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Distributed source processing with linear nested arrays

Distributed source processing with linear nested arrays

引用

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 St. Louis MO USA

We consider the problem of using linear nested arrays to estimate the direction of arrival (DOA) of spatially distributed sources and detect the source number, where we have more sources than the actual physical sensors. Angular spread, caused by the multipath nature of distributed sources, makes the commonly used point source assumption ineffective. In this paper, we establish the signal model for distributed sources using a nested array. Due to the characteristics of distributed sources, the regular spatial smoothing technique, which is used to exploit the increased degrees of freedom provided by the co-array, no longer works. We thus propose a novel spatial smoothing approach to circumvent this problem. Based on the analytical results, we construct corresponding DOA estimation and source number detection methods. The effectiveness of the proposed methods is verified through numerical examples.

关键词： arrays Direction-of-arrival estimation Vectors Estimation Smoothing methods Manifolds Multiple signal classification

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Detection in analog sensor networks with a large scale antenna fusion center

Detection in analog sensor networks with a large scale anten...

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sensor array and multichannel signal processing workshop

作者： Feng Jiang Jie Chen A. Lee Swindlehurst Center for Pervasive Communications and Computing University of California at Irvine Irvine CA USA

We consider the distributed detection of a zero-mean Gaussian signal in an analog wireless sensor network with a fusion center (FC) configured with a large number of antennas. The transmission gains of the sensor nodes are optimized by minimizing the ratio of the log probability of detection (PD) and log probability of false alarm (PFA). We show that the problem is convex with respect to the squared norm of the transmission gains, and that a closed-form solution can be found using the Karush-Kuhn-Tucker conditions. Our results indicate that a constant PD can be maintained with decreasing sensor transmit gain provided that the number of antennas increases at the same rate. This is contrasted with the case of a singleantenna FC, where PD is monotonically decreasing with transmit gain. On the other hand, we show that when the transmit power is high, the single- and multi-antenna FC both asymptotically achieve the same PD upper bound.

关键词： Wireless sensor networks Upper bound Wireless communication Transmitting antennas Antenna measurements Fading

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Mismatch loss constrained instrumental variable filtering for GSM passive bistatic radar

Mismatch loss constrained instrumental variable filtering fo...

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sensor array and multichannel signal processing workshop

作者： Florian Bahlke Reda Zemmari Ulrich Nickel Marius Pesavento Sensor Data and Information Fusion Dept. Fraunhofer FKIE Wachtberg Germany Communication Systems Group University of Science Darmstadt Darmstadt Germany

In this paper we address the design and evaluation of a mismatched filter for application in GSM passive bistatic radar (PBR). We discuss various exisiting approaches for the filter design, which originate from active radar systems and have to be adapted for PBR. Our proposed method minimizes the L p -norm of the sidelobe level energies under an upper-bound constraint on the SNR loss relative to the matched filter. We show the suitibility of this approach for GSM based PBR and give a performance evaluation. Based on simulated and measured data, we show that the proposed filter can improve range resultion and target detection of the PBR system.

关键词： GSM Vectors signal to noise ratio Passive radar Conferences Bistatic radar

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Nonlinear parameter estimation in statistical manifolds

Nonlinear parameter estimation in statistical manifolds

引用

sensor array and multichannel signal processing workshop

作者： Xuezhi Wang Yongqiang Cheng Bill Moran Department of Electrical and Electronic Engineering University of Melbourne Australia School of Electronic Science and Engineering National University of Defense Technology China Defense Science Institute University of Melbourne Australia

Many nonlinear parameter estimation problems can be described by the class of curved exponential families. The latter are fundamental concept in the framework of Information Geometry. This paper shows that when a closed-form statistical model is available the problem can be mapped onto the corresponding statistical manifolds via fixed parameterizations and thus solved optimally through a manifold gradient method. The solution process involves a dual projection which iteratively operates under the e-connection and m-connection in the flat manifolds with the coordinate systems in which the Cramér Rao Bound is attained. An example of tracking a moving target by two bearings-only sensors with location uncertainties is presented to demonstrate the efficiency and optimality of this manifold based method as well as the associated geometrical interpretation.

关键词： Manifolds Target tracking Estimation Uncertainty signal processing Conferences Educational institutions

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Gaussian processes regressors for complex proper signals in digital communications

Gaussian processes regressors for complex proper signals in ...

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sensor array and multichannel signal processing workshop

作者： Rafael Boloix-Tortosa F. Javier Payán-Somet Juan José Murillo-Fuentes Dep. Teoría de la Señal y Comunicaciones Universidad de Sevilla Sevilla Spain

In this paper we develop the complex-valued version of the Gaussian processes for regression (GPR) for proper complex signals. This tool has proved to be useful in the nonlinear detection in digital communications in real valued models. GPRs can be cast as nonlinear MMSE where hyperparameters can be tuned optimizing a marginal likelihood (ML). This feature allows for a flexible kernel that can easily adapt either to a linear or nonlinear solution. We introduce the complex-valued form of the GPR, and develop it for the proper complex case. We also deal with the optimization of the ML. Some experiments included illustrate the good performance of the proposal.

关键词： Ground penetrating radar Vectors Gaussian processes Kernel Training signal processing Detectors

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Distributed detection of correlated random processes under energy and bandwidth constraints

Distributed detection of correlated random processes under e...

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sensor array and multichannel signal processing workshop

作者： Juan Augusto Maya Leonardo Rey Vega Cecilia G. Galarza Andrés Altieri University of Buenos Aires CSC-CONICET Buenos Aires Argentina

We analyze a binary hypothesis testing problem built on a wireless sensor network (WSN). Using Large Deviation Theory (LDT), we compute the probability error exponents of a distributed scheme for detecting a correlated circularly-symmetric complex Gaussian process under the Neyman-Pearson framework. Using an analog scheme, the sensors transmit scaled versions of their measurements several times through a multiple access channel (MAC) to reach the fusion center (FC), whose task is to decide whether the process is present or not. In the analysis, we consider the energy constraint on each node transmission. We show that the proposed distributed scheme requires relatively few MAC channel uses to achieve the centralized error exponents when detecting correlated Gaussian processes.

关键词： Detectors Bandwidth Wireless sensor networks Covariance matrices Gaussian processes Vectors Communication channels

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On the uniform asymptotic convergence of a distributed particle filter

On the uniform asymptotic convergence of a distributed parti...

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sensor array and multichannel signal processing workshop

作者： Joaquín Míguez Department of Signal Theory & Communications Universidad Carlos III de Madrid. Leganés Madrid Spain

Distributed signal processing algorithms suitable for their implementation over wireless sensor networks (WSNs) and ad hoc networks with communications and computing capabilities have become a hot topic during the past years. One class of algorithms that have received special attention are particles filters. However, most distributed versions of this type of methods involve various heuristic or simplifying approximations and, as a consequence, classical convergence theorems for standard particle filters do not hold for their distributed counterparts. In this paper, we look into a distributed particle filter scheme that has been proposed for implementation in both parallel computing systems and WSNs, and prove that, under certain stability assumptions regarding the physical system of interest, its asymptotic convergence is guaranteed. Moreover, we show that convergence is attained uniformly over time. This means that approximation errors can be kept bounded for an arbitrarily long period of time without having to progressively increase the computational effort.

关键词： Convergence signal processing algorithms Approximation methods Wireless sensor networks signal processing Approximation algorithms Monte Carlo methods

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The correlation preserving transform resolves phase ambiguity in complex decorrelating transforms

The correlation preserving transform resolves phase ambiguit...

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sensor array and multichannel signal processing workshop

作者： C. Cheong Took S. C. Douglas D. P. Mandic Department of Computing University of Surrey UK Department of Electrical Engineering Southern Methodist University USA Department of Electronic Engineering Imperial College London UK

A solution to the problem of intraference is obtained by recognising that the current diagonalisation schemes for the complex symmetric pseudocovariance matrix are not adequate to preserve its intrinsic complex-valued nature. To this end, we propose the correlation preserving transform (CPT), which both maintains the degree of the intrinsic correlation within a recovered bivariate source (minimum intraference) and minimises the correlation between different bivariate sources (minimum interference). Unlike the existing literature, this work therefore considers both the correlation structure between the data channels within bivariate sources and inter-source correlation. The efficacy of the proposed method is validated through analysis and simulations.

关键词： Correlation Transforms signal processing Symmetric matrices Covariance matrices Matrix decomposition Approximation methods

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Envelope only TDOA estimation for sensor network self calibration

Envelope only TDOA estimation for sensor network self calibr...

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sensor array and multichannel signal processing workshop

作者： Dan Ohev Zion Hagit Messer School of Electrical Engineering Tel Aviv University

Self localization of nodes in wireless sensor networks (WSN) attracts wide interest in the past few years. The main challenge is to adopt calibration algorithms to the low-power/ low-bandwidth/ low-cost constrains on each sensor in the network. In this paper we present a novel approach, in which position-known anchors transmit special calibration signals simultaneously. Each node measures only the received signal strength (RSSI), i.e. envelope of the sum of received signals, and estimates time difference of arrivals (TDOA) of the signals. Two or more TDOA measurements allow the node to estimate its own position in the WSN. Based on an analysis of the appropriate Cramer Rao Lower Bound (CRLB) on the TDOA estimates, we suggest the choice of calibrating signals which provide optimal performance. The theoretical results are supported by simulations of the Maximum Likelihood (ML) estimators and numerical evaluation of the CRLB for a specific scenario of interest.

关键词： Conferences Wireless sensor networks Position measurement

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A sparse Bayesian learning algorithm with dictionary parameter estimation

A sparse Bayesian learning algorithm with dictionary paramet...

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sensor array and multichannel signal processing workshop

作者： Thomas L. Hansen Mihai A. Badiu Bernard H. Fleury Bhaskar D. Rao Dept. of Electronic Systems Aalborg University Aalborg Denmark Dept. of Electrical and Computer Engineering University of California at San Diego CA USA

This paper concerns sparse decomposition of a noisy signal into atoms which are specified by unknown continuous-valued parameters. An example could be estimation of the model order, frequencies and amplitudes of a superposition of complex sinusoids. The common approach is to reduce the continuous parameter space to a fixed grid of points, thus restricting the solution space. In this work, we avoid discretization by working directly with the signal model containing parameterized atoms. Inspired by the “fast inference scheme” by Tipping and Faul we develop a novel sparse Bayesian learning (SBL) algorithm, which estimates the atom parameters along with the model order and weighting coefficients. Numerical experiments for spectral estimation with closely-spaced frequency components, show that the proposed SBL algorithm outperforms state-of-the-art subspace and compressed sensing methods.

关键词： signal processing algorithms Dictionaries Computational modeling Bayes methods Estimation signal to noise ratio Atomic clocks

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