IEEE Transactions on Signal Processing最新文献_第3页

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 5.4 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

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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

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Proportionally Fair Joint Power and Channel Allocation for Hybrid NOMA-OMA Downlink Systems 混合NOMA-OMA下行系统的比例公平联合功率和信道分配

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-03 DOI: 10.1109/tsp.2025.3584665

Tanin Sultana, Sorina Dumitrescu

引用次数: 0

Decentralised Variational Inference Frameworks for Multi-object Tracking on Sensor Networks 传感器网络中多目标跟踪的分散变分推理框架

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-07-02 DOI: 10.1109/tsp.2025.3584248

Qing Li, Runze Gan, Simon J. Godsill

引用次数: 0

Hybrid Population Monte Carlo 混合种群蒙特卡罗

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-30 DOI: 10.1109/TSP.2025.3583988

Ali Mousavi;Víctor Elvira

{"title":"Hybrid Population Monte Carlo","authors":"Ali Mousavi;Víctor Elvira","doi":"10.1109/TSP.2025.3583988","DOIUrl":"10.1109/TSP.2025.3583988","url":null,"abstract":"Importance sampling (IS) is a powerful Monte Carlo (MC) technique for approximating intractable integrals, for instance in Bayesian inference. The performance of IS relies heavily on the appropriate choice of the so-called proposal distribution. Adaptive IS (AIS) methods iteratively improve target estimates by adapting the proposal distribution. Recent AIS research focuses on enhancing proposal adaptation for high-dimensional problems, while addressing the challenge of multi-modal targets. In this paper, a new class of AIS methods is presented, utilizing a hybrid approach that incorporates weighted samples and proposal distributions to enhance performance. This approach belongs to the family of population Monte Carlo (PMC) algorithms, where a population of proposals is adapted to better approximate the target distribution. The proposed hybrid population Monte Carlo (HPMC) implements a novel two-step adaptation mechanism. In the first step, a hybrid method is used to generate the population of the preliminary proposal locations based on both weighted samples and location parameters. We use Hamiltonian Monte Carlo (HMC) to generate the preliminary proposal locations. HMC has a good exploratory behavior, especially in high dimension scenarios. In the second step, the novel cooperation algorithms are performing to find the final proposals for the next iteration. HPMC achieves a significant performance improvement in high-dimensional problems when compared to the state-of-the-art algorithms. We discuss the statistical properties of HPMC and show its high performance in two challenging benchmarks.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2676-2687"},"PeriodicalIF":4.6,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520578","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

Efficient Off-Grid Bayesian Parameter Estimation for Kronecker-Structured Signals kronecker结构信号的有效离网贝叶斯参数估计

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-30 DOI: 10.1109/TSP.2025.3583895

Yanbin He;Geethu Joseph

引用次数: 0

A Radio Map Approach for Reduced Pilot CSI Tracking in Massive MIMO Networks 大规模MIMO网络中减少导频CSI跟踪的无线电地图方法

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-30 DOI: 10.1109/tsp.2025.3584229

Yuanshuai Zheng, Junting Chen

引用次数: 0

FieldFormer: Self-Supervised Reconstruction of Physical Fields via Tensor Attention Prior FieldFormer：基于张量注意先验的自监督物理场重建

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-27 DOI: 10.1109/TSP.2025.3580374

Panqi Chen;Siyuan Li;Lei Cheng;Xiao Fu;Yik-Chung Wu;Sergios Theodoridis

{"title":"FieldFormer: Self-Supervised Reconstruction of Physical Fields via Tensor Attention Prior","authors":"Panqi Chen;Siyuan Li;Lei Cheng;Xiao Fu;Yik-Chung Wu;Sergios Theodoridis","doi":"10.1109/TSP.2025.3580374","DOIUrl":"10.1109/TSP.2025.3580374","url":null,"abstract":"Reconstructing physical field tensors from <italic>in situ</i> observations, such as radio maps and ocean sound speed fields, is crucial for enabling environment-aware decision making in various applications, e.g., wireless communications and underwater acoustics. Field data reconstruction is often challenging, due to the limited and noisy nature of the observations, necessitating the incorporation of prior information to aid the reconstruction process. Deep neural network-based data-driven structural constraints (e.g., “deeply learned priors”) have showed promising performance. However, this family of techniques faces challenges such as model mismatches between training and testing phases. This work introduces FieldFormer, a self-supervised neural prior learned solely from the limited <italic>in situ</i> observations without the need of offline training. Specifically, the proposed framework starts with modeling the fields of interest using the tensor Tucker model of a high multilinear rank, which ensures a universal approximation property for all fields. In the sequel, an attention mechanism is incorporated to learn the sparsity pattern that underlies the core tensor in order to reduce the solution space. In this way, a “complexity-adaptive” neural representation, grounded in the Tucker decomposition, is obtained that can flexibly represent various types of fields. A theoretical analysis is provided to support the recoverability of the proposed design. Moreover, extensive experiments, using various physical field tensors, demonstrate the superiority of the proposed approach compared to state-of-the-art baselines. The code is available at <uri>https://github.com/OceanSTARLab/FieldFormer</uri>.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2704-2718"},"PeriodicalIF":4.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503341","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

Infinite Factorial Linear Dynamical Systems for Transient Signal Detection 用于暂态信号检测的无穷阶乘线性动力系统

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-06-27 DOI: 10.1109/TSP.2025.3582215

Jiadi Bao;Yatong Wang;Yunjie Li;Mengtao Zhu;Shafei Wang

{"title":"Infinite Factorial Linear Dynamical Systems for Transient Signal Detection","authors":"Jiadi Bao;Yatong Wang;Yunjie Li;Mengtao Zhu;Shafei Wang","doi":"10.1109/TSP.2025.3582215","DOIUrl":"10.1109/TSP.2025.3582215","url":null,"abstract":"Accurately detecting the transient signal of interest from the background signal is one of the fundamental tasks in signal processing. The most recent approaches assume the existence of a single background source and represent the background signal using a linear dynamical system, but this assumption might fail to capture the complexities of modern electromagnetic environments with multiple sources. To address this limitation, this paper proposes a method for detecting the transient signal in a background composed of an unknown number of emitters. The proposed method consists of two main tasks. First, a Bayesian nonparametric model called the infinite factorial linear dynamical systems is developed. The developed model is based on the Markov Indian buffet process and enables the representation and parameter learning of an unbounded number of background sources. This study also designs a parameter learning method for the infinite factorial linear dynamical systems using slice sampling and particle Gibbs with ancestor sampling. Second, a theoretically straightforward generalized likelihood ratio stopping time is defined, but it is computationally infeasible for factorial linear dynamical systems. To facilitate the computation, we derive the factorial Kalman forward filtering method and design a dependence structure for the underlying model, enabling the stopping time to be defined recursively. Then, the statistical performance of the proposed stopping time is investigated. Numerical simulations demonstrate the effectiveness of the proposed method and the validity of the theoretical results. The experimental results of the pulse signal detection under the condition of communication interference confirm the effectiveness and superiority of the proposed method.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2574-2589"},"PeriodicalIF":4.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144503342","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