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

Dynamic Transmit-Receive Beampattern Optimization of Colocated MIMO Radars for Multi-Target Tracking Under Suppression Jamming 抑制干扰下多目标定位MIMO雷达动态收发波束优化

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-23 DOI: 10.1109/TSP.2025.3572503

Jun Sun;Wei Yi;Ye Yuan;Pramod K. Varshney

{"title":"Dynamic Transmit-Receive Beampattern Optimization of Colocated MIMO Radars for Multi-Target Tracking Under Suppression Jamming","authors":"Jun Sun;Wei Yi;Ye Yuan;Pramod K. Varshney","doi":"10.1109/TSP.2025.3572503","DOIUrl":"10.1109/TSP.2025.3572503","url":null,"abstract":"In this paper, we propose a dynamic transmit and receive beampattern optimization (DTRBO) method for colocated MIMO (C-MIMO) radars in cognitive multi-target tracking (MTT) scenarios under suppression jamming. To accurately account for the effects of suppression jamming on MTT performance, we develop a comprehensive signal model that integrates the complete transmit-receive beamforming process into the resource optimization framework. Based on this enhanced signal model, we derive an enumeration-based posterior Cramér-Rao lower bound (PCRLB), which incorporates both measurement errors and target miss detections, providing a more accurate performance metric under suppression jamming. Using the derived PCRLB, the DTRBO strategy is formulated to dynamically optimize transmit and receive beams for each tracking interval, thereby maximizing global MTT performance. Due to the coupling of optimization variables and the non-convexity of the derived PCRLB, the resulting optimization problem is computationally challenging. To address this, we propose a partition-based three-stage iterative method to decouple the variables, employing a first-order Taylor expansion-based convex approximation technique to solve the non-convex problem efficiently. Simulation results demonstrate the effectiveness and the superiority of the proposed DTRBO strategy in terms of both MTT performance and anti-jamming capabilities, outperforming existing approaches that omit the beampattern modeling process in resource-aware optimization.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2271-2287"},"PeriodicalIF":4.6,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144130582","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

Feasibility Study and Optimum Sensor Path Planning for Localization by Doppler Derivatives 多普勒导数定位的可行性研究及传感器路径优化规划

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-23 DOI: 10.1109/TSP.2025.3573120

Xiaochuan Ke;K. C. Ho

引用次数: 0

Direction Finding in Partly Calibrated Arrays With Hybrid Imperfections 混合缺陷部分校准阵列的测向

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-21 DOI: 10.1109/TSP.2025.3571836

Yihan Su;Lei Wang;Zhiyong Hu;Yimin Liu

引用次数: 0

Generalization of Geometric Graph Neural Networks With Lipschitz Loss Functions 用 Lipschitz 损失函数泛化几何图神经网络

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-21 DOI: 10.1109/TSP.2025.3553378

Zhiyang Wang;Juan Cerviño;Alejandro Ribeiro

引用次数: 0

Robust Online Reconstruction of Continuous-Time Signals From a Lean Spike Train Ensemble Code 连续时间信号的鲁棒在线重构

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-20 DOI: 10.1109/TSP.2025.3569798

Anik Chattopadhyay;Arunava Banerjee

{"title":"Robust Online Reconstruction of Continuous-Time Signals From a Lean Spike Train Ensemble Code","authors":"Anik Chattopadhyay;Arunava Banerjee","doi":"10.1109/TSP.2025.3569798","DOIUrl":"10.1109/TSP.2025.3569798","url":null,"abstract":"Sensory stimuli in animals are encoded into spike trains by neurons, offering advantages such as sparsity, energy efficiency, and high temporal resolution. This paper presents a signal processing framework that deterministically encodes continuous-time signals into biologically feasible spike trains, and addresses the questions about representable signal classes and reconstruction bounds. The framework considers encoding of a signal through spike trains generated by an ensemble of neurons using a convolve-then-threshold mechanism with various convolution kernels. A closed-form solution to the inverse problem, from spike trains to signal reconstruction, is derived in the Hilbert space of shifted kernel functions, ensuring sparse representation of a generalized Finite Rate of Innovation (FRI) class of signals. Additionally, inspired by real-time processing in biological systems, an efficient iterative version of the optimal reconstruction is formulated that considers only a finite window of past spikes, ensuring robustness of the technique to ill-conditioned encoding; convergence guarantees of the windowed reconstruction to the optimal solution are then provided. Experiments on a large audio dataset demonstrate excellent reconstruction accuracy at spike rates as low as one-third of the Nyquist rate, while showing clear competitive advantage in comparison to state-of-the-art sparse coding techniques in the low spike rate regime.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2008-2021"},"PeriodicalIF":4.6,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144104783","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

Globally Optimal Max-Min Rate Joint Channel and Power Allocation for Hybrid NOMA-OMA Downlink Systems 混合NOMA-OMA下行系统全局最优最大最小速率联合信道和功率分配

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-19 DOI: 10.1109/TSP.2025.3553084

Tanin Sultana;Sorina Dumitrescu

{"title":"Globally Optimal Max-Min Rate Joint Channel and Power Allocation for Hybrid NOMA-OMA Downlink Systems","authors":"Tanin Sultana;Sorina Dumitrescu","doi":"10.1109/TSP.2025.3553084","DOIUrl":"10.1109/TSP.2025.3553084","url":null,"abstract":"This work proposes a globally optimal solution algorithm to the joint power allocation (PA) and channel allocation (CA) problem for downlink hybrid NOMA-OMA systems with the objective of maximizing the minimum user rate. In the hybrid NOMA-OMA scenario, the users are divided into clusters, each cluster shares one channel using NOMA (Non-Orthogonal Multiple Access), while different clusters are assigned channels orthogonally. The optimization problem is converted to the problem of maximizing the user rate under the constraint that all rates be equal. It is further decomposed into PA and CA subproblems, which are solved iteratively. The PA subproblem is handled by first deriving an analytical expression of the total power as a function of the common user rate, and then solving it via bisection search. The CA subproblem keeps the equal-rate assignment fixed and aims to find the CA that minimizes the total power. We prove that the CA subproblem is equivalent to a minimum bipartite graph matching problem, for which efficient algorithms exist. Finally, we demonstrate that the proposed iterative algorithm converges to the globally optimal solution after a finite number of iterations. In addition, we prove that the number of iterations is at most three when the power budget is sufficiently large. Extensive experiments demonstrate the effectiveness of the proposed scheme.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1674-1690"},"PeriodicalIF":4.6,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661538","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

Direct Multipath-Based SLAM 直接多路径SLAM

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-19 DOI: 10.1109/TSP.2025.3552747

Mingchao Liang;Erik Leitinger;Florian Meyer

{"title":"Direct Multipath-Based SLAM","authors":"Mingchao Liang;Erik Leitinger;Florian Meyer","doi":"10.1109/TSP.2025.3552747","DOIUrl":"10.1109/TSP.2025.3552747","url":null,"abstract":"Radio-based localization approaches that make use of reflections in the propagation environment to improve the accuracy and robustness of location estimates have a variety of potential applications in future wireless communication networks. Multipath-based simultaneous localization and mapping (SLAM) aims at estimating the position of agents and reflecting features in the environment by exploiting the relationship between the local geometry and multipath components (MPCs) in received radio signals. Existing multipath-based SLAM methods preprocess received radio signals using a channel estimator. The channel estimator lowers the data rate by extracting a set of dispersion parameters for each MPC. These parameters are then used as measurements for SLAM. Bayesian estimation for multipath-based SLAM is facilitated by the lower data rate. However, due to finite resolution capabilities limited by signal bandwidth, channel estimation is prone to errors and MPC parameters may be extracted incorrectly and lead to a reduced SLAM performance. We propose a multipath-based SLAM approach that directly uses received radio signals as inputs. A new statistical model that can effectively be represented by a factor graph is introduced. The factor graph is the starting point for the development of an efficient belief propagation (BP) method for multipath-based SLAM that avoids data preprocessing by a channel estimator. Numerical results based on synthetic and real data in challenging single-input, single-output (SISO) scenarios demonstrate that the proposed method outperforms conventional methods in terms of localization and mapping accuracy.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2336-2352"},"PeriodicalIF":4.6,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661537","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

Locally Differentially Private Online Federated Learning With Correlated Noise 基于相关噪声的局部差分私有在线联邦学习

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-19 DOI: 10.1109/TSP.2025.3553355

Jiaojiao Zhang;Linglingzhi Zhu;Dominik Fay;Mikael Johansson

引用次数: 0

Invariance Theory for Radar Detection in Disturbance With Kronecker Product Covariance Structure—Part I: Gaussian Environment Kronecker积协方差结构干扰下雷达探测的不变性理论第一部分：高斯环境

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-17 DOI: 10.1109/TSP.2025.3551204

Mengjiao Tang;Augusto Aubry;Antonio De Maio;Yao Rong

{"title":"Invariance Theory for Radar Detection in Disturbance With Kronecker Product Covariance Structure—Part I: Gaussian Environment","authors":"Mengjiao Tang;Augusto Aubry;Antonio De Maio;Yao Rong","doi":"10.1109/TSP.2025.3551204","DOIUrl":"10.1109/TSP.2025.3551204","url":null,"abstract":"This two-part paper addresses maximally invariant detection of range-spread targets embedded in disturbance characterized by an unknown Kronecker product-structured covariance matrix. Part I focuses on Gaussian interference, whereas Part II extends the study to compound-Gaussian, clutter-dominated environments. Leveraging the principle of invariance, this part identifies a suitable transformation group that effectively compresses the nuisance parameter space, ensuring the constant false alarm rate (CFAR) property (with respect to the Kronecker-structured covariance matrix) for all invariant detectors. A maximal invariant and an induced maximal invariant are subsequently derived, serving as powerful tools to guide the design of CFAR detectors. Some existing two-step CFAR detectors for this structured situation are expressed as functions of the derived maximal invariant. Furthermore, two novel detectors (whose CFARity holds true under some mild technical conditions) are devised: the former employs a pseudo-missing strategy by treating elements possibly contaminated by target signals as missing and utilizes an Expectation-Maximization algorithm to perform the covariance matrix estimation; the latter is based on the one-step generalized likelihood ratio test criterion and is implemented via an alternate optimization algorithm. Finally, their CFAR behavior and detection performance are assessed through numerical examples, demonstrating their superiority with respect to some conventional decision rules.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1577-1593"},"PeriodicalIF":4.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640562","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

Invariance Theory for Radar Detectionin Disturbance With Kronecker ProductCovariance Structure—Part II: CompoundGaussian Environment Kronecker积协方差结构干扰下雷达检测的不变性理论第二部分：复合高斯环境

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-17 DOI: 10.1109/TSP.2025.3551199

Mengjiao Tang;Augusto Aubry;Antonio De Maio;Yao Rong

{"title":"Invariance Theory for Radar Detectionin Disturbance With Kronecker ProductCovariance Structure—Part II: CompoundGaussian Environment","authors":"Mengjiao Tang;Augusto Aubry;Antonio De Maio;Yao Rong","doi":"10.1109/TSP.2025.3551199","DOIUrl":"10.1109/TSP.2025.3551199","url":null,"abstract":"In this part of the paper, we consider the invariant framework and the novel constant false alarm rate (CFAR) detector design of Part I in compound Gaussian clutter. Specifically, the focus is on detecting range-spread targets embedded in compound Gaussian clutter that exhibits a Kronecker covariance structure. A suitable transformation group has been identified, ensuring that invariance implies the fully CFAR property, i.e., with respect to both the Kronecker covariance matrix and the texture. A maximal invariant is derived and used to gain insightful re-expressions of some established two-step adaptive CFAR detectors. At the stage of detector design, the pseudo-missing strategy proposed in Part I is adapted to the compound Gaussian case and then integrated into the test architectures to yield modified adaptive detectors. Furthermore, the one-step generalized likelihood ratio test is derived. Both detection strategies result in fully CFAR detectors under some mild technical conditions, as evidenced by their invariance with respect to the identified transformation group. For performance evaluation, their CFAR behavior and detection probability are assessed and analyzed across different experimental setups and signal models, highlighting the superior performance of the newly proposed detectors compared to some conventional counterparts and to those that do not leverage the prior (Kronecker) structure.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1594-1610"},"PeriodicalIF":4.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640847","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