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

Spatially Scalable Recursive Estimation of Gaussian Process Terrain Maps Using Local Basis Functions 基于局部基函数的高斯过程地形图空间可伸缩递归估计

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-11 DOI: 10.1109/TSP.2025.3549966

Frida Viset;Rudy Helmons;Manon Kok

{"title":"Spatially Scalable Recursive Estimation of Gaussian Process Terrain Maps Using Local Basis Functions","authors":"Frida Viset;Rudy Helmons;Manon Kok","doi":"10.1109/TSP.2025.3549966","DOIUrl":"10.1109/TSP.2025.3549966","url":null,"abstract":"We address the computational challenges of large-scale geospatial mapping with Gaussian process (GP) regression by performing localized computations rather than processing the entire map simultaneously. Traditional approaches to GP regression often involve computational and storage costs that either scale with the number of measurements, or with the spatial extent of the mapped area, limiting their scalability for real-time applications. Our method places a global grid of finite-support basis functions and restricts computations to a local subset of the grid 1) surrounding the measurement when the map is updated, and 2) surrounding the query point when the map is queried. This localized approach ensures that only the relevant area is updated or queried at each timestep, significantly reducing computational complexity while maintaining accuracy. Unlike many existing methods, which suffer from boundary effects or increased computational costs with mapped area, our localized approach avoids discontinuities and ensures that computational costs remain manageable regardless of map size. This approximation to GP mapping provides high accuracy with limited computational budget for the specialized task of performing fast online map updates and fast online queries of large-scale geospatial maps. It is therefore a suitable approximation for use in real-time applications where such properties are desirable, such as real-time simultaneous localization and mapping (SLAM) in large, nonlinear geospatial fields. We show on experimental data with magnetic field measurements that our algorithm is faster and equally accurate compared to existing methods, both for recursive magnetic field mapping and for magnetic field SLAM.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1444-1453"},"PeriodicalIF":4.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143599471","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

Reduced-Complexity CRB Optimization for Dual-Function Radar-Communication Systems Using Hybrid Linear-Nonlinear Precoding 基于线性-非线性混合预编码的双功能雷达通信系统低复杂度CRB优化

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-08 DOI: 10.1109/TSP.2025.3567269

Cai Wen;Yating Chen;Yan Huang;Timothy N. Davidson

{"title":"Reduced-Complexity CRB Optimization for Dual-Function Radar-Communication Systems Using Hybrid Linear-Nonlinear Precoding","authors":"Cai Wen;Yating Chen;Yan Huang;Timothy N. Davidson","doi":"10.1109/TSP.2025.3567269","DOIUrl":"10.1109/TSP.2025.3567269","url":null,"abstract":"This paper proposes a waveform design technique for dual-function radar-communication (DFRC) systems that employ hybrid linear-nonlinear precoding (HLNP). The HLNP signal is a superposition of linear precoding of the communication symbols that enables conventional coherent decoding and a nonlinearly precoded auxiliary signal that introduces additional degrees of design freedom that can be used to improve system performance. Our design goal is to obtain accurate direction of arrival (DOA) estimation and satisfactory waveform ambiguity properties, and hence we optimize a weighted sum of a Cramer-Rao bound (CRB) on DOA estimation and a waveform similarity metric. To simultaneously enable effective communication at the chosen data rates, we set lower bounds on the communication SINRs, and to facilitate implementation, we constrain the total transmission power, the per-antenna power, and the peak-to-average power ratio (PAPR) on each antenna. We deploy successive convex approximation to solve the resultant nonconvex design problem, while leveraging feasible point pursuit to provide a feasible initial point. To reduce the computational cost, we introduce sub-block and null space variants of our design technique. Simulation results verify the effectiveness of the proposed algorithm and its variants, and validate their performance advantages over regular nonlinear precoding schemes.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2123-2138"},"PeriodicalIF":4.6,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143926698","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

Decentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking 紧子流形的量化黎曼梯度跟踪分散优化

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-08 DOI: 10.1109/TSP.2025.3568045

Jun Chen;Lina Liu;Tianyi Zhu;Yong Liu;Guang Dai;Yunliang Jiang;Ivor W. Tsang

{"title":"Decentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking","authors":"Jun Chen;Lina Liu;Tianyi Zhu;Yong Liu;Guang Dai;Yunliang Jiang;Ivor W. Tsang","doi":"10.1109/TSP.2025.3568045","DOIUrl":"10.1109/TSP.2025.3568045","url":null,"abstract":"This paper considers the problem of decentralized optimization on compact submanifolds, where a finite sum of smooth (possibly non-convex) local functions is minimized by <inline-formula><tex-math>$n$</tex-math></inline-formula> agents forming an undirected and connected graph. However, the efficiency of distributed optimization is often hindered by communication bottlenecks. To mitigate this, we propose the Quantized Riemannian Gradient Tracking (Q-RGT) algorithm, where agents update their local variables using quantized gradients. The introduction of quantization noise allows our algorithm to bypass the constraints of the accurate Riemannian projection operator (such as retraction), further improving iterative efficiency. To the best of our knowledge, this is the first algorithm to achieve an <inline-formula><tex-math>$mathcal{O}(1/K)$</tex-math></inline-formula> convergence rate in the presence of quantization, matching the convergence rate of methods without quantization. Additionally, we explicitly derive lower bounds on decentralized consensus associated with a function of quantization levels. Numerical experiments demonstrate that Q-RGT performs comparably to non-quantized methods while reducing communication bottlenecks and computational overhead.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1851-1861"},"PeriodicalIF":4.6,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143926699","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 Monotonic Neural Networks for Performance Prediction and Threshold Determination in Multichannel Detection 利用单调神经网络进行多通道检测的性能预测和阈值确定

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-08 DOI: 10.1109/TSP.2025.3567761

Rui Zhou;Wenqiang Pu;Ming-Yi You;Qingjiang Shi

引用次数: 0

Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraints 可靠性和通信约束下传感器网络的共形分布式远程推理

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-07 DOI: 10.1109/TSP.2025.3549222

Meiyi Zhu;Matteo Zecchin;Sangwoo Park;Caili Guo;Chunyan Feng;Petar Popovski;Osvaldo Simeone

{"title":"Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraints","authors":"Meiyi Zhu;Matteo Zecchin;Sangwoo Park;Caili Guo;Chunyan Feng;Petar Popovski;Osvaldo Simeone","doi":"10.1109/TSP.2025.3549222","DOIUrl":"10.1109/TSP.2025.3549222","url":null,"abstract":"This paper presents communication-constrained distributed conformal risk control (CD-CRC) framework, a novel decision-making framework for sensor networks under communication constraints. Targeting multi-label classification problems, such as segmentation, CD-CRC dynamically adjusts local and global thresholds used to identify significant labels with the goal of ensuring a target false negative rate (FNR), while adhering to communication capacity limits. CD-CRC builds on online exponentiated gradient descent to estimate the relative quality of the observations of different sensors, and on online conformal risk control (CRC) as a mechanism to control local and global thresholds. CD-CRC is proved to offer deterministic worst-case performance guarantees in terms of FNR and communication overhead, while the regret performance in terms of false positive rate (FPR) is characterized as a function of the key hyperparameters. Simulation results highlight the effectiveness of CD-CRC, particularly in communication resource-constrained environments, making it a valuable tool for enhancing the performance and reliability of distributed sensor networks.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1485-1500"},"PeriodicalIF":4.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575097","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

Vertex-Frequency Analysis on Directed Graphs 有向图的顶点频率分析

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-07 DOI: 10.1109/TSP.2025.3567831

Deyun Wei;Shuangxiao Yuan

{"title":"Vertex-Frequency Analysis on Directed Graphs","authors":"Deyun Wei;Shuangxiao Yuan","doi":"10.1109/TSP.2025.3567831","DOIUrl":"10.1109/TSP.2025.3567831","url":null,"abstract":"Vertex-frequency analysis (VFA) is a useful technique in graph signal processing to extract the correspondence between frequencies and vertices. VFA can be calculated by the windowed graph Fourier transform (WGFT) and the localized graph Fourier transform (LGFT). However, since the inability of the classical graph Fourier transform (GFT) to generate orthogonal bases for directed graphs, the two approaches are confined to undirected graphs. In order to address the absence of VFA on directed graphs, we extend the fundamental concepts of VFA, shifted vertex window function and band-pass transfer filters in spectral domain to directed graphs. We first propose WDGFT based on the shifted window function in vertex domain. The relevant properties, reconstruction formulas, and simulation results of vertex-frequency representation are given. Then, we propose the localized directed graph Fourier transform (LDGFT), where the band-pass transfer filters can capture the localized spectral domain. The transfer functions satisfying the reconstruction condition are discussed. The vertex-frequency representation obtained by LDGFT is provided through numerical examples. Furthermore, we present a polynomial approximation technique to decrease computational cost. The LDGFT can be calculated without any matrix decomposition. Finally, we evaluate the proposed directed VFA framework with two kinds of applications, clustering and malfunction detection. We demonstrate that the proposed VFA framework is a powerful tool for directed graph signal processing.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"2255-2270"},"PeriodicalIF":4.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143920031","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

Batch SLAM With PMBM Data Association Sampling and Graph-Based Optimization 批量SLAM与PMBM数据关联采样和基于图的优化

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-07 DOI: 10.1109/TSP.2025.3567916

Yu Ge;Ossi Kaltiokallio;Yuxuan Xia;Ángel F. García-Fernández;Hyowon Kim;Jukka Talvitie;Mikko Valkama;Henk Wymeersch;Lennart Svensson

引用次数: 0

Quaternion Information Filters With Inaccurate Measurement Noise Covariance: A Variational Bayesian Method 测量噪声协方差不准确的四元数信息滤波：一种变分贝叶斯方法

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-07 DOI: 10.1109/TSP.2025.3549023

Dongyuan Lin;Xiaofeng Chen;Yunfei Zheng;Zhongyuan Guo;Qiangqiang Zhang;Shiyuan Wang

{"title":"Quaternion Information Filters With Inaccurate Measurement Noise Covariance: A Variational Bayesian Method","authors":"Dongyuan Lin;Xiaofeng Chen;Yunfei Zheng;Zhongyuan Guo;Qiangqiang Zhang;Shiyuan Wang","doi":"10.1109/TSP.2025.3549023","DOIUrl":"10.1109/TSP.2025.3549023","url":null,"abstract":"Quaternion Kalman filters (QKFs) are designed for state estimation in three-dimensional (3-D) space. To simplify initialization, this paper focuses on the quaternion information filter (QIF), which converts the information vector and matrix into quaternion form. While QIF demonstrates strong performance under the assumption of known quaternion measurement noise statistics, this assumption frequently does not hold in practical scenarios. To address this issue, a variational Bayesian adaptive QIF (VBAQIF) is proposed by modeling the inverse of the covariance matrix for the quaternion measurement noise as the quaternion Wishart distribution in this paper. First, the adaptive QIF is derived under the recursive Bayesian estimation framework to propagate the quaternoin information vector and information matrix. Then, the quaternion measurement noise covariance matrix together with the quaternion state is inferred using the variational Bayesian approach. Furthermore, a corresponding square root version, called variational Bayesian adaptive square-root QIF (VBASQIF), is developed to enhance numerical stability of VBAQIF, and this stability is analyzed from a theoretical perspective. Finally, a 3-D target tracking example is simulated to demonstrate that the proposed VBAQIF exhibits excellent performance even in the presence of uncertainties in the quaternion measurement noise covariance matrices.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1367-1378"},"PeriodicalIF":4.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575098","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

Fast Robust Sparse Bayesian Learning Image Reconstruction Model Based on Generalized Approximate Message Passing 基于广义近似消息传递的快速鲁棒稀疏贝叶斯学习图像重建模型

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-05 DOI: 10.1109/TSP.2025.3566404

Wenzhe Jin;Wentao Lyu;Qing Guo;Zhijiang Deng

{"title":"Fast Robust Sparse Bayesian Learning Image Reconstruction Model Based on Generalized Approximate Message Passing","authors":"Wenzhe Jin;Wentao Lyu;Qing Guo;Zhijiang Deng","doi":"10.1109/TSP.2025.3566404","DOIUrl":"10.1109/TSP.2025.3566404","url":null,"abstract":"Sparse Bayesian learning (SBL) is an algorithm for high-dimensional data processing based on Bayesian statistical theory. Its goal is to improve the generalization ability and efficiency of the model by introducing sparsity, that is, retaining only some important features of the image. However, the traditional Sparse Bayesian Learning algorithm involves the operation of n<inline-formula><tex-math>$boldsymbol{times}$</tex-math></inline-formula>n dimensional matrix inversion during iterative update, which seriously affects the efficiency and speed of image reconstruction. In order to overcome the above defects, in this paper, we propose a fast robust Sparse Bayesian Learning image reconstruction model based on generalized approximate message passing (GAMP-FRSBL). The damped Gaussian generalized approximate message passing algorithm (Damped GGAMP) is introduced on the basis of SBL to avoid the matrix inversion problem. Combined with the convex optimization strategy, the block coordinate descent (BCD) method is used to iteratively update the parameters to improve the reconstruction efficiency of the model. Finally, experiments are conducted on Indor and Mondrian images, DOTA, COCO and UCM datasets to verify the effectiveness of the GAMP-FRSBL in image reconstruction.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1839-1850"},"PeriodicalIF":4.6,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143909915","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 Decentralized Primal-Dual Method With Quasi-Newton Tracking 一类具有准牛顿跟踪的分散原对偶方法

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-04 DOI: 10.1109/TSP.2025.3547787

Liping Wang;Hao Wu;Hongchao Zhang

引用次数: 0