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Massive MIMO meets decision fusion: Decode-and-fuse vs. decode-then-fuse

Massive MIMO meets decision fusion: Decode-and-fuse vs. deco...

引用

sensor array and multichannel signal processing workshop

作者： Domenico Ciuonzo Pierluigi Salvo Rossi Subhrakanti Dey Department of Industrial and Information Engineering Second University of Naples Italy Department of Engineering Sciences Uppsala University Sweden

We study channel-aware decision fusion over a multiple-input multiple-output (MIMO) channel in the largearray regime at the decision-fusion center (DFC). Inhomogeneous large-scale fading between the sensors and the DFC is consider in addition to the small-scale fading, and pilot-based channel estimation is performed at the DFC. Linear processing techniques are analyzed in order to design low-complexity alternatives to the optimum log-likelihood ratio test (LLRT). Performance evaluation based on Monte Carlo simulations are presented.

关键词： arrays Wireless sensor networks Fading MIMO Channel estimation Receiving antennas Complexity theory

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A moving horizon convex relaxation for mobile sensor network localization

A moving horizon convex relaxation for mobile sensor network...

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sensor array and multichannel signal processing workshop

作者： Andrea Simonetto Geert Leus Delft University of Technology Delft The Netherlands

In mobile sensor network localization problems we seek to estimate the position of the mobile sensor nodes by using a subset of pair-wise range measurements (among the nodes and with mobile anchors). When the sensor nodes are static, convex relaxations have been shown to provide a remarkably accurate approximate solution to this NP-hard estimation problem. In this paper, we propose a novel convex relaxation to tackle the more challenging dynamic case and we develop a moving horizon convex estimator based on a maximum a posteriori (MAP) formulation. The resulting estimator is then compared to standard extended and unscented Kalman filters both with respect to computational complexity and performance with simulated data. The results are promising, yet a more detailed analysis is needed.

关键词： Mobile communication Mobile computing Estimation Kalman filters Vectors Computational complexity Noise

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Localization and array shape estimation using software defined radio array testbed

Localization and array shape estimation using software defin...

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sensor array and multichannel signal processing workshop

作者： Akinbiyi Akindoyin Marc Willerton Athanassios Manikas Electrical and Electronics Engineering Imperial College London Imperial College London London London GB

The emergence of software defined radio (SDR) aims to increase flexibility as well as reduce cost, size, weight and power (SWAP) inherent in traditional hardware radios. This paper is concerned with addressing issues associated with the formation of an array system from multiple SDR boards where each has an independent local oscillators (LO) in a multi-antenna system using representative examples such as localization and array shape estimation. In particular, practical experimental results are initially presented for estimating the unknown location of a single source using an SDR array of known array geometry. Furthermore, in the case that the SDR array geometry is unknown, a novel array shape estimation algorithm is proposed. The proposed algorithm estimates the antenna locations without requiring any external sources. This is achieved by allowing the array elements operate as transceivers.

关键词： arrays signal processing Conferences Software Vectors Shape Estimation

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Statistical angular resolution limit for array radar with ultrawideband stochastic signals

Statistical angular resolution limit for array radar with ul...

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sensor array and multichannel signal processing workshop

作者： Xiaoli Zhou Hongqiang Wang Yongqiang Cheng Yuliang Qin Haowen Chen Bin Sun School of Electronic Science and Engineering National University of Defense Technology Changsha China

The 2-D angular resolution limit for array radar with ultrawideband (UWB) stochastic signals based on the statistical resolution theory is presented in this paper. The signal model of monostatic array radar with circular array configuration and UWB stochastic signals is established, while the resolution of two closely spaced targets is modelled as a binary hypothesis test. The statistical angular resolution limit (SARL) for array radar with UWB stochastic signals is derived based on the generalized likelihood ratio test (GLRT) with given probabilities of false alarm and detection. The effects of detection parameters, transmit waveforms, array geometry, signal-to-noise ratio and parameters of target on SARL are analysed. Compared with the conventional resolution defined by ambiguity function, the SARL reflects the practical resolution ability of radar and can provide an optimization criterion for radar system design.

关键词： arrays signal resolution Manganese Radar cross-sections Stochastic processes

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Ridge regression and Kalman filtering for target tracking in wireless sensor networks

Ridge regression and Kalman filtering for target tracking in...

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sensor array and multichannel signal processing workshop

作者： Sandy Mahfouz Farah Mourad-Chehade Paul Honeine Joumana Farah Hichem Snoussi Institut Charles Delaunay (CNRS) Université de Technologie de Troyes Troyes France Department of telecommunications Universite Saint-Esprit de Kaslik Lebanon

This paper introduces an original method for target tracking in wireless sensor networks that combines machine learning and Kalman filtering. A database of radio-fingerprints is used, along with the ridge regression learning method, to compute a model that takes as input RSSI information, and yields, as output, the positions where the RSSIs are measured. This model leads to a position estimate for each target. The Kalman filter is used afterwards to combine the model's estimates with predictions of the target's positions based on acceleration information, leading to more accurate ones.

关键词： Kalman filters Target tracking Wireless sensor networks Acceleration Vectors Noise Computational modeling

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Energy detection for decision fusion in wireless sensor networks over Ricean-mixture fading

Energy detection for decision fusion in wireless sensor netw...

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sensor array and multichannel signal processing workshop

作者： Pierluigi Salvo Rossi Domenico Ciuonzo Torbjörn Ekman Kimmo Kansanen Department of Industrial and Information Engineering Second University of Naples Italy Department of Electronics and Telecommunications Norwegian University of Science and Technology Norway

In this paper we focus on the energy detector for decision fusion in wireless sensor networks over multiple access channels. More specifically, we derive analytical performance in terms of global probability of false alarm and detection (including asymptotic performance for large number of sensors) when the fading is a Ricean mixture, i.e. channel coefficients are sampled from a Gaussian mixture (GM) distribution. The motivation for the GM is the ability to model real-world scenarios while keeping mathematical tractability. Analytical results are confirmed through numerical simulations.

关键词： Rayleigh channels signal to noise ratio Detectors Vectors arrays

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Strategies for principal component analysis in wireless sensor networks

Strategies for principal component analysis in wireless sens...

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sensor array and multichannel signal processing workshop

作者： Nisrine Ghadban Paul Honeine Clovis Francis Farah Mourad-Chehade Joumana Farah Universite Libanaise Beirut LB Institut Charles Delaunay (CNRS) Université de technologie de Troyes CNRS France Faculté de Génie Université Libanaise Lebanon Telecommunications department Faculty of Engineering Holy-Spirit University of Kaslik Lebanon

This paper deals with the issue of monitoring physical phenomena using wireless sensor networks. It provides principal component analysis for the time series of sensors' measurements. Without the need to compute the sample covariance matrix, we derive several in-network strategies to estimate the principal axis, including noncooperative and diffusion strategies. The performance of the proposed strategies is illustrated in the issue of monitoring gas diffusion.

关键词： Wireless sensor networks Principal component analysis Covariance matrices Temperature measurement Convergence Time series analysis Pollution measurement

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Optimal virtual array length under position imperfections

Optimal virtual array length under position imperfections

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sensor array and multichannel signal processing workshop

作者： Anders Mannesson Bo Bernhardsson Muhammad Atif Yaqoob Fredrik Tufvesson Dept. of Automatic Control Lund University SWEDEN Dept. of Electrical and Information Technology Lund University SWEDEN

This article contains a study of how spatial errors and receiver imperfections affect the angle of arrival estimation accuracy for virtual antenna arrays. A virtual antenna array consists of one receiver element whose location is tracked as the element is moved and in this work, linear arrays are studied. If the location of the receiver is tracked using an inertial measurement unit, an interesting trade-off emerges. The array should extend as far as possible but since the position estimates from the unaided inertial measurement unit become increasingly uncertain over time, the angle of arrival estimation will deteriorate. Several algorithms are available for estimating the angle of arrival in such a scenario but the one used for evaluation here is a sparse enforcing least squares method.

关键词： arrays signal to noise ratio Estimation Receiving antennas Frequency estimation

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MMOSPA-based direction-of-arrival estimation for planar antenna arrays

MMOSPA-based direction-of-arrival estimation for planar ante...

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sensor array and multichannel signal processing workshop

作者： Marcus Baum Peter Willett Uwe D. Hanebeck Department of Electrical and Computer Engineering University of Connecticut USA Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Karlsruhe Institute of Technology (KIT) Germany

This work is concerned with direction-of-arrival (DOA) estimation of narrowband signals from multiple targets using a planar antenna array. We illustrate the shortcomings of Maximum Likelihood (ML), Maximum a Posteriori (MAP), and Minimum Mean Squared Error (MMSE) estimation, issues that can be attributed to the symmetry in the likelihood function that must exist when there is no information about labeling of targets. We proffer the recently introduced concept of Minimum Mean OSPA (MMOSPA) estimation that is based on the optimal sub-pattern assignment (OSPA) metric for sets and hence inherently incorporates symmetric likelihood functions.

关键词： Direction-of-arrival estimation arrays Maximum likelihood estimation Vectors Antenna arrays

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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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