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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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Classification of microwave scattering data based on a subspace distance with application to detection of bleeding stroke

Classification of microwave scattering data based on a subsp...

引用

ieee International workshop on Computational Advances in Multi-sensor Adaptive processing (CAMSAP)

作者： Mohammad Ali Khorshidi Tomas McKelvey Mikael Persson Hana Dobsicek Trefna Department of Signals and Systems Chalmers University of Technology Gothenburg Sweden

ISBN: (纸本)9781424451791;9781424451807

This paper demonstrates the usefulness of a classifier based on a subspace distance for the detection of bleeding stroke based on microwave scattering measurements from an antenna array placed around the skull. This discriminating classifier is suitable for high dimensional data applications when the number of training data samples is less than the data dimension. The proposed classifier was tested on both clinical and experimental data to separate bleeding subjects from non-bleeding ones. A pseudo-inverse Mahalanobis distance based classifier and a classifier based on the Euclidean distance were used on clinical data for the purpose of comparison with the proposed classifier.

关键词： Hemorrhaging Linear discriminant analysis Electromagnetic scattering Covariance matrix signal processing Microwave technology Paper technology Antenna measurements Microwave measurements Microwave antenna arrays

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Optimal Adaptive array and Angle Tracking for Multiple Targets - A Re-examination of Optimal array processing

Optimal Adaptive Array and Angle Tracking for Multiple Targe...

引用

sensor array and multichannel signal processing workshop

作者： Huaijin Gu Louisiana Tech University USA

ISBN: (纸本)1424403081

The optimal array for detecting the signal from a desired direction but contaminated by receiver noise and strong interference from different sources with unknown arriving-angles is re-examined. The well-known optimal array is obtained by inverting the covariance matrix of interference and noise to maximize the signal to interference and noise ratio (SINR). In practice, the covariance matrix is unknown and has to be estimated by a sample covariance matrix. The optimal array is thus estimated by inverting the sample covariance matrix. This procedure has been employed in optimal array research without challenge. However, it is shown in this paper that the estimated optimal array fails to yield the highest SINR in the case of unknown arriving-angles. Instead the highest SINR can be achieved by optimally estimating the arriving-angles of interference followed by a constrained matched filter, which maximizes the signal to noise ratio subject to canceling the interference from the estimated arriving-angles. In order to reduce the computational burden, an angle-tracking system for multiple targets is adopted to achieve the optimal estimation of arriving-angles. The resulting system of angle-tracking adaptive array offers the highest SINR at a computational burden only on 2 the order of N middot M 2 multiplications within a radar range-cellDeltatau, rather than N 3 multiplications in the well-known but questionable estimated optimal array. Here N is the number of sensors in the array and M the number of interference sources. Typically, N = 1,000 in a planar radar array, M = 2-10 and Deltatau = 1 mus. Numerical simulations confirm the theoretical results

关键词： Adaptive arrays Target tracking array signal processing signal to noise ratio Interference Covariance matrix sensor arrays Radar tracking signal detection Yield estimation

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Model for a Smart Network Monitoring a Wired sensor Matrix

Model for a Smart Network Monitoring a Wired Sensor Matrix

引用

sensor array and multichannel signal processing workshop

作者： L. Magagni D. Gennaretti M. Nicolini M. Sergio R. Guerrieri R. Canegallo ARCES University of Bologna Italy STMicroelectronics Italy

Monitoring harsh environments such as underwater scenarios or aircraft external surfaces pertains to important applications like assisted navigation and tactical surveillance; nevertheless, it poses additional challenges compared with standard applications. At Transducers2005 we presented a wired addressing architecture of distributed sensors for monitoring real-time in-situ pressure variations in underwater environment that faces the above-mentioned issues. This architecture consists in a double array of identical and interconnected smart nodes monitoring a matrix of passive sensors. In this paper, we present an analysis of the delay model related to the presented architecture and a calculation of the overall frame-rate of the system as a function of the geometrical topology of the arrays. The topology of the network, i.e. the length of each bus and the number of nodes, can be chosen according to the application, and directly affects the global capacitive load on the serial lines. Each serial line can be schematized with a distributed RC model for the flat cable plus a lumped capacitance for each smart node. Then, a 3 rd -order pi-segmented model of O'Brien-Savarino is calculated for a 16-block line with block length equal to 0.3 m. Thanks to that, the global time per iteration is calculated on each bus as well as the scanning time of the whole matrix and the frame rate for the system as a function of sensor distribution and of the aspect ratio of the matrix. This model can be employed to identify the optimal arrangement for the sensor matrix and smart node arrays

关键词： Monitoring Intelligent sensors sensor arrays Aircraft navigation Network topology Surveillance Delay Solid modeling Power cables Underwater cables

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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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Fast multilinear Singular Value Decomposition for higher-order Hankel tensors

Fast multilinear Singular Value Decomposition for higher-ord...

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sensor array and multichannel signal processing workshop

作者： Maxime Boizard Rémy Boyer Gérard Favier Pascal Larzabal SATIE Cachan France LSS Gifsur-Yvette France I3S Université de Nice Sophia Antipolis Sophia Antipolis France

The Higher-Order Singular Value Decomposition (HOSVD) is a possible generalization of the Singular Value Decomposition (SVD) to tensors, which have been successfully applied in various domains. Unfortunately, this decomposition is computationally demanding. Indeed, the HOSVD of a N th -order tensor involves the computation of the SVD of N matrices. Previous works have shown that it is possible to reduce the complexity of HOSVD for third-order structured tensors. These methods exploit the columns redundancy, which is present in the mode of structured tensors, especially in Hankel tensors. In this paper, we propose to extend these results to fourth order Hankel tensor. We propose two ways to extend Hankel structure to fourth order tensors. For these two types of tensors, a method to build a reordered mode is proposed, which highlights the column redundancy and we derive a fast algorithm to compute their HOSVD. Finally we show the benefit of our algorithms in terms of complexity.

关键词： Tensile stress signal processing algorithms Redundancy Complexity theory Harmonic analysis arrays

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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...

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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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A parametric spatio-temporal channel estimation technique for FDD UMTS uplink

A parametric spatio-temporal channel estimation technique fo...

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sensor array and multichannel signal processing workshop

作者： M. Chenu-Tournier P. Chevalier J.-P. Barbot Thomson-CSF-Commun. Gennevilliers France BSR/TBR/TSI Thomson CSF Communication France L. E. S. I. R Cachan France

Many multi-user receivers have been developed to mitigate the effects of both multipath and interference in CDMA radiocommunication networks. However despite of these works, very few papers have been devoted to the implementation of these receivers and, more precisely, to the channel estimation problem in both multi-user and FDD UMTS context. Assuming specular propagation channels and a multi-sensor receiver; the purpose of this paper is to present a parametric technique for the joint estimation of the delays and angles of arrival of the propagation channels between each user and the array of sensor for the FDD UMTS uplink. The quality of the latter are then evaluated through mean square error (MSE) computations and bit error rates (BER) after demodulation at the output of two receivers (2D-RAKE and decorrelator receiver) adapted to the FDD UMTS uplink.

关键词： Channel estimation 3G mobile communication sensor arrays Delay estimation Bit error rate Interference Multiaccess communication Propagation delay Mean square error methods Demodulation

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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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