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检索条件"任意字段=4th IEEE Sensor Array and Multichannel Signal Processing Workshop"

共 931 条记录，以下是741-750 订阅

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Selective focusing with numerical technique of time reversal operator decomposition

Selective focusing with numerical technique of time reversal...

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sensor array and multichannel signal processing workshop

作者： Dinh-Quy Nguyen Xuexin Yap Woon-Seng Gan Yong-Kim Chong Digital Signal Processing Laboratory Nanyang Technological University Singapore

Recently, a time reversal operator method that is applied to ultrasonic detection and focusing with arrays of transducers has been proposed. this method is known as the DORT (Decomposition de lpsilaOperateur de Retournement Temporel). the DORT method requires the measurement of the inter-element impulse responses of the propagating medium for all pairs of elements in the array. Time reversal operator (TRO) is deduced from these responses and diagonalized. the eigenvector and eigenvalue of TRO is analyzed to perform selective focusing on different scatterers. We develop the numerical technique of DORT method by using the Greenpsilas function estimation in homogeneous and multiple scattering medium. this technique can set up the feasibility study of DORT method to perform selective focusing on multiple scatterers.

关键词： Focusing Eigenvalues and eigenfunctions Scattering Transducers Numerical models Media Acoustics

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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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ON thE ESTIMATION OF thE COVARIANCE EIGENSPECTRUM OF array SAMPLE OBSERVATIONS

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

引用

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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Localization in sensor networks - A matrix regression approach

Localization in sensor networks - A matrix regression approa...

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sensor array and multichannel signal processing workshop

作者： Paul Honeine Cedric Richard Mehdi Essoloh Hichem Snoussi Institut Charles Delaunay (FRE CNRS 2848)-LM2S Université de technologie de Troyes Troyes France

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： Distance measurement Kernel Optimization Distributed algorithms Wireless sensor networks Network topology Topology

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A CLOSED-FORM SOLUTION FOR MULTILINEAR PARAFAC DECOMPOSITIONS

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

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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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Blind detection of exclusive source activity periods in reverberant acoustic environments

Blind detection of exclusive source activity periods in reve...

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sensor array and multichannel signal processing workshop

作者： Robert M. Nickel Department of Electrical Engineering Bucknell University Lewisburg PA USA

Blind separation and dereverberation of acoustic sources is still considered a very challenging task despite many years of research and the availability of increasingly powerful computation engines. the complexity of the task can be significantly reduced for sources that exhibit sufficiently long exclusive activity periods (EAPs). EAPs are time intervals during which only one source is active and all other sources are inactive (i.e. zero). During EAPs the estimation of the underlying system parameters simplifies from a MIMO type to a SIMO type. the existence of EAPs is not guaranteed for arbitrary signal classes. EAPs occur very frequently, however, in recordings of conversational speech. In this paper we propose a new low complexity method for EAP detection which significantly outperforms earlier approaches.

关键词： Speech Mathematical model Equations Speech processing Acoustics Nickel Transfer functions

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MAP-PF 3D position tracking using multiple sensor array

MAP-PF 3D position tracking using multiple sensor array

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sensor array and multichannel signal processing workshop

作者： Kristine L. Bell Richard Pitre Department of Statistics George Mason University Fairfax VA USA RobTre Research Limited Liability Company Clarksville MD USA

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： Target tracking Direction of arrival estimation arrays Estimation Spectral analysis Maximum likelihood estimation sensor arrays

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MULTIVARIATE SPECTRAL RECONSTRUCTION OF STAP COVARIANCE MATRICES: HERMITIAN "RELAXATION" AND PERFORMANCE ANALYSIS

MULTIVARIATE SPECTRAL RECONSTRUCTION OF STAP COVARIANCE MATR...

引用

2008 ieee sensor array and multichannel signal processing workshop

作者： Yuri I. Abramovich Ben A. Johnson Nicholas K. Spencer ISR Division DSTO Australia Edinburgh Australia ITR-University of South Australia SA Australia Adelaide Research & Innovation Pty Ltd (ARI) DSTO Australia Edinburgh SA Australia

In space-time adaptive processing (STAP) applications, temporally stationary clutter results in a Toeplitz-block clutter covariance matrix. In the reduced-order parametric matched filter STAP technique, this covariance matrix is reconstructed from a small number of estimated parameters, resulting in a much more efficient use of training samples. this paper explores a computationally advantageous "relaxed" maximum entropy (Burg) reconstruction technique which does not restore a strict Toeplitz-block structure, but does preserve the Burg spectrum. Performance of the reconstructed covariance matrix model as a STAP filter is evaluated using the DARPA KASSPER dataset and compared with "proper" Toeplitz-block reconstruction.

关键词： Toeplitz matrices Covariance matrices Autoregressive processes Maximum entropy methods Space-time adaptive processing

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Rapid prototyping of a cost effective and flexible 4×4 mimo testbed

Rapid prototyping of a cost effective and flexible 4×4 mimo...

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sensor array and multichannel signal processing workshop

作者： Douglas Kim Murat Torlak Department of Electrical Engineering University of Texas Dallas Richardson TX USA

In this paper, we present the design of a low cost 4 times 4 multiple in multiple out (MIMO) wireless communications system testbed. Over recent years, an increasing number of research laboratories have begun experimental studies aimed at evaluating the achievable performance gains of MIMO technology as proposed in theory. the complexity of the experimental test platforms employed by these institutions have also increased over the years, supporting greater numbers of antennas while providing even more real-time computational processing power. these tremendous increases in capability, however, come at a very steep price which the vast majority of the wireless research community cannot afford. As an alternative, we present the design of a low cost 4 times 4 MIMO testbed for researchers solely interested in analyzing the physical layer performance of various MIMO technologies. As a demonstration of our testbed capabilities, we also present the results of a direction of arrival (DOA) experiment using the MUSIC algorithm and also cite another body of work on the experimental performance analysis of space, time, polarization diversity schemes which was also completed with use of the MIMO testbed described herein.

关键词： MIMO Transceivers Radio frequency Wireless communication Antennas Receiving antennas Real time systems

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On the Cramér-Rao bound for the constrained and unconstrained complex parameters

On the Cramér-Rao bound for the constrained and unconstrain...

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sensor array and multichannel signal processing workshop

作者： Esa Ollila Visa Koivunen Jan Eriksson Department of Mathematical Sciences University of Oulu Oulu Finland Signal Processing Laboratory Helsinki University of Technology Finland

We derive a complex form of the unconstrained and constrained Cramer-Rao lower bound (CRB) of composite real parameters formed by stacking the real and imaginary part of the complex parameters. the derived complex constrained and unconstrained CRB is easy to calculate and possesses similar structure as in the real parameter case but with the real covariance, Jacobian and the Fisher information matrix replaced by complex matrices with analogous interpretations. the advantage of the complex CRB is that it is oftentimes easier to calculate than its real form. It is highlighted that a statistic that attains a bound on the complex covariance matrix alone do not necessarily attain the CRB since complex covariance matrix does not provide a full second-order description of a complex statistic since also the pseudo-covariance matrix is needed. Our derivations also lead to some new insights and theory that are similar to real CRB theory.

关键词： Covariance matrix Jacobian matrices Symmetric matrices Matrices signal processing Estimation Cramer-Rao bounds

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