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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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Distribution-free detection under complex elliptically symmetric clutter distribution

Distribution-free detection under complex elliptically symme...

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sensor array and multichannel signal processing workshop

作者： Esa Ollila David E. Tyler Department of Signal Processing and Acoustics Aalto University Aalto Finland Department of Statistics Rutgers University Piscataway NJ USA

We study the constant false alarm rate matched subspace detector (CFAR MSD) of a signal observed under additive noise following a complex elliptically symmetric (CES) distribution which include the class of compound-Gaussian (CG) distributions as special cases. We prove that the detector is distribution-free under the null (signal free) hypothesis and derive simple expressions for the probability of detection assuming CG-distributed clutter. The derived theoretical results are then illustrated by contrasting them with the performance of a practical adaptive detector which estimates the shape matrix (normalized clutter covariance matrix) from the set of secondary data using complex Tyler's M-estimator of scatter under small sample lengths. We also prove that the complex Tyler's M-estimator is the maximum likelihood estimator (MLE) of the shape matrix under the assumption that the secondary data are independent random vectors from a possibly different CES distributions but which share the same shape matrix parameter.

关键词： Detectors Clutter Shape Covariance matrix signal processing Vectors Maximum likelihood estimation

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sensor network distributed computation for Direct Position Determination

Sensor network distributed computation for Direct Position D...

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sensor array and multichannel signal processing workshop

作者： Mohammad Pourhomayoun Mark L. Fowler Department of Electrical and Computer Engineering Binghamton University Binghamton NY USA

Classical localization systems based on TDOA/FDOA use a two-stage estimation approach. In the first stage, pairs of sensors share data to estimate TDOA/FDOA. Then, the extracted TDOA/FDOA measurements are used to estimate the emitter location. In some recently published methods, an optimal single-stage approach named Direct Position Determination (DPD) has been proposed to improve the position estimation accuracy. However, unlike the classical two-stage method where the TDOA/FDOA estimation can be distributed across all sensors, DPD processes all the received signals together at a single sensor node. However, when sensors have limited computational capabilities it is desirable to distribute the computation across all sensors. Furthermore, concentrating all the processing into a single node makes the location system less robust to the loss of sensors. In this paper, we develop a distributed localization method with the goal of reducing the computational load on each sensor and increasing the reliability of the system.

关键词： Eigenvalues and eigenfunctions Estimation Computer aided manufacturing Approximation methods Data compression Delay Doppler effect

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Wideband sound reproduction in a 2D multi-zone system using a combined two-stage Lasso-LS algorithm

Wideband sound reproduction in a 2D multi-zone system using ...

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sensor array and multichannel signal processing workshop

作者： Nasim Radmanesh Ian S Burnett School of Electrical and Computer Engineering RMIT University Melbourne VIC Australia

This paper addresses the provision of personal soundfields (zones) to multiple listeners in a space for which there are several fixed, virtual sources. For such multizone systems, optimization of speaker positions and weightings is important to reduce the number of active speakers. In this paper a two stage pressure matching optimization is proposed for wideband sound sources (such as speech signals). In the first stage, the least-absolute shrinkage and selection operator (Lasso) is used to select the speakers' positions for all sources and frequencies. A second stage then optimizes reproduction using all selected speakers on the basis of regularized least-squares (LS) or Lasso algorithm. The performance of these new, two-stage approaches is investigated for different speaker numbers, frequency range and reproduction angles. Results show that a limited arc of speakers using a two-stage optimization can give up to 38dB improvement in zone normalized squared error over a single-stage LS optimization.

关键词： Wideband arrays Speech Optimization Vectors Speech processing Manganese

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Source counting in real-time sound source localization using a circular microphone array

Source counting in real-time sound source localization using...

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sensor array and multichannel signal processing workshop

作者： Despoina Pavlidi Anthony Griffin Matthieu Puigt Athanasios Mouchtaris Department of Computer Science University of Crete Heraklion Crete Greece FORTH-ICS Heraklion Crete Greece

Recently, we proposed an approach inspired by Sparse Component Analysis for real-time localization of multiple sound sources using a circular microphone array. The method was based on identifying time-frequency zones where only one source is active, reducing the problem to single-source localization for these zones. A histogram of estimated Directions of Arrival (DOAs) was formed and then processed to obtain improved DOA estimates, assuming that the number of sources was known. In this paper, we extend our previous work by proposing three different methods for counting the number of sources by looking for prominent peaks in the derived histogram based on: (a) performing a peak search, (b) processing an LPC-smoothed version of the histogram, (c) employing a matching pursuit-based approach. The third approach is shown to perform very accurately in simulated reverberant conditions and additive noise, and its computational requirements are very small.

关键词： Estimation Histograms Direction of arrival estimation arrays Microphones Matching pursuit algorithms Real time systems

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Fusion of multiple microphone arrays for blind source separation and localization

Fusion of multiple microphone arrays for blind source separa...

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sensor array and multichannel signal processing workshop

作者： Tao Wu Longji Sun Qi Cheng Pramod K. Varshney School of Electrical and Computer Engineering Oklahoma State University Stillwater OK USA Department of Electrical Engineering and Computer Science Syracuse University Syracuse NY USA

Motivated by potential applications such as the use of multiple robots to collaboratively localize multiple objects/events of interest through audio cues, we propose a novel solution for blind source separation (BSS) and localization using multiple microphone arrays. Unlike the existing state-of-the-art where direction of arrivals (DOAs) and locations are estimated sequentially, the proposed approach jointly estimates both by solving a constrained optimization problem, in which the dependence of the separated signals is minimized. By adopting the alternating direction method of multipliers (ADMM), the problem can be solved in a distributed way in which only the local DOA estimates are communicated among arrays in each iteration. Because the augmented Lagrangian used in ADMM naturally forces the estimates to approach true values by adding penalties on non-optimal ones, the critical scaling and permutation problems in BSS are solved simultaneously. Simulation results further demonstrate that the estimation accuracy can be significantly improved using the proposed method.

关键词： arrays Microphones Direction of arrival estimation Source separation Frequency domain analysis signal processing algorithms Optimization

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SUAS for 8-channel GMTI radar using alternative high performance non-SMI STAP algorithm

SUAS for 8-channel GMTI radar using alternative high perform...

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sensor array and multichannel signal processing workshop

作者： S. Lawrence Sensors and Electromagnetic Applications Laboratory (SEAL) Georgia Tech Research Institute (GTRI) Smyrna 30080 USA

GTRI is developing an 8-channel X-band experimental radar for a small unmanned aircraft system (SUAS) for adaptive multi-channel, MIMO, and waveform diversity test bed studies. New adaptive algorithms, one of which is covered in this paper, are also part of the test bed. Estimation of the statistical covariance matrix forms a central role in radar detection and adaptive beamforming algorithm. For example, the optimal (adaptive) linear combiner (beamformer) weights for a radar sensor array are expressed in terms of the inverse of the multi-channel (MC) covariance matrix for MIMO problems. Rather than form an estimate of the covariance matrix directly from the available data and inverting (sample matrix inversion [SMI]), an alternative direct estimate of the inverse may be obtained by forming parametric MC linear prediction estimates and then expressing the inverse in terms of these parametric MC estimates. The resulting parametric inverse estimate will be more accurate than inverting the estimate of the covariance matrix, leading to greatly improved detection performance over conventional methods of covariance estimation and inversion. This paper reveals the structure of the inverse of the covariance matrix for one parametric technique. The inverse structure involves products of triangular block MC Toeplitz matrices, which leads to a fast computational solution. Performance improvements over classical sample covariance matrix estimation are illustrated.

关键词： Covariance matrix Vectors Radar signal processing algorithms arrays Interference Jamming

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MULTI-sensor BEAMSTEERING BASED ON THE ASYMPTOTIC LIKELIHOOD FOR COLORED signalS

MULTI-SENSOR BEAMSTEERING BASED ON THE ASYMPTOTIC LIKELIHOOD...

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ieee Statistical signal processing workshop (SSP)

作者： Ramirez, David Via, Javier Santamaria, Ignacio Scharf, Louis Univ Cantabria Commun Engn Dept E-39005 Santander Spain Colorado State Univ Dept Elect & Comp Engn Ft Collins CO 80523 USA

ISBN: (纸本)9781457705700

In this work, we derive a maximum likelihood formula for beamsteering in a multi-sensor array. The novelty of the work is that the impinging signal and noises are wide sense stationary (WSS) time series with unknown power spectral densities, unlike in previous work that typically considers white signals. Our approach naturally provides a way of fusing frequency-dependent information to obtain a broadband beamformer. In order to obtain the compressed likelihood, it is necessary to find the maximum likelihood estimates of the unknown parameters. However, this problem turns out to be an ML estimation of a block-Toeplitz matrix, which does not have a closed-form solution. To overcome this problem, we derive the asymptotic likelihood, which is given in the frequency domain. Finally, some simulation results are presented to illustrate the performance of the proposed technique. In these simulations, it is shown that our approach presents the best results.

关键词： array processing maximum likelihood (ML) estimation compressed likelihood bearing response pattern

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SAM 2008 - 5th ieee sensor array and multichannel signal processing workshop

SAM 2008 - 5th IEEE Sensor Array and Multichannel Signal Pro...

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SAM 2008 - 5th ieee sensor array and multichannel signal processing workshop

ISBN: (纸本)9781424422418

The proceedings contain 113 papers. The topics discussed include: a comparative study of blind channel identification methods for alamounti coded systems over indoor transmissions at 2.4 GHZ;rapid prototyping of a cost effective and flexible 4×4 MIMO testbed;a low complexity decoding scheme for quasi-orthogonal space-time block coding;experimental investigation of polarization diversity;deterministic MIMO channel order estimation based on canonical correlation analysis;spectrum sharing in wireless networks: a QoS-aware secondary multicast approach with worst user performance optimization;optimality of multichannel beamforming for spatially correlated multiple-antenna Rayleigh fading channels with channel covariance information at transmitter;and comparison of the CAF-DF and SAGE algorithms in multiple channel parameter estimation.

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Space-time compressive sampling array

Space-time compressive sampling array

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ieee sensor array and multichannel signal processing workshop

作者： Wang, Ying Leus, Geert Philips Research Europe High Tech Campus 5656 AE Eindhoven Netherlands Department of EEMCS Delft University of Technology Netherlands

ISBN: (纸本)9781424489770

We propose a space-time compressive sampling (STCS) array architecture by exploiting the sparsity in the angle and frequency domain. Two Doppler-DoA estimation methods are designed to efficiently reconstruct the two-dimensional (2D) sparse signal. The STCS array together with the 2D reconstruction methods allow for accurate yet low-power location and speed estimation of moving targets. The proposed methods are not only tested by simulations but also using an experimental ultrasonic sensor array setup. © 2010 ieee.

关键词： Compressed sensing

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Closed-form expression of the Weiss-Weinstein bound for 3D source localization: The conditional case

Closed-form expression of the Weiss-Weinstein bound for 3D s...

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ieee sensor array and multichannel signal processing workshop

作者： Vu, Dinh Thang Renaux, Alexandre Boyer, Rémy Marcos, Sylvie Université Paris-Sud XI CNRS Rue Joliot Curie 91192 Gif-sur-Yvette Cedex France

ISBN: (纸本)9781424489770

In array processing, lower bounds are used as a benchmark to evaluate the ultimate performance of estimators. Among these bounds, the Weiss-Weinstein bound (WWB) is known as the tightest bound of Weiss-Weinstein family, and is able to predict the threshold effect of estimator's mean square error (MSE) at low signal-to-noise ratio (SNR) and/or at low number of snapshots. In this paper, we derive a closedform expression of the WWB for 3D source localization using an arbitrary planar antenna array in the case of a deterministic known signal. The presented results are shown to be useful for system design such as array geometry optimization. © 2010 ieee.

关键词： array processing

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