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检索条件"任意字段=2008 IEEE Sensor Array and Multichannel Signal Processing Workshop"

共 1267 条记录，以下是71-80 订阅

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Calibration of Polarimetric Antenna arrays Using Neural Networks

Calibration of Polarimetric Antenna Arrays Using Neural Netw...

引用

sensor array and multichannel signal processing workshop

作者： Jannik Springer Philipp Mikus Marc Oispuu Wolfgang Koch Peter Knott Fraunhofer Institute for Communication Information Processing and Ergonomics FKIE Wachtberg Germany Fraunhofer Institute for High Frequency Physics and Radar Techniques FHR Wachtberg Germany

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

This paper addresses the problem of calibration for antenna arrays with multi-port polarimetric elements. Model-based array signal processing techniques require an accurate model of the complex array response and model errors can, for example, cause significant systematic direction finding errors. Modeling the response of polarimetric antenna arrays can be particularly challenging due to cross-polarization and mutual coupling effects in the multi-port antenna elements. This work proposes a new calibration technique for polarimetric antenna arrays using neural networks that learn any mismatches between the modeled and the actual array response. The technique is evaluated based on the measured response of a five-element dual-polarized antenna array and outperforms conventional calibration techniques like mutual coupling calibration or local polynomial approximation. Its performance is studied exemplarily for the direction finding problem.

关键词： Antenna measurements Training Direction-of-arrival estimation Systematics array signal processing Artificial neural networks Parallel processing

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Distributed UAV Beamforming Using Graph Recurrent Neural Networks

Distributed UAV Beamforming Using Graph Recurrent Neural Net...

引用

sensor array and multichannel signal processing workshop

作者： Wenqing Zheng Brian M. Sadler Fernando Gama Tianlong Chen Univ. of Texas at Austin Austin TX USA Univ. of Pennsylvania Philadelphia PA USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

In this paper we develop a novel learning-based approach for mobile distributed beamforming without channel state information. We consider narrowband beamforming between a mobile UAV group and a base station under limited feedback, and propose a graph recurrent neural network (GRNN) approach to leverage local collaboration among the UAVs. The GRNN method is shown to be robust to variations in UAV speeds and group heading, and scales with the UAV group size. We compare to codebook and binary feedback methods and show that better performance is achieved with the proposed GRNN method.

关键词： Training Base stations Tracking loops Recurrent neural networks array signal processing Autonomous aerial vehicles Picture archiving and communication systems

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ADMM for $\ell_{0}$ Factor Analysis

ADMM for $\ell_{0}$ Factor Analysis

引用

sensor array and multichannel signal processing workshop

作者： Linyang Wang Wanquan Liu Bin Zhu School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Factor analysis is widely utilized for data dimensionality reduction. Its objective is to extract a low-rank plus sparse factor structure from the covariance matrix of the observed data. In this paper, we propose a nonconvex and nonsmooth optimization model to obtain the additive matrix decomposition based on the nuclear norm of the low-rank component, the $\ell_{0}$ norm of the sparse component, and the Kullback-Leibler divergence between the candidate covariance matrix and the sample covariance matrix. We present an ADMM algorithm to solve the optimization problem and demonstrate its effectiveness through experiments on both synthetic and real datasets.

关键词： Dimensionality reduction Optimization models Noise signal processing algorithms Estimation Numerical models Matrix decomposition

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Multi-Tier Structured array for Sensing Pilot Design in Integrated Sensing and Communication

Multi-Tier Structured Array for Sensing Pilot Design in Inte...

引用

sensor array and multichannel signal processing workshop

作者： Jiaying Ren Shawn Tsai Advanced Communication Technology Communication System Design Mediatek USA Inc San Diego CA USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

In this paper, we explore the use of large-scale sparse arrays for pilot placement in pilot-based sensing within integrated sensing and communication (ISAC) systems. Unlike conventional regular pilot placement method, sparse placement offers a significant reduction in overhead while maintaining high sensing performance. We present a novel large-scale sparse array construction method by introducing a multi-tier array structure. Using the proposed method, the design of large-scale sparse arrays can be simplified into several smaller-sized array design problems, significantly reducing computational complexity and storage requirements. Numerical examples demonstrate the effectiveness of the proposed method for low-overhead sensing pilot placement design, which is applicable in future 6G applications.

关键词： 6G mobile communication array signal processing Design methodology Conferences Integrated sensing and communication Frequency division multiplexing Computational complexity

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Monte Carlo Source Enumeration for Sparse arrays

Monte Carlo Source Enumeration for Sparse Arrays

引用

sensor array and multichannel signal processing workshop

作者： Chun-Lin Liu Dept. of Electrical Engineering Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan

Linear sparse arrays with $N$ physical sensors can resolve $\mathcal{O}(N^array)$ directions of arrival (DOAs) for uncorrelated sources. This attribute is associated with the difference coarray of size $\mathcal{O}... 详细信息

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Linear sparse arrays with $N$ physical sensors can resolve $\mathcal{O}(N^sensor)$ directions of arrival (DOAs) for uncorrelated sources. This attribute is associated with the difference coarray of size $\mathcal{O}(N^sensor)$ , However, the number of sources is assumed to be known in many coarray-based DOA estimators. This paper proposes a Monte Carlo source enumerator (MCSE) for sparse arrays by maximizing the log-likelihood function (LLF). This LLF depends on an unbounded parameter space and a multiple integral, which are challenging for computation. This unbounded parameter space is replaced with a bounded space derived from coarse estimates and Cramér-Rao bounds. Next, the multiple integral is approximated with Monte Carlo methods. The MCSE applies to three scenarios: no sources, fewer sources than sensors, and more sources than sensors. Furthermore, some details in the MCSE can be computed in parallel. Numerical examples demonstrate the applicability of the MCSE to sparse arrays.

关键词： Monte Carlo methods Direction-of-arrival estimation array signal processing Conferences sensor phenomena and characterization sensor arrays

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A Preconditioned Fast Iterative Hard Thresholding Algorithm for Spectrally Sparse signal Reconstruction

A Preconditioned Fast Iterative Hard Thresholding Algorithm ...

引用

sensor array and multichannel signal processing workshop

作者： Fengmiao Bian Jian-Feng Cai Xueyang Quan Yang Wang Department of Mathematics Hong Kong University of Science and Technology Hong Kong China

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Spectrally sparse signals arise from various applications, including seismic imaging, autoregression, and magnetic resonance spectroscopy. Reconstructing a spectrally sparse signal from sub-samples on the uniform grid in the time domain can be reformulated as a low-rank Hankel matrix completion problem. Although the fast iterative hard thresholding (FIHT) algorithm is an efficient non-convex solver for this problem, it relies on the canonical metric in the matrix space. To speed up FIHT, we introduce a novel algorithm, preconditioned fast iterative hard thresholding (PFIHT), which adopts a data-driven metric by employing preconditioning techniques. Numerical experiments demonstrate that PFIHT outperforms FIHT and projected gradient descent (PGD) in terms of the number of iterations required for convergence and overall computational time.

关键词： Spectroscopy Thresholding (Imaging) signal processing algorithms Magnetic resonance Iterative algorithms signal reconstruction Sparse matrices

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ADMM-Based Outage Constrained MIMO-ISAC Hybrid Beamforming Design

ADMM-Based Outage Constrained MIMO-ISAC Hybrid Beamforming D...

引用

sensor array and multichannel signal processing workshop

作者： Hao Liang Bin Liao College of Electronics and Information Engineering Shenzhen University Shenzhen China State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen University Shenzhen China

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

This paper considers the problem of robust hybrid beamforming design for multiple-input multiple-output (MIMO) integrated sensing and communications (ISAC) systems in the presence of channel state information (CSI) error. Specifically, the analog and digital hybrid beamformer at the transmitter is designed with imperfect CSI, through synthesizing a desired transmit power pattern for radar sensing under the outage probability constraints on the signal-to-interference-plus-noise ratio (SINR) of each user. The formulated nonconvex problem is addressed by taking advantage of the Cantelli's Inequality and the Alternating Direction Method of Multipliers (ADMM) frame- work, and deriving the solution of each subproblem involved in the algorithm. Numerical experiments are carried out to show the effectiveness and robustness of the proposed MIMO-ISAC hybrid beamforming design.

关键词： array signal processing Simulation signal processing algorithms Interference Probability Integrated sensing and communication Approximation algorithms

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Near-Field ISAC: Performance Analysis and Rate Region Characterization

Near-Field ISAC: Performance Analysis and Rate Region Charac...

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sensor array and multichannel signal processing workshop

作者： Boqun Zhao Chongjun Ouyang Jiaqi Xu Xingqi Zhang Yuanwei Liu University of Alberta Edmonton Canada University College Dublin Dublin Ireland Queen Mary University of London London UK University of California Irvine USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

A near-field integrated sensing and communications (ISAC) framework is proposed with an accurate channel model, where the effective aperture loss is considered. Based on the proposed framework, sensing and communication performance are analyzed in terms of three different beamforming designs: a communications-centric design, a sensing-centric design, and a Pareto optimal design. Within each design, the closed-form expressions of sensing rates (SRs) and communication rates (CRs) are derived. Moreover, the attainable SR-CR regions of the near-field ISAC are characterized. Numerical results reveal that the performance of the proposed near-field ISAC framework converges to upper bounds as the number of BS antennas increases.

关键词： Accuracy Upper bound Closed-form solutions array signal processing Conferences Pareto optimization Integrated sensing and communication

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A Federated Learning Approach for Graph Convolutional Neural Networks

A Federated Learning Approach for Graph Convolutional Neural...

引用

sensor array and multichannel signal processing workshop

作者： Andrew Campbell Hang Liu Anna Scaglione Tong Wu Department of Electrical and Computer Engineering Cornell Tech Cornell University NY USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

In this paper we cast the problem of training a graph neural network based on labeled graph data in a “federated learning” scenario where different agents have access to data from a subset of the network nodes. The learning problem is not decomposable, therefore it does not lend itself to a straightforward mapping onto a distributed multi-agent protocol. We propose a multi-agent federated learning scheme which leverages the local and sparse structure of graph filters to limit the information sharing while emulating the performance of centralized training. Even though we preserve data locality and agent communication is restricted to the neighborhood level, the proposed method still converges in simulation.

关键词： Training Federated learning Computational modeling Distributed databases signal processing algorithms Data models Numerical models

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On Detecting Low-Pass Graph signals Under Partial Observations

On Detecting Low-Pass Graph Signals Under Partial Observatio...

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sensor array and multichannel signal processing workshop

作者： Hoang-Son Nguyen Hoi-To Wai Dept. of Computer Science & Engineering The Chinese University of Hong Kong Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

The application of graph signal processing (GSP) on partially observed graph signals with missing nodes has gained attention recently. This is because processing data from large graphs are difficult, if not impossible due to the lack of availability of full observations. Many prior works have been developed using the assumption that the generated graph signals are smooth or low pass filtered. This paper treats a blind graph filter detection problem under this context. We propose a detector that certifies whether the partially observed graph signals are low pass filtered, without requiring the graph topology knowledge. As an example application, our detector leads to a pre-screening method to filter out non low pass signals and thus robustify the prior GSP algorithms. We also bound the sample complexity of our detector in terms of the class of filters, number of observed nodes, etc. Numerical experiments verify the efficacy of our method.

关键词： array signal processing Conferences Low-pass filters signal processing algorithms Detectors Filtering algorithms Complexity theory

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