IEEE Transactions on Signal Processing最新文献_第2页

Invariance Theory for Radar Detection in Disturbance With Kronecker Product Covariance Structure — Part II: Compound Gaussian Environment

IF 5.4 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

引用次数: 0

Detection and Multiparameter Estimation for NLoS Targets: An RIS-Assisted Framework NLoS 目标的探测和多参数估计：RIS 辅助框架

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-17 DOI: 10.1109/TSP.2025.3546991

Zhouyuan Yu;Xiaoling Hu;Chenxi Liu;Qin Tao;Mugen Peng

{"title":"Detection and Multiparameter Estimation for NLoS Targets: An RIS-Assisted Framework","authors":"Zhouyuan Yu;Xiaoling Hu;Chenxi Liu;Qin Tao;Mugen Peng","doi":"10.1109/TSP.2025.3546991","DOIUrl":"10.1109/TSP.2025.3546991","url":null,"abstract":"Reconfigurable intelligent surface (RIS) has the potential to enhance sensing performance, due to its capability of reshaping the echo signals. Different from the existing literature, which has commonly focused on RIS beamforming optimization, in this paper, we pay special attention to designing effective signal processing approaches to extract sensing information from RIS-reshaped echo signals. To this end, we investigate an RIS-assisted non-line-of-sight (NLoS) target detection and multi-parameter estimation problem in orthogonal frequency division multiplexing (OFDM) systems. To address this problem, we first propose a novel detection and direction estimation framework, including a low-overhead hierarchical codebook that allows the RIS to generate three-dimensional beams with adjustable beam direction and width, a delay spectrum peak-based beam training scheme for detection and direction estimation, and a beam refinement scheme for further enhancing the accuracy of the direction estimation. Then, we propose a target range and velocity estimation scheme by extracting the delay-Doppler information from the RIS-reshaped echo signals. Numerical results demonstrate that the proposed schemes can achieve a <inline-formula><tex-math>$99.7%$</tex-math></inline-formula> target detection rate, a <inline-formula><tex-math>$10^{-3}$</tex-math></inline-formula>-rad level direction estimation accuracy, and a <inline-formula><tex-math>$10^{-6}$</tex-math></inline-formula>-m/<inline-formula><tex-math>$10^{-5}$</tex-math></inline-formula>-m/s level range/velocity estimation accuracy.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1470-1484"},"PeriodicalIF":4.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143640563","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

Optimal TOA-based Sensor-Anchor-Source Geometries and Estimation Bounds for Simultaneous Sensor and Source Localization

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-14 DOI: 10.1109/tsp.2025.3545928

Sheng Xu, Bing Zhu, Xinyu Wu, Kutluyıl Doğançay

引用次数: 0

Greedy Selection for Heterogeneous Sensors 异构传感器的贪婪选择

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-12 DOI: 10.1109/TSP.2025.3549301

Kaushani Majumder;Sibi Raj B. Pillai;Satish Mulleti

{"title":"Greedy Selection for Heterogeneous Sensors","authors":"Kaushani Majumder;Sibi Raj B. Pillai;Satish Mulleti","doi":"10.1109/TSP.2025.3549301","DOIUrl":"10.1109/TSP.2025.3549301","url":null,"abstract":"Simultaneous operation of all sensors in a large-scale sensor network is power-consuming and computationally expensive. Hence, it is desirable to select fewer sensors. A greedy algorithm is widely used for sensor selection in homogeneous networks with a theoretical worst-case performance of <inline-formula><tex-math>$boldsymbol{(mathbf{1-1}/mathbf{e})mathbf{approx 63}}$</tex-math></inline-formula>% of the optimal performance when optimizing submodular metrics. For heterogeneous sensor networks (HSNs) comprising multiple sets of sensors, most of the existing sensor selection methods optimize the performance constrained by a budget on the total value of the selected sensors. However, in many applications, the number of sensors to select from each set is known apriori and solutions are not well-explored. For this problem, we propose a joint greedy heterogeneous sensor selection algorithm. Theoretically, we show that the worst-case performance of the proposed algorithm is bounded to <inline-formula><tex-math>$50$</tex-math></inline-formula>% of the optimum for submodular cost metrics. In the special case of HSNs with two sensor networks, the performance guarantee can be improved to <inline-formula><tex-math>$63$</tex-math></inline-formula>% when the number of sensors to select from one set is much smaller than the other. To validate our results experimentally, we propose a submodular metric based on the frame potential measure that considers both the correlation among the sensor measurements and their heterogeneity. We prove theoretical bounds for the mean squared error of the solution when this performance metric is used. We validate our results through simulation experiments considering both linear and non-linear measurement models corrupted by additive noise and quantization errors. Our experiments show that the proposed algorithm results in <inline-formula><tex-math>$4 {boldsymbol{mathbf{-}}} 10$</tex-math></inline-formula> dB lower error than existing methods.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1394-1409"},"PeriodicalIF":4.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143608003","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

Arithmetic Average Density Fusion - Part IV: Distributed Heterogeneous Fusion of RFS and LRFS Filters via Variational Approximation

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-11 DOI: 10.1109/tsp.2025.3550157

Tiancheng Li, Haozhe Liang, Guchong Li, Jesús García Herrero, Quan Pan

引用次数: 0

Optimal Bilinear Equalizer for Cell-Free Massive MIMO Systems over Correlated Rician Channels

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-11 DOI: 10.1109/tsp.2025.3547380

Zhe Wang, Jiayi Zhang, Emil Björnson, Dusit Niyato, Bo Ai

引用次数: 0

Online Federated Reproduced Gradient Descent With Time-Varying Global Optima

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2025-03-11 DOI: 10.1109/TSP.2025.3549591

Yifu Lin;Wenling Li;Jia Song;Xiaoming Li

{"title":"Online Federated Reproduced Gradient Descent With Time-Varying Global Optima","authors":"Yifu Lin;Wenling Li;Jia Song;Xiaoming Li","doi":"10.1109/TSP.2025.3549591","DOIUrl":"10.1109/TSP.2025.3549591","url":null,"abstract":"This paper addresses an online federated learning problem, where the time drift in data distribution leads to time-varying global optima. To adapt to the drift, this paper designs a random Fourier features (RFF) model combined with Reproducing Kernel Hilbert Space (RKHS) theory to tracking the global gradient. Meanwhile, the model also can mitigate gradient variance from local data and gradient bias due to data heterogeneity. Based on this model, the paper further proposes an online federated reproduced gradient descent (OFedRGD) algorithm. The Wasserstein distance is then employed as a distribution metric to analyze the regret by OFedRGD, which is composed of cumulative distribution drifts and cumulative gradient error caused by stochasticity and heterogeneity. Additionally, a set of CLEAR-datasets, including two online learning tasks, are used to test the proposed algorithm. The results show that the proposed algorithm can effectively improve classification accuracy in the two tasks by <inline-formula><tex-math>$5%$</tex-math></inline-formula> and <inline-formula><tex-math>$16%$</tex-math></inline-formula>, respectively, and its performance is less adversely affected by the degree of data dispersion.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"73 ","pages":"1379-1393"},"PeriodicalIF":4.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143599456","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

Spatially Scalable Recursive Estimation of Gaussian Process Terrain Maps Using Local Basis Functions

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

Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraints

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

Quaternion Information Filters With Inaccurate Measurement Noise Covariance: A Variational Bayesian Method

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