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Truncated nuclear norm minimization for tensor completion

Truncated nuclear norm minimization for tensor completion

引用

sensor array and multichannel signal processing workshop

作者： Long-Ting Huang H. C. So Yuan Chen Wen-Qin Wang Department of Electronic Engineering City University of Hong Kong Hong Kong SAR China School of Communication and Information Engineering University of Electronic Science and Technology of China Chengdu China

In this paper, a tensor n-mode matrix unfolding truncated nuclear norm is proposed, which is extended from the matrix truncated nuclear norm, to tensor completion problem. the alternating direction method of multipliers is utilized to solve this optimization problem. Meanwhile, the original two-step solution of the matrix truncated nuclear norm is reduced to one step. Employing the intermediate results returned by singular value shrinkage operator, rank information of each tensor unfolding matrix is not required and thus the computational complexity of the devised approach is not demanding. Computer simulation results demonstrate the effectiveness of the proposed method.

关键词： Tensile stress Minimization signal processing algorithms Optimization Matrix decomposition arrays signal processing

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Rapid prototyping of a cost effective and flexible 4×4 mimo testbed

Rapid prototyping of a cost effective and flexible 4×4 mimo...

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sensor array and multichannel signal processing workshop

作者： Douglas Kim Murat Torlak Department of Electrical Engineering University of Texas Dallas Richardson TX USA

In this paper, we present the design of a low cost 4 times 4 multiple in multiple out (MIMO) wireless communications system testbed. Over recent years, an increasing number of research laboratories have begun experimental studies aimed at evaluating the achievable performance gains of MIMO technology as proposed in theory. the complexity of the experimental test platforms employed by these institutions have also increased over the years, supporting greater numbers of antennas while providing even more real-time computational processing power. these tremendous increases in capability, however, come at a very steep price which the vast majority of the wireless research community cannot afford. As an alternative, we present the design of a low cost 4 times 4 MIMO testbed for researchers solely interested in analyzing the physical layer performance of various MIMO technologies. As a demonstration of our testbed capabilities, we also present the results of a direction of arrival (DOA) experiment using the MUSIC algorithm and also cite another body of work on the experimental performance analysis of space, time, polarization diversity schemes which was also completed with use of the MIMO testbed described herein.

关键词： MIMO Transceivers Radio frequency Wireless communication Antennas Receiving antennas Real time systems

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A distributed algorithm for two-way multiple-relay networks

A distributed algorithm for two-way multiple-relay networks

引用

sensor array and multichannel signal processing workshop

作者： C. Lameiro J. Vía I. Santamaria Communications Engineering Department (DICOM) University of Cantabria Santander Spain

In this paper we propose an efficient transmission strategy for the two-way relay channel (TWRC) with multiple relays, when these are multiple-input multiple-output (MIMO) transceivers that apply the amplify-and-forward (AF) protocol. Although the optimal beamforming strategy is known, it requires a central node, with channel state information (CSI) of the entire network, to compute all the beamforming matrices, which is impractical. To reduce the overhead, in this paper we present a distributed algorithm for the computation of the relay beamforming matrices. the proposed algorithm divides the problem in two stages. First, each relay computes its own beamforming matrix in parallel using only local CSI. Next, a distributed beamforming is applied to make the signals add up coherently at the nodes. Although the proposed algorithm is suboptimal, we show through simulations that it performs very close to the optimal achievable rate region.

关键词： Relays array signal processing MIMO Protocols signal processing algorithms signal to noise ratio Upper bound

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How to implement doubly-stochastic matrices for consensus-based distributed algorithms

How to implement doubly-stochastic matrices for consensus-ba...

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sensor array and multichannel signal processing workshop

作者： Sergio Valcarcel Macua Carlos Moreno Leon Jhoan Samuel Romero Silvana Silva Pereira Javier Zazo Alba Pagès-Zamora Roberto López-Valcarce Santiago Zazo Escuela Técnica Superior de Ingenieros de Telecomunicación Universidad Politécnica de Madrid Spain SPCOM Group Universitat Politécnica de Catalunya-Barcelona Tech Spain GPSC Universidade de Vigo Spain

Doubly-stochastic matrices are usually required by consensus-based distributed algorithms. We propose a simple and efficient protocol and present some guidelines for implementing doubly-stochastic combination matrices even in noisy, asynchronous and changing topology scenarios. the proposed ideas are validated with the deployment of a wireless sensor network, in which nodes run a distributed algorithm for robust estimation in the presence of nodes with faulty sensors.

关键词： signal processing algorithms Topology Distributed algorithms Robustness Estimation Protocols Network topology

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A sparse regularization technique for source localization with non-uniform sensor gain

A sparse regularization technique for source localization wi...

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sensor array and multichannel signal processing workshop

作者： Christian Weiss Abdelhak M. Zoubir Technische Universität Darmstadt Graduate School of Computational Engineering Darmstadt Germany

A robust sparse regularization technique with applications to source localization in the presence of non-uniform gain distribution is presented. As a key component in sparse optimization, a proper choice of the regularization parameter is crucial. It renders high impact on the localization performance and has to account for any kind of model perturbation. Our proposed technique utilizes a statistical framework to provide a direct relation between the physical system parameters and the regularization parameter of the resulting optimization problem. It addresses the joint effects of sensor gain variations and noise. As a figure of merit, we consider the mean-squared error (MSE) between the perturbed measurements and the assumed underlying model. An upper bound of the MSE is attained in order to estimate the regularization parameter. the presented method shows good performance for moderate gain variances even in low SNR regimes.

关键词： Robustness Gain arrays signal to noise ratio Upper bound

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On the Cramér-Rao bound for the constrained and unconstrained complex parameters

On the Cramér-Rao bound for the constrained and unconstrain...

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sensor array and multichannel signal processing workshop

作者： Esa Ollila Visa Koivunen Jan Eriksson Department of Mathematical Sciences University of Oulu Oulu Finland Signal Processing Laboratory Helsinki University of Technology Finland

We derive a complex form of the unconstrained and constrained Cramer-Rao lower bound (CRB) of composite real parameters formed by stacking the real and imaginary part of the complex parameters. the derived complex constrained and unconstrained CRB is easy to calculate and possesses similar structure as in the real parameter case but with the real covariance, Jacobian and the Fisher information matrix replaced by complex matrices with analogous interpretations. the advantage of the complex CRB is that it is oftentimes easier to calculate than its real form. It is highlighted that a statistic that attains a bound on the complex covariance matrix alone do not necessarily attain the CRB since complex covariance matrix does not provide a full second-order description of a complex statistic since also the pseudo-covariance matrix is needed. Our derivations also lead to some new insights and theory that are similar to real CRB theory.

关键词： Covariance matrix Jacobian matrices Symmetric matrices Matrices signal processing Estimation Cramer-Rao bounds

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Gaussian Source Detection and Spatial Spectral Estimation Using a Coprime sensor array with the Min Processor

Gaussian Source Detection and Spatial Spectral Estimation Us...

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作者： Liu, Yang Buck, John R. Electrical and Computer Engineering Department University of Massachusetts Dartmouth DartmouthMA02747 United States

A coprime sensor array (CSA) interleaves two undersampled uniform linear arrays with coprime undersampling factors and has recently found broad applications in signal detection and estimation. CSAs commonly use the product processor by multiplying the scanned responses of two colinear subarrays to estimate the spatial power spectral density (PSD) of the received signal. this paper proposes a new CSA processor, the CSAmin processor, which chooses the minimum between the two CSA subarray periodograms at each bearing to estimate the spatial PSD. the proposed CSAmin processor resolves the CSA subarray spatial aliasing equally well as the product processor. For an extended aperture CSA, the CSAmin reduces the peak sidelobe height and total sidelobe area over the product processor for the same CSA geometry. Moreover, unlike the PSD estimate from the product processor, the PSD estimate from the CSAmin is guaranteed to be positive semidefinite. this paper derives the probability density function, the complementary cumulative distribution function (CCDF, or tail distribution), and the first two moments of the CSAmin PSD estimator in closed form for Gaussian sources in white Gaussian noise. Numerical simulations verify the derived CSAmin statistics and demonstrate that the CSAmin improves the performance over the product processor in detecting narrowband Gaussian sources in the presence of loud interferers and noise. the CSAmin spatial PSD estimate achieves lower variance than the product processor estimate, and keeps the PSD estimate unbiased for white Gaussian processes and asymptotically unbiased for nonwhite Gaussian processes. © 2017 ieee.

关键词： Covariance matrix

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Mainlobe Deceptive Jammer Suppression with FDA-MIMO Radar

Mainlobe Deceptive Jammer Suppression with FDA-MIMO Radar

引用

sensor array and multichannel signal processing workshop

作者： Jingwei Xu Jialin Kang Guisheng Liao Hing Cheung So National Lab of Radar Signal Processing Xidian Univesity Xi'an Shaanxi China City University of Hong Kong Hong Kong China

Anti-jamming technique has become an increasing demand in radar systems due to the fast development of electronic countermeasures. To alleviate the extremely difficult deceptive jammer suppression problem, frequency diverse array (FDA) based multiple-input multiple-output (MIMO) radar is devised and a sample selection based deceptive jammer suppression method is developed. the basic principle of the FDA-MIMO radar regarding its superiority in canceling the deceptive jammers is studied in detail. In order to handle the pseudorandom distribution issue of false targets within the secondary training samples, a subspace projection based sample selection method is proposed. When certain a priori knowledge of the true target is available, the time-delayed false targets can be adequately suppressed, even if the latter are in the mainlobe direction. Simulation examples demonstrate the effectiveness of our solution.

关键词： Jamming array signal processing Delay effects Radar countermeasures Doppler radar

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Direction of arrival estimation using nested vector-sensor arrays via tensor modeling

Direction of arrival estimation using nested vector-sensor a...

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sensor array and multichannel signal processing workshop

作者： Keyong Han Arye Nehorai Preston M. Green Department of Electrical and Systems Engineering Washington University in St. Louis MO USA

We propose a new class of nested vector-sensor arrays, via tensor modeling, which is capable of increasing the number of degrees of freedom (DOFs) for direction-of-arrival (DOA) estimation, compared with the uniform linear array (ULA). By using one component's information of the interspectral tensor, the proposed nested vector-sensor array can provide O(N 2 ) DOFs with only N physical sensors. To utilize the increased DOFs, a novel spatial smoothing approach is proposed, which preserves the data structure and avoids reorganization via tensor modeling. Further, we propose corresponding direction of arrival (DOA) estimation strategies. Based on the analytical results, we consider two main applications: electromagnetic (EM) vector sensors and acoustic vector sensors. We show that the DOA estimation performance is greatly improved due to the combination of the nested-array strategy and tensor modeling. the effectiveness of the proposed methods is verified through numerical examples.

关键词： arrays Vectors Tensile stress Estimation Direction-of-arrival estimation Acoustics Electromagnetics

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A novel approach to rank determination of multichannel data covariance matrices

A novel approach to rank determination of multichannel data ...

引用

ieee workshop on Statistical signal and array processing

作者： P.M. Djuric Department of Electrical Engineering State University of New York Stony Brook Stony Brook NY USA

the rank selection problem of a multichannel data covariance matrix is addressed by Bayesian methodology. A maximum a posteriori solution is derived, and a bootstrap technique for its implementation proposed. Our rule is tested on simulated sensor array data that represent random signals embedded in white Gaussian noise. the tests include comparisons with the popular AIC and MDL criteria. the results show that the Bayesian rule outperforms them, particularly for low signal-to-noise ratios and small direction-of-arrival separations.

关键词： Covariance matrix sensor arrays Eigenvalues and eigenfunctions Testing Bayesian methods Gaussian noise array signal processing Parameter estimation signal to noise ratio signal processing

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