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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31-40 订阅

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WIDEBAND DOA ESTIMATION FOR NONUNIFORM LINEAR arrayS WITH WIENER array INTERPOLATION

WIDEBAND DOA ESTIMATION FOR NONUNIFORM LINEAR ARRAYS WITH WI...

引用

ieee sensor array and multichannel signal processing workshop

作者： Yasar, T. Kaya Tuncer, T. Engin METU Dept Elect & Elect Engn Ankara Turkey

ISBN: (纸本)9781424422401

Coherent wideband DOA estimation for non-uniform linear arrays (NLA) is considered. array interpolation is used for two mappings. In the first mapping, NLA is mapped to a uniform linear array with the same array aperture. In the second mapping covariance matrices for each frequency bin are mapped to a single one at the center frequency for coherent DOA estimation. A Wiener formulation is used for array interpolation where both signal and noise powers are estimated with maximum likelihood method. Different approaches are compared and the advantages of wideband processing versus narrowband processing are outlined. The accuracy of the SNR estimation is high and it is shown that Wiener array interpolation significantly improves the DOA estimation performance in both narrowband and wideband

关键词： Mapping

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COMPRESSIVE SENSING FOR sensor CALIBRATION

COMPRESSIVE SENSING FOR SENSOR CALIBRATION

引用

ieee sensor array and multichannel signal processing workshop

作者： Cevher, Volkan Baraniuk, Richard Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA

ISBN: (纸本)9781424422401

We consider a calibration problem, where we determine an unknown sensor location using the known track of a calibration target and a known reference sensor location. We cast the calibration problem as a sparse approximation problem where the unknown sensor location is determined over a discrete spatial grid with respect to the reference sensor. To achieve the calibration objective, low dimensional random projections of the sensor data are passed to the reference sensor, which significantly reduces the inter-sensor communication bandwidth. The unknown sensor location is then determined by solving an l(1)-norm minimization problem (linear program). Field data results are provided to demonstrate the effectiveness of the approach.

关键词： Direction-of-arrival estimation Microphone arrays sensor networks

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ALGORITHMS FOR TRACKING WITH AN array OF MAGNETIC sensorS

ALGORITHMS FOR TRACKING WITH AN ARRAY OF MAGNETIC SENSORS

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ieee sensor array and multichannel signal processing workshop

作者： Kozick, Richard J. Sadler, Brian M. Bucknell Univ Lewisburg PA 17837 USA Army Res Lab Adelphi MD 20783 USA

ISBN: (纸本)9781424422401

We consider the problem of tracking a magnetic target as it travels in a straight-line path in the vicinity of N magnetic sensors. The target is modeled as a magnetic dipole, and we study tracking algorithms when the sensors are total-field (scalar) magnetometers and vector magnetometers. A novel, computationally-efficient vector-field algorithm is presented that jointly processes the data from N sensors, yielding estimates of the track and the target dipole moment vector. Simulation examples are included to illustrate the performance of the total-field and vector algorithms.

关键词： Magnetic sensors

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array SELF-CALIBRATION USING SAGE ALGORITHM

ARRAY SELF-CALIBRATION USING SAGE ALGORITHM

引用

ieee sensor array and multichannel signal processing workshop

作者： Chung, Pei-Jung Wan, Shuang Univ Edinburgh Sch Engn & Elect Inst Digital Commun Edinburgh EH8 9YL Midlothian Scotland

ISBN: (纸本)9781424422401

The performance of most existing array processing algorithms relies heavily on the precise knowledge of array manifold, which is decided by individual sensor characteristics and array configuration. A major challenge for self-calibration techniques is the increased computational burden due to additional perturbation parameters. In this contribution, a novel procedure for array self-calibration is presented. We apply the well known numerical method, the Space Alternating Generalized EM algorithm (SAGE), to simplify the multi-dimensional search procedure required for finding maximum likelihood (ML) estimates. Simulation shows that the proposed algorithm outperforms existing methods that are based on the small perturbation assumption. Furthermore, the proposed algorithm remain robust in critical scenarios including large sensor position errors and closely located signals.

关键词： array processing

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LOW-COMPLEXITY IMPLEMENTATION FOR WORST-CASE OPTIMIZATION-BASED ROBUST ADAPTIVE BEAMFORMING

LOW-COMPLEXITY IMPLEMENTATION FOR WORST-CASE OPTIMIZATION-BA...

引用

ieee sensor array and multichannel signal processing workshop

作者： Jiang, Biao Hangzhou Appl Acoust Res Inst Underwater Acoust Syst Lab Hangzhou Peoples R China

ISBN: (纸本)9781424422401

In this paper, an efficient low-complexity robust adaptive beamforming method based on worst-case performance optimization is proposed. Lagrangian method was applied to obtain the expression for the robust adaptive weight vector, which is optimized on the boundary of the steering vector uncertainty region, that is to say, in the worst mismatch case. Combining the constraint condition and the eigendecomposition of the array covariance matrix, root-finding method is used to obtain the optimal Lagrange multiplier. Then, the diagonal loading-like robust weight vector is achieved. ne implementation efficiency is greatly improved since the main computational burden is the eigendecomposition operator. Numerical results show that the performance of the proposed method is nearly identical to the robust Capon beamforming.

关键词： Robustness array signal processing Loading signal to noise ratio arrays Interference Algorithm design and analysis

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COMPRESSED SENSING OF GAUSS-MARKOV RANDOM FIELDS WITH WIRELESS sensor NETWORKS

COMPRESSED SENSING OF GAUSS-MARKOV RANDOM FIELDS WITH WIRELE...

引用

ieee sensor array and multichannel signal processing workshop

作者： Oka, Anand Lampe, Lutz Univ British Columbia Dept Elect & Comp Engn Vancouver BC V5Z 1M9 Canada

ISBN: (纸本)9781424422401

We propose a scalable and energy efficient method for reconstructing a 'sparse' Gauss-Markov random field that is observed by an array of sensors and described over wireless channels to a fusion center. The encoder is universal, i.e. invariant to the statistical model of the source and the channel, and is based on compressed sensing. The reconstruction algorithms exploit the a-priori statistical information about the field and the channel at the fusion center to yield a performance comparable to information theoretic bounds. Furthermore, by putting stringent constraints on the sensing matrix we avoid (or even eliminate) inter-sensor communication while suffering negligible degradation in performance.

关键词： Compressed sensing

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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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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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