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检索条件"任意字段=2008 IEEE Sensor Array and Multichannel Signal Processing Workshop"

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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

2008 ieee sensor array and multichannel signal processing workshop

作者： Y. Noam H. Messer School of Electrical Engineering Tel-Aviv University Israel

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 [1] 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： Maximum likelihood estimation Estimation Bayesian methods Vectors Radio access networks Joints signal processing

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Fast subspace-based source localization methods

Fast subspace-based source localization methods

引用

sensor array and multichannel signal processing workshop

作者： J. Marot C. Fossati S. Bourennane GSM-Institut Fresnel / CNRS-UMR 6133 Ecole Centrale Marseille Marseilles France

Source localization is based on the spectral matrix algebraic properties. Propagator, and Ermolaev-Gershman (EG) noneigenvector algorithms exhibit a low computational load. Propagator is based on spectral matrix partitioning. EG algorithm obtains an approximation of noise subspace using an adjustable power parameter of the spectral matrix and choosing a threshold value. In this paper, we aim at demonstrating the usefulness of QR and LU factorizations of the spectral matrix to improve these methods. Experiments show that the modified propagator and EG algorithms based on factorized spectral matrix lead to better localization results, compared to the existing methods.

关键词： Matrices Noise signal processing algorithms signal to noise ratio Algorithm design and analysis sensors arrays

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Patterned complex-valued matrix derivatives

Patterned complex-valued matrix derivatives

引用

sensor array and multichannel signal processing workshop

作者： Are Hjorungnes Daniel P. Palomar UniK - University Graduate Center University of Oslo Kjeller Norway Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong China

A systematic and simple method is proposed for how to find the derivative of complex-valued matrix functions which depend on matrix arguments that contain patterns. The proposed method is developed by means of the chain rule and it is able to handle both linear and nonlinear patterns. One main issue of the proposed method is to identify a matrix function which depends on independent variables and its domain must have the same dimension as the dimension of the set of patterned matrices. In addition, this function must produce all the matrices within the pattern of interest. Some illustrative examples which are relevant for problems in the area of signal processing for communications are presented.

关键词： Matrices Jacobian matrices Symmetric matrices signal processing Matrix decomposition Presses Vectors

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On the generalization of blind source separation algorithms from instantaneous to convolutive mixtures

On the generalization of blind source separation algorithms ...

引用

sensor array and multichannel signal processing workshop

作者： Tiemin Mei Alfred Mertins Fuliang Yin Institute for Signal Processing University of Lübeck Lubeck Germany School of Information Science and Engineering Shenyang Ligong University Shenyang China School of Electronic and Information Engineering Dalian University of Technology Dalian China

Many convolutive blind source separation (BSS) approaches are generalized from instantaneous BSS methods in either time or frequency domain. In this paper, we establish in a general way the inner relationship between the time-domain instantaneous BSS and the frequency-domain convolutive BSS. From this point of view, the time-domain approaches for instantaneous mixture separation are generalized to those for convolutive mixture separation in the frequency domain. Two examples are given to illustrate the feasibility of the proposed approach.

关键词： Frequency domain analysis Source separation Time domain analysis Blind source separation Finite impulse response filter signal processing algorithms Joints

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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

2008 ieee sensor array and multichannel signal processing workshop

作者： Andrew J. Poulsen Raj Rao Nadakuditi Arthur B. Baggeroer Massachusetts Institute of Technology Cambridge MA USA

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： arrays Gain Noise Sonar equipment White noise Robustness Acoustics

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Buried object localization using direct algorithm

Buried object localization using direct algorithm

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sensor array and multichannel signal processing workshop

作者： Zineb Mehel - Saidi Salah Bourennane Caroline Fossati CRAN-ESSTIN/UHP Vandoeuvre-les-Nancy France Institut Fresnel UMR CNRS 6133 Ecole Centrale Marseille Marseilles France

This study deals with buried object localization using a towed flexible array of sensors. So, the phases of the received signals deviate from the assumed plane wave model by the phase errors which are due to the random distortions of the antenna. In this letter, an objective function is defined and a new source steering vector including the acoustic scattering model at each sensor is proposed. The robust DIRECT (DIviding RECTangles) method is applied to minimize this objective function for estimating the bearings and the ranges of objects together with the phase errors. To decorrelate the received signals and to estimate the coherent signal subspace a fast focusing operator is proposed . The performance of the developed method is investigated on experimental data recorded during underwater acoustic experiments.

关键词： arrays sensors Acoustic distortion Phase distortion sensor arrays Buried object detection Focusing

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NON-MYOPIC sensor SCHEDULING FOR A CENTRALIZED sensor NETWORK

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

引用

2008 ieee sensor array and multichannel signal processing workshop

作者： Himanshu Shah Darryl Morrell Department of Electrical Engineering Arizona State University USA Department of Engineering Arizona State University USA

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： Planning Target tracking Acoustics Acoustic measurements Approximation methods Schedules Scheduling

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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...

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2008 ieee sensor array and multichannel signal processing workshop

作者： Francisco Rubio Xavier Mestre Parc Mediterrani de la Tecnologia Centre Tecnològic de Telecomunicacions de Catalunya Castelldefels Spain

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 Eigenvalues and eigenfunctions arrays Estimation Noise Approximation methods Convergence

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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

sensor array and multichannel signal processing workshop

作者： L. M. Vicente K. C. Ho Department of Electrical and Computer Engineering University of Missouri Columbia Columbia MO USA

The partial adaptive concentric ring array (CRA) has been successfully applied to 3D 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： arrays Interference signal to noise ratio Steady-state array signal processing Power generation Correlation

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A CLOSED-FORM SOLUTION FOR MULTILINEAR PARAFAC DECOMPOSITIONS

A CLOSED-FORM SOLUTION FOR MULTILINEAR PARAFAC DECOMPOSITION...

引用

2008 ieee sensor array and multichannel signal processing workshop

作者： Florian Roemer Martin Haardt Communications Research Laboratory Ilmenau University of Technology Ilmenau Germany

In this paper we study the R-way Parallel Factor Analysis (also referred to as R-way PARAFAC) problem. This branch of multi-way signal processing has received increased attention recently which is due to the versatility of the model as well as the identifiability results demonstrating its superiority to matrix-only (2-way) approaches. In R-way PARAFAC analysis, the goal is to decompose an R-dimensional tensor into a minimal sum of rank-1 terms. So far, there exist sub-optimal closed-form solutions as well as iterative techniques for finding these decompositions. However, the latter often require many iterations to converge. In this contribution we demonstrate that the R-way PARAFAC decomposition can be reduced to a set of simultaneous matrix diagonalization problems. Exploiting the structure of the R-dimensional problem, we obtain several estimates for each of the factors and present a "best matching" scheme to select the best estimate for each factor. By means of computer simulations we compare our closed-form solution to an iterative technique and demonstrate the enhanced robustness in critical scenarios.

关键词： Multidimensional signal processing Parameter estimation array signal processing

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