated with the underlying joint distribution of the input and output data. The considered Parzen’s estimator involves two strictly positive smoothing “<inline-formula><tex-math>$mathcal{K}$</tex-math></inline-formula>”ernel functions. These are defined independently across the input and output domains with possibly different bandwidth parameters. This data-weighting strategy leads to mitigation of low-density contaminations attributed to different types of input and output outlying measurements. The considered approach is illustrated for robust training of GELU neural networks, with applications to function approximation and time-series prediction.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"3253-3269"},"PeriodicalIF":5.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144603592","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

Fine Grained Analysis and Optimization of Large Scale Automotive Radar Networks 大型汽车雷达网络的细粒度分析与优化

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-09 DOI: 10.1109/tsp.2025.3586880

Mohammad Taha Shah, Gourab Ghatak, Shobha Sundar Ram

引用次数: 0

RIS-Assisted High Resolution Radar Sensing ris辅助高分辨率雷达传感

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-08 DOI: 10.1109/TSP.2025.3586551

Martin V. Vejling;Hyowon Kim;Christophe A. N. Biscio;Henk Wymeersch;Petar Popovski

{"title":"RIS-Assisted High Resolution Radar Sensing","authors":"Martin V. Vejling;Hyowon Kim;Christophe A. N. Biscio;Henk Wymeersch;Petar Popovski","doi":"10.1109/TSP.2025.3586551","DOIUrl":"10.1109/TSP.2025.3586551","url":null,"abstract":"This paper analyzes monostatic sensing by a user equipment (UE) for a setting in which the UE is unable to resolve multiple targets due to their interference within a single resolution bin. It is shown how sensing accuracy, in terms of both detection rate and localization accuracy, can be boosted by a reconfigurable intelligent surface (RIS), which can be advantageously used to provide signal diversity and aid in resolving the targets. Specifically, assuming prior information on the presence of a cluster of targets, a RIS beam sweep procedure is used to facilitate the high resolution sensing. This setting requires a tailored Fisher analysis, as well as introduction of two new coherence concepts that are central to the derived theoretical bounds, namely the Cramér-Rao lower bound and a new upper bound on the detection probability. Next, we propose an orthogonal matching pursuit channel estimation algorithm combined with data association to fuse the information of the non-RIS signal and the RIS signal and perform sensing. Finally, we provide numerical results to verify the potential of RIS for improving sensor resolution, and to demonstrate that the proposed methods can realize this potential for RIS-assisted high resolution sensing.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2940-2955"},"PeriodicalIF":5.8,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144586310","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

Fractional-Order RNNs: A Universal Approximation Framework for Non-Local Dynamic System Modeling 分数阶rnn：非局部动态系统建模的通用逼近框架

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-07 DOI: 10.1109/tsp.2025.3585881

Guoqing Jiang, Xiaoya Gao, Ran Huang, Cong Wu

引用次数: 0

An Optimal Pairwise Merge Algorithm Improves the Quality and Consistency of Nonnegative Matrix Factorization 一种最优成对合并算法提高了非负矩阵分解的质量和一致性

IF 5.8 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-04 DOI: 10.1109/TSP.2025.3585893

Youdong Guo;Timothy E. Holy

{"title":"An Optimal Pairwise Merge Algorithm Improves the Quality and Consistency of Nonnegative Matrix Factorization","authors":"Youdong Guo;Timothy E. Holy","doi":"10.1109/TSP.2025.3585893","DOIUrl":"10.1109/TSP.2025.3585893","url":null,"abstract":"Non-negative matrix factorization (NMF) is widely used for dimensionality reduction of large datasets and is an important feature extraction technique for source separation. However, NMF algorithms may converge to poor local minima, or to one of several minima with similar objective value but differing feature parametrizations. Here we show that some of these weaknesses may be mitigated by performing NMF in a higher-dimensional feature space and then iteratively combining components with an efficient and analytically solvable pairwise merge strategy. Both theoretical and experimental results demonstrate that our method allows optimizers to escape poor minima and achieve greater consistency of the solutions. Despite these extra steps, our approach exhibits computational performance similar to established methods by reducing the occurrence of “plateau phenomena” near saddle points. Our method is compatible with a variety of standard NMF algorithms and exhibits an average performance that exceeds all algorithms tested. Thus, this can be recommended as a preferred approach for most applications of NMF.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2862-2878"},"PeriodicalIF":5.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11071940","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144566631","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

Causal Link Discovery with Unequal Edge Error Tolerance 不等边容错的因果链发现

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-04 DOI: 10.1109/tsp.2025.3585825

Joni Shaska, Urbashi Mitra

引用次数: 0