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

Transmit Energy Focusing for Parameter Estimation in Slow-Time Transmit Beamspace L-Shaped MIMO Radar 慢时发射波束空间 L 型多输入多输出雷达参数估计的发射能量聚焦

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-11-06 DOI: 10.1109/TSP.2024.3492692

Tingting Zhang;Sergiy A. Vorobyov;Feng Xu

{"title":"Transmit Energy Focusing for Parameter Estimation in Slow-Time Transmit Beamspace L-Shaped MIMO Radar","authors":"Tingting Zhang;Sergiy A. Vorobyov;Feng Xu","doi":"10.1109/TSP.2024.3492692","DOIUrl":"10.1109/TSP.2024.3492692","url":null,"abstract":"We present a novel slow-time transmit beamspace (TB) multiple-input multiple-output (MIMO) technique for L-shaped array radar with uniform linear subarrays to estimate target parameters including 2-dimensional (2-D) directions of arrival (DOA) and unambiguous velocity. Doppler division multiple access (DDMA) approach, as a type of slow-time waveform achieving waveform orthogonality across multiple pulses within a coherent processing interval, disperses the transmit energy over the entire spatial region, suffering from beam-shape loss. Moreover, Doppler spectrum division, which is necessary for transmit channel separation prior to parameter estimation, leads to the loss of crucial information for velocity disambiguation. To optimize transmit energy distribution, slow-time TB technique is proposed to focus the energy within a desired spatial region. Unlike DDMA approach, slow-time TB technique divides the entire Doppler spectrum into more subbands than the number of transmit antenna elements to narrow down the beam mainlobe intervals between adjacent beams formed by DDMA modulation vectors. As a result, more beams are incorporated into the region of interest, and slow-time TB radar can direct transmit energy to the region of interest by properly selecting the DDMA modulation vectors whose beams are directed there. To resolve velocity ambiguity, tensor signal modeling, by storing measurements in a tensor without Doppler spectrum division, is used. Parameter estimation is then addressed using canonical polyadic decomposition (CPD), and the performance of slow-time TB L-shaped MIMO radar is shown to be improved as compared to DDMA MIMO techniques. Simulations are conducted to validate the proposed method.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5228-5243"},"PeriodicalIF":4.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10746379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142594833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Polarization Diversity Detection and Localization of a Target With Energy Spillover 利用能量溢出对目标进行偏振分集探测和定位

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-11-06 DOI: 10.1109/tsp.2024.3490844

Naixin Kang, Weijian Liu, Jun Liu, Chengpeng Hao, Xiaotao Huang, Zheran Shang

引用次数: 0

Input Distribution Optimization in OFDMDual-Function Radar-Communication Systems OFDMD 双功能雷达通信系统中的输入分配优化

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-11-05 DOI: 10.1109/tsp.2024.3491899

Yumeng Zhang, Sundar Aditya, Bruno Clerckx

引用次数: 0

Compute-Update Federated Learning: A Lattice Coding Approach 计算-更新联合学习：网格编码方法

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-11-05 DOI: 10.1109/TSP.2024.3491993

Seyed Mohammad Azimi-Abarghouyi;Lav R. Varshney

引用次数: 0

Localized Distributional Robustness in Submodular Multi-Task Subset Selection 次模态多任务子集选择中的局部分布稳健性

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-11-05 DOI: 10.1109/tsp.2024.3492165

Ege C. Kaya, Abolfazl Hashemi

引用次数: 0

Observability Guaranteed Distributed Intelligent Sensing for Industrial Cyber-Physical System 工业网络物理系统的可观测性保证分布式智能传感

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-11-04 DOI: 10.1109/TSP.2024.3490838

Zhiduo Ji;Cailian Chen;Xinping Guan

{"title":"Observability Guaranteed Distributed Intelligent Sensing for Industrial Cyber-Physical System","authors":"Zhiduo Ji;Cailian Chen;Xinping Guan","doi":"10.1109/TSP.2024.3490838","DOIUrl":"10.1109/TSP.2024.3490838","url":null,"abstract":"Distributed sensing is a key process for acquiring system state information in the network environments of industrial cyber-physical system (ICPS). Considering the unknown complex industrial system models, the intelligent methods for distributed sensing are received extensive attention. In most existing works, the system observability is assumed strictly first to obtain complete sensing information for subsequent state estimation. But with the expansion of industrial monitoring network scale, the observability requirement is increasingly difficult to be satisfied in advance. Therefore, a new distributed intelligent sensing method with guaranteed observability is proposed for ICPS in this paper. Specifically, a distributed learning mechanism based on field level data is designed to dynamically approximate the distributed sensing process. Then, the learning weight complete update condition is provided to actively guarantee the observability, and the novel convex-set construction approach is proposed to handle the non-convex property of this condition. Besides, the learning convergence speed and error bound are analyzed in detail. Finally, the proposed method is applied into the industrial hot rolling laminar cooling process based on the established simulation system. Compared with state-of-the-art methods in distributed intelligent sensing, the proposed method can actively reduce the sensing cost while improving the sensing performance with guaranteed observability. An average overall improvement of 24.1% in the normalized sensing performance and selection number of sensing terminals is achieved, which provides a solution for the upgrade of intelligent sensing of key processes in similar ICPS.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5198-5212"},"PeriodicalIF":4.6,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580020","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

Robust Stochastically-Descending Unrolled Networks 稳健的随机递减未展开网络

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-11-04 DOI: 10.1109/tsp.2024.3489223

Samar Hadou, Navid NaderiAlizadeh, Alejandro Ribeiro

引用次数: 0

One-Bit Target Detection in Colocated MIMO Radar with Colored Background Noise 带有彩色背景噪声的同地多输入多输出雷达中的一位目标检测

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-11-04 DOI: 10.1109/tsp.2024.3484582

Yu-Hang Xiao, David Ramírez, Lei Huang, Xiao Peng Li, Hing Cheung So

引用次数: 0

Channel Parameter Estimation and Location Sensing in MmWave Systems under Phase Noise via Nested PARAFAC Analysis 通过嵌套 PARAFAC 分析实现相位噪声下毫米波系统中的信道参数估计和位置感应

IF 5.4 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-10-31 DOI: 10.1109/tsp.2024.3488781

Meng Han, Jianhe Du, Yuanzhi Chen, Libiao Jin, Feifei Gao

引用次数: 0

Parametric Minimum Error Entropy Criterion: A Case Study in Blind Sensor Fusion and Regression Problems 参数最小误差熵准则：盲传感器融合与回归问题案例研究

IF 4.6 2区工程技术

IEEE Transactions on Signal Processing Pub Date : 2024-10-30 DOI: 10.1109/TSP.2024.3488554

Carlos Alejandro Lopez;Jaume Riba

{"title":"Parametric Minimum Error Entropy Criterion: A Case Study in Blind Sensor Fusion and Regression Problems","authors":"Carlos Alejandro Lopez;Jaume Riba","doi":"10.1109/TSP.2024.3488554","DOIUrl":"10.1109/TSP.2024.3488554","url":null,"abstract":"The purpose of this article is to present the Parametric Minimum Error Entropy (PMEE) principle and to show a case study of the proposed criterion in a blind sensor fusion and regression problem. This case study consists on the estimation of a temporal series with a certain temporal invariance, which is measured from multiple independent sensors with unknown variances and unknown mutual correlations of the measurement errors. In this setting, we show that a particular case of the PMEE criterion is obtained from the Conditional Maximum Likelihood (CML) principle of the measurement model, leading to a \u0000<italic>semi-data-driven</i>\u0000 solution. Despite the fact that Information Theoretic Criteria (ITC) are inherently robust, they often result in difficult non-convex optimization problems. Our proposal is to address the non-convexity by means of a Majorization-Minimization (MM) based algorithm. We prove the conditions in which the resulting solution of the proposed algorithm reaches a stationary point of the original problem. In fact, the aforementioned global convergence of the proposed algorithm is possible thanks to a reformulation of the original cost function in terms of a variable constrained in the Grassmann manifold. As shown in this paper, the latter procedure is possible thanks to a homogeneity property of the PMEE cost function.","PeriodicalId":13330,"journal":{"name":"IEEE Transactions on Signal Processing","volume":"72 ","pages":"5091-5106"},"PeriodicalIF":4.6,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555958","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