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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801-810 订阅

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LOCALIZATION IN sensor NETWORKS - A MATRIX REGRESSION APPROACH

LOCALIZATION IN SENSOR NETWORKS - A MATRIX REGRESSION APPROA...

引用

ieee sensor array and multichannel signal processing workshop

作者： Honeine, Paul Richard, Cedric Essoloh, Mehdi Snoussi, Hichem Univ Technol Troyes CNRS Inst Charles Delaunay FRE 2848LM2S F-10010 Troyes France

ISBN: (纸本)9781424422401

In this paper, we propose a new approach to sensor localization problems, based on recent developments in machine leaning. The main idea behind it is to consider a matrix regression method between the ranging matrix and the matrix of inner products between positions of sensors, in order to complete the latter. Once we have learnt this regression from information between sensors of known positions (beacons), we apply it to sensors of unknown positions. Retrieving the estimated positions of the latter can be done by solving a linear system. We propose a distributed algorithm, where each sensor positions itself with information available from its nearby beacons. The proposed method is validated by experimentations.

关键词： Regression analysis

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OPTIMAL NODE DENSITY FOR TWO-DIMENSIONAL sensor arrayS

OPTIMAL NODE DENSITY FOR TWO-DIMENSIONAL SENSOR ARRAYS

引用

ieee sensor array and multichannel signal processing workshop

作者： Sung, Youngchul Poor, H. Vincent Yu, Heejung Korea Adv Inst Sci & Technol Dept Elect Engn Taejon 305701 South Korea Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

ISBN: (纸本)9781424422401

The problem of optimal node density for ad hoc sensor networks deployed for making inferences about two dimensional correlated random fields is considered. Using a symmetric first order conditional autoregressive Gauss-Markov random field model, large deviations results are used to characterize the asymptotic per-node information gained from the array. This result then allows an analysis of the node density that maximizes the information under an energy constraints yielding insights into the trade-offs among the information, density and energy.

关键词： Markov processes

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NEW SUBSPACE UPDATING ALGORITHM FOR ADAPTIVE DIRECTION ESTIMATION AND TRACKING AND ITS STATISTICAL ANALYSIS

NEW SUBSPACE UPDATING ALGORITHM FOR ADAPTIVE DIRECTION ESTIM...

引用

ieee sensor array and multichannel signal processing workshop

作者： Xin, Jingmin Zheng, Nanning Sano, Akira Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Keio Univ Dept Syst Design Engn Yokohama Kanagawa 2238522 Japan

ISBN: (纸本)9781424422401

Subspace estimation is of importance to high-resolution direction estimation in array processing. In this paper, a new recursive least-squares (RLS) algorithm is proposed for null space estimation, which is used to estimate or track the directions of coherent and/or incoherent signals impinging on a uniform linear array (ULA). Especially by investigating the expectation computation of an inverse matrix, the statistical analysis of the RLS algorithm is studied in the mean and mean-squares senses in stationary environment, and further the mean-square-error (MSE) and mean-square derivation (MSD) learning curves are derived explicitly. The theoretical analyses and effectiveness of the proposed RLS algorithm are substantiated through numerical examples.

关键词： array processing

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NON-MYOPIC sensor SCHEDULING FOR A CENTRALIZED sensor NETWORK

NON-MYOPIC SENSOR SCHEDULING FOR A CENTRALIZED SENSOR NETWOR...

引用

ieee sensor array and multichannel signal processing workshop

作者： Shah, Himanshu Morrell, Darryl Arizona State Univ Dept Elect Engn Tempe Campus Tempe AZ 85287 USA Arizona State Univ Dept Engn Tempe AZ 85287 USA

ISBN: (纸本)9781424422401

When tracking a target in a sensor network with constrained resources, the target state estimate error can be significantly reduced using non-myopic sensor scheduling strategies. Integer non-linear programming has been used to obtain myopic sensor schedules [1]. In this paper, we apply it to a non-myopic sensor scheduling scenario consisting of a network of acoustic sensors in a centralized sensor network;there is one fusion center that combines measurements to update target belief. We cast this problem, which we call the Central Node Scheduling problem, as an integer non-linear programming problem with the objective of minimizing the total predicted tracking error over an M step planning horizon subject to sensor usage and start-up cost constraints. Using Monte Carlo simulations, we show the benefits of this approach for the centralized sensor network.

关键词： Nonlinear programming

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ON THE ESTIMATION OF THE COVARIANCE EIGENSPECTRUM OF array SAMPLE OBSERVATIONS

ON THE ESTIMATION OF THE COVARIANCE EIGENSPECTRUM OF ARRAY S...

引用

ieee sensor array and multichannel signal processing workshop

作者： Rubio, Francisco Mestre, Xavier CTTC Barcelona 08860 Spain

ISBN: (纸本)9781424422401

In this paper, we propose an estimator of the eigenspectrum of the array observation covariance matrix that builds upon the well-known power method and is consistent for an arbitrarily large array dimension. Traditional estimators based on the eigendecomposition of the sample covariance matrix are known to be consistent provided that the number of observations grow to infinity with respect to any other dimension in the signal model. On the contrary, in order to avoid the loss in the estimation accuracy associated with practical finite sample-size situations, a generalization of the conventional implementation is derived that proves to be a very good approximation for a sample-size and an array dimension that are comparatively large. The proposed solution is applied to the construction of a subspace-based extension of the Capon source power estimator. For our purposes, we resort to the theory of the spectral analysis of large dimensional random matrices, or random matrix theory. As it is shown via numerical simulations, the new estimator turns out to allow for a significantly improved estimation accuracy in practical finite sample-support scenarios.

关键词： Covariance matrix

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OPTIMIZING THE PERFORMANCE OF THE PARTIAL ADAPTIVE CONCENTRIC RING array IN THE PRESENCE OF PRIOR KNOWLEDGE

OPTIMIZING THE PERFORMANCE OF THE PARTIAL ADAPTIVE CONCENTRI...

引用

ieee sensor array and multichannel signal processing workshop

作者： Vicente, L. M. Ho, K. C. Univ Missouri Dept Elect & Comp Engn Columbia MO 65211 USA

ISBN: (纸本)9781424422401

The partial adaptive concentric ring array (CRA) has been successfully applied to 3-D beamforming because of its flexibility, faster tracking ability and reduced computation with respect to the fully adaptive CRA. In some cases, prior knowledge regarding some interferences is available so that better beamformers can be designed. The previous method that exploits prior knowledge by using a fixed penalty factor could not guarantee in maintaining a low residual interference and noise level. We propose in this paper an adaptive beamformer that automatically seeks out the optimum penalty factor to achieve the best performance. The proposed beamformer outperforms the previous design in maintaining a higher output signal to interference and noise ratio, even after the characteristics of the interferences have changed. The performance of the proposed beamformer is evaluated through simulations.

关键词： Beamforming

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THE HYBRID CRAMER-RAO BOUND AND THE GENERALIZED GAUSSIAN LINEAR ESTIMATION PROBLEM

THE HYBRID CRAMER-RAO BOUND AND THE GENERALIZED GAUSSIAN LIN...

引用

ieee sensor array and multichannel signal processing workshop

作者： Noam, Y. Messer, H. Tel Aviv Univ Sch Elect Engn IL-69978 Tel Aviv Israel

ISBN: (纸本)9781424422401

This paper explores the Hybrid Cramer-Rao Lower-bound (HCRLB) for a Gaussian generalized linear estimation problem in which some of the unknown parameters are deterministic while the other are random. In general, the HCRLB on the non-Bayesian parameters is not asymptotically tight. However, we show that for the generalized Gaussian linear estimation problem, the HCRLB of the deterministic parameters coincides with the CRLB, so it is an asymptotically tight bound. In addition, we show that the ML/MAP estimator Ill is asymptotically efficient for the non-Bayesian parameters while providing optimal estimate of the Bayesian parameters. The results are demonstrated on a signal processing example. It is shown the Hybrid estimation can increase spectral resolution if some prior knowledge is available only on a subset of the parameters.

关键词： signal processing

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FAST DISTRIBUTED CONSENSUS ALGORITHMS BASED ON ADVECTION-DIFFUSION PROCESSES

FAST DISTRIBUTED CONSENSUS ALGORITHMS BASED ON ADVECTION-DIF...

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ieee sensor array and multichannel signal processing workshop

作者： Sardellitti, S. Giona, M. Barbarossa, S. Univ Roma La Sapienza INFOCOM Dept Via Eudossiana 18 I-00184 Rome Italy Univ Roma La Sapienza Dept Chem Engn Via Eudossiana 18 I-00184 Rome Italy

ISBN: (纸本)9781424422401

Distributed consensus algorithms have recently gained a large interest in sensor networks as a way to achieve globally optimal decisions in a totally decentralized way, that is without the need of sending all the data collected by the sensors to a fusion center. The goal of this work is to show that modeling a consensus algorithm as the homogenization process of a fluid through an advection-diffusion process provides a fundamental clue to design innovative consensus algorithms whose convergence rate can be increased by acting on the (equivalent) advection mechanism, without increasing the coverage radius of any sensor. In particular, we show the increase of convergence rate resulting from a proper interplay between advection and diffusion mechanisms.

关键词： sensor networks

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ROBUST ADAPTIVE VECTOR sensor processing IN THE PRESENCE OF MISMATCH AND FINITE SAMPLE SUPPORT

ROBUST ADAPTIVE VECTOR SENSOR PROCESSING IN THE PRESENCE OF ...

引用

ieee sensor array and multichannel signal processing workshop

作者： Poulsen, Andrew J. Nadakuditi, Raj Rao Baggeroer, Arthur B. MIT Cambridge MA 02139 USA

ISBN: (纸本)9781424422401

We present analytical results which quantify the effect of system mismatch and finite sample support on acoustic vector sensor array performance. One noteworthy result is that the vector aspect of the array "dampens" the effect of array mismatch, enabling deeper true nulls. This is accomplished because the variance of the vector sensor array spatial response (due to rotational, positional and filter gain/phase perturbations) decreases in the sidelobes, unlike arrays of omnidirectional hydrophones. When sensor orientation is measured within a reasonable tolerance, the beampattern variance dominates the average sidelobe power response. Our analysis also suggests that vector sensor array gain performance is less sensitive to rotational than to positional perturbations in the regions of interest. We analytically characterize the eigen-SNR threshold, which depends on the signal and noise covariance and the number of noise-only and signal-plus-noise snapshots, below which (asymptotically speaking) reliable detection using sample eigenvalue based techniques is not possible. Thus for a given number of snapshots, since the dimensionality of the snapshot in a vector sensor array is larger than that of a hydrophone-only array, the eigen-SNR detection threshold will be greater whenever the eigenvector information is discarded. We present processing techniques customized to the unique characteristics of vector sensors, which exploit information encoded in the sample eigenvectors and are robust to the mismatch and finite sample support issues. These methods include adaptive processing techniques with multiple white noise constraints.

关键词： Hydrophones

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MAP-PF 3D POSITION TRACKING USING MULTIPLE sensor arrayS

MAP-PF 3D POSITION TRACKING USING MULTIPLE SENSOR ARRAYS

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ieee sensor array and multichannel signal processing workshop

作者： Bell, Kristine L. Pitre, Richard George Mason Univ Dept Stat Fairfax VA 22030 USA LLC RobTre Res Clarksville TN 21029 USA

ISBN: (纸本)9781424422401

The maximum a posteriori penalty function (MAP-PF) approach is applied to three-dimensional (3D) target position tracking of multiple wideband sources using multiple distributed sensor arrays. The track estimation problem is formulated directly from the array data using the maximum a posteriori (MAP) estimation criterion. The penalty function (PF) method of nonlinear programming is used to obtain a tractable solution. A sequential update procedure is developed in which penalized maximum likelihood estimates of target directions-of-arrival (DOAs) and spectra are computed at each array and then used as synthetic measurements in a set of extended Kalman filters. The two steps are coupled via the penalty function. The current target states are used to guide the DOA/spectrum estimation, and the estimated signal spectra control the influence of the DOA estimates from each array on the final track estimates. The algorithm can be implemented in a decentralized manner where DOA/spectrum estimation is performed at the arrays, and track estimation is performed at a central processing site.

关键词： Direction of arrival

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