IEEE Transactions on Signal Processing最新文献_第10页

Angular Parameter Estimation for Incoherently Distributed Sources With Single RF Chain 使用单射频链的非相干分布源的角度参数估计

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-08-20 DOI: 10.1109/TSP.2024.3446572

Ziyu Guo;Tao Yang;Peng Chen;Jun Han;Xiaoyang Zeng;Bo Hu

{"title":"Angular Parameter Estimation for Incoherently Distributed Sources With Single RF Chain","authors":"Ziyu Guo;Tao Yang;Peng Chen;Jun Han;Xiaoyang Zeng;Bo Hu","doi":"10.1109/TSP.2024.3446572","DOIUrl":"10.1109/TSP.2024.3446572","url":null,"abstract":"In this paper, we consider the problem of estimating the angular parameters, i.e., the nominal angle-of-arrivals (AoAs) and angular spreads, of incoherently distributed sources using the phased-array equipped with a single RF chain. We first derive the approximate Fourier series of the received power. The coefficients can be expressed in closed form with the angular parameters. In the case of single source, this finding directly suggests the design of the low-complexity algorithm that performs spatial sampling and discrete Fourier transform to estimate the Fourier series coefficients, from which the nominal AoA and angular spread can be obtained successively. In the case of multiple sources, we focus on one source at one time, and the multiples sources are handled one by one. Based on the Fourier series expression, the power fitting approach is proposed to build the nonlinear least-squares problem. Then, the semi-exhaustive search algorithm is developed to find the solution, which gives the angular parameters of the target source. Additionally, the approximate Cramer-Rao bound is derived as benchmark. The numerical results demonstrate that in certain cases, the proposed methods can even outperform the existing method that uses fully-digital array.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5244-5257"},"PeriodicalIF":4.6,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142022102","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

ECCM Strategies for Radar Systems Against Smart Noise-Like Jammers 雷达系统对抗智能噪声干扰器的 ECCM 战略

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-08-19 DOI: 10.1109/TSP.2024.3445530

Dario Benvenuti;Pia Addabbo;Gaetano Giunta;Goffredo Foglia;Danilo Orlando

{"title":"ECCM Strategies for Radar Systems Against Smart Noise-Like Jammers","authors":"Dario Benvenuti;Pia Addabbo;Gaetano Giunta;Goffredo Foglia;Danilo Orlando","doi":"10.1109/TSP.2024.3445530","DOIUrl":"10.1109/TSP.2024.3445530","url":null,"abstract":"In this paper, we address the problem of detecting a Noise-Like Jammer (NLJ) that does not quickly transmit all the available power but it gradually increases the transmitted power. This control strategy would prevent conventional electronic counter-countermeasures from revealing the presence of a noise power discontinuity in the window under test. As a consequence, the radar system under attack becomes blind requiring a reaction by an expert operator. In order to face such a situation, we devise two innovative NLJ detection architectures by assuming at the design stage specific models for the NLJ power variation. The first model is based on a linear variation law over the observation time, whereas in the second model, the NLJ power experiences unconstrained fluctuations in the window under test. Under these hypotheses, we resort to ad hoc modifications of the generalized likelihood ratio test, where the unknown parameters are replaced by suitable estimates obtained through iterative procedures. The performance analysis, carried out using synthetic data, shows the effectiveness and superiority of the proposed detectors over the conventional approach.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"3912-3926"},"PeriodicalIF":4.6,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142007417","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

Matrix Completion from One-Bit Dither Samples 从一位抖动采样完成矩阵补全

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-08-19 DOI: 10.1109/tsp.2024.3445289

Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian

引用次数: 0

Multiple-Time-Slot Multiple Access Binary Computation Offloading in the $K$-User Case K 用户情况下的多时隙多址二进制计算卸载

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-08-15 DOI: 10.1109/TSP.2024.3444194

Xiaomeng Liu;Timothy N. Davidson

{"title":"Multiple-Time-Slot Multiple Access Binary Computation Offloading in the $K$-User Case","authors":"Xiaomeng Liu;Timothy N. Davidson","doi":"10.1109/TSP.2024.3444194","DOIUrl":"10.1109/TSP.2024.3444194","url":null,"abstract":"When multiple devices seek to offload computational tasks to their access point, the nature of the multiple access scheme plays a critical role in the system performance. For a system with heterogeneous tasks, we adopt a time-slotted signaling architecture in which different numbers of devices transmit in each slot, subject to individual power constraints. We consider the problem of jointly selecting the devices that will offload, along with optimizing their communication resources (their powers and rates in each time slot, and the time slot lengths) so as to minimize the a weighted sum of the energy expended by the devices. We employ a customized tree search algorithm for the offloading decisions in which a resource allocation problem is solved at each node. For time-division multiple access (TDMA) and “rate optimal” multiple access, we obtain reduced-dimension convex formulations of the resource allocation problem. For non-orthogonal multiple access (NOMA) with independent decoding (ID) or fixed-order sequential decoding (FOSD) we show that the resource allocation problem has a difference-of-convex structure and we develop a successive convex approximation algorithm with feasible point pursuit. Furthermore, for the FOSD scheme we obtain a closed-form expression that provides the optimal decoding order when it is feasible, and efficient algorithms for finding a good decoding order when it is not. Our results capture the inherent tradeoffs between the complexity of a multiple access scheme (and its resource allocation algorithm), and its performance in the computation offloading application.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"3927-3944"},"PeriodicalIF":4.6,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141991939","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

HKF: Hierarchical Kalman Filtering With Online Learned Evolution Priors for Adaptive ECG Denoising HKF：分层卡尔曼滤波与在线学习进化先验用于自适应心电图去噪

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-08-14 DOI: 10.1109/TSP.2024.3443875

Guy Revach;Timur Locher;Nir Shlezinger;Ruud J. G. van Sloun;Rik Vullings

{"title":"HKF: Hierarchical Kalman Filtering With Online Learned Evolution Priors for Adaptive ECG Denoising","authors":"Guy Revach;Timur Locher;Nir Shlezinger;Ruud J. G. van Sloun;Rik Vullings","doi":"10.1109/TSP.2024.3443875","DOIUrl":"10.1109/TSP.2024.3443875","url":null,"abstract":"Electrocardiography (ECG) signals play a pivotal role in many healthcare applications, especially in at-home monitoring of vital signs. Wearable technologies, which these applications often depend upon, frequently produce low-quality ECG signals. While several methods exist for ECG denoising to enhance signal quality and aid clinical interpretation, they often underperform with ECG data from wearable technology due to limited noise tolerance or inadequate flexibility in capturing ECG dynamics. This paper introduces HKF, a hierarchical and adaptive Kalman filter, which uses a proprietary state space model to effectively capture both intra- and inter-heartbeat dynamics for ECG signal denoising. HKF learns a patient-specific structured prior for the ECG signal's intra-heartbeat dynamics in an online manner, resulting in a filter that adapts to the specific ECG signal characteristics of each patient. In an empirical study, HKF demonstrated superior denoising performance (reduced Mean-Squared Error) while preserving the unique properties of the waveform. In a comparative analysis, HKF outperformed previously proposed methods for ECG denoising, such as the model-based Kalman filter and data-driven autoencoders. This makes it a suitable candidate for applications in extramural healthcare settings.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"3990-4006"},"PeriodicalIF":4.6,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141986281","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 Matrix-Factorization-Error-Ratio Approach to Cooperative Sensing in Non-Ideal Communication Environment 非理想通信环境中的矩阵因子化-误差率合作传感方法

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-08-13 DOI: 10.1109/TSP.2024.3443291

Rui Zhou;Wenqiang Pu;Licheng Zhao;Ming-Yi You;Qingjiang Shi;Sergios Theodoridis

{"title":"A Matrix-Factorization-Error-Ratio Approach to Cooperative Sensing in Non-Ideal Communication Environment","authors":"Rui Zhou;Wenqiang Pu;Licheng Zhao;Ming-Yi You;Qingjiang Shi;Sergios Theodoridis","doi":"10.1109/TSP.2024.3443291","DOIUrl":"10.1109/TSP.2024.3443291","url":null,"abstract":"A fundamental challenge in cognitive radio is the detection of primary users in a licensed spectrum. Cooperative sensing, which utilizes multiple receivers distributed across different locations, offers the advantage of utilizing multiple antennas and achieving spatial diversity gain. However, successful implementation of cooperative sensing relies on the ideal exchange of information among cooperating receivers, which may not always be feasible in real-world scenarios. In this paper, we consider the cooperative sensing problem in a non-ideal communication scenario, where the raw data broadcasted from a receiving node can be received by only a subset of the nearby nodes. Existing multiantenna detectors can not deal with such a scenario. To tackle this issue, we propose a novel cooperative sensing scheme, where each node sends only its local correlation coefficients to the fusion center. A detection mechanism based on factorizing the partially received sample covariance matrix is developed. To achieve fast convergence and avoid exhaustive step size tuning, a Bregman proximal method, based on an alternating minimization algorithm (with convergence guarantees), is also developed. The advantages of our proposed cooperative scheme is demonstrated through numerical simulations.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"3851-3864"},"PeriodicalIF":4.6,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141980686","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 Jamming Strategy From Limited Observations: An Imitation Learning Perspective 从有限观测中感知干扰策略：模仿学习的视角

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-08-13 DOI: 10.1109/TSP.2024.3443121

Youlin Fan;Bo Jiu;Wenqiang Pu;Ziniu Li;Kang Li;Hongwei Liu

引用次数: 0

An Adversarially Robust Formulation of Linear Regression With Missing Data 有缺失数据的线性回归的逆向鲁棒公式

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-08-13 DOI: 10.1109/TSP.2024.3442712

Alireza Aghasi;Saeed Ghadimi;Yue Xing;Mohammadjavad Feizollahi

引用次数: 0

Broad Beam Designs for Broadcast Channels 广播频道的宽波束设计

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-08-13 DOI: 10.1109/TSP.2024.3440353

Cheng Du;Yi Jiang

引用次数: 0

Bayesian Deep Learning via Expectation Maximization and Turbo Deep Approximate Message Passing 通过期望最大化和 Turbo 深度近似信息传递进行贝叶斯深度学习

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-08-13 DOI: 10.1109/TSP.2024.3442858

Wei Xu;An Liu;Yiting Zhang;Vincent Lau

{"title":"Bayesian Deep Learning via Expectation Maximization and Turbo Deep Approximate Message Passing","authors":"Wei Xu;An Liu;Yiting Zhang;Vincent Lau","doi":"10.1109/TSP.2024.3442858","DOIUrl":"10.1109/TSP.2024.3442858","url":null,"abstract":"Efficient learning and model compression algorithm for deep neural network (DNN) is a key workhorse behind the rise of deep learning (DL). In this work, we propose a message passing-based Bayesian deep learning algorithm called EM-TDAMP to avoid the drawbacks of traditional stochastic gradient descent (SGD)-based learning algorithms and regularization-based model compression methods. Specifically, we formulate the problem of DNN learning and compression as a sparse Bayesian inference problem, in which group sparse prior is employed to achieve structured model compression. Then, we propose an expectation maximization (EM) framework to estimate posterior distributions for parameters (E-step) and update hyperparameters (M-step), where the E-step is realized by a newly proposed turbo deep approximate message passing (TDAMP) algorithm. We further extend the EM-TDAMP and propose a novel Bayesian federated learning framework, in which the clients perform TDAMP to efficiently calculate the local posterior distributions based on the local data, and the central server first aggregates the local posterior distributions to update the global posterior distributions and then update hyperparameters based on EM to accelerate convergence. We detail the application of EM-TDAMP to Boston housing price prediction and handwriting recognition, and present extensive numerical results to demonstrate the advantages of EM-TDAMP.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"3865-3878"},"PeriodicalIF":4.6,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141980685","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