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Single-snapshot beamformer performance using large arrays with faulty sensors

Single-snapshot beamformer performance using large arrays wi...

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sensor array and multichannel signal processing workshop

作者： O.R. Kazanci J.L. Krolik Department of Electrical & Computer Engineering Duke University Durham NC USA

Large sensor arrays are prone to element failures which can seriously degrade their performance. In the event of sensor failures, conventional non-adaptive re-design of array weights for the resulting non-uniform or even sparse array often leads to undesirable mainlobe-versus-sidelobe level trade-offs. Alternatively, adaptive array processing techniques can be employed but for large arrays it can be compromised by lack of snapshot support in dynamic environments. Recently, an approach called beamspace adaptive channel compensation (BACC) was introduced based on the idea of reconstructing the filled array data from the receive beams in which the sidelobe leakage of strong interferers from adjacent beams is minimized. In this paper, BACC and various alternatives are evaluated using actual radar data with both injected and real scatterers. Results of four methods, BACC, MV adaptive beamforming using an augmented Toeplitz covariance matrix (Toeplitz-MV), principal solution beamforming, and conventional beamforming in the presence of faulty sensors are compared in terms of ROC performance, probability of detection versus SINR, and the unmasking of targets in estimated range-Doppler spectra.

关键词： sensor arrays array signal processing Adaptive arrays Covariance matrix Radar scattering Interference Data models Degradation Radar detection Fault detection

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Study of the human eye working principle: an impressive high angular resolution system with simple array detectors

Study of the human eye working principle: an impressive high...

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sensor array and multichannel signal processing workshop

作者： D. Betancourt C. del Rio Antenna Group Public University of Navarra Pamplona Spain

In this paper the working principle of the human eye is studied since uses very simple detectors, cones and rods, to provide an impressive high angular resolution without using any kind of scanning technique. Usually the resolution capability of the human eyes has been attributed to the complexity of the brain, but it is quite difficult to understand that something, specially small details, that it has not been seen by the eyes could be generated by the brain. Finally, it will be proved that with the actual technology and design philosophy of array antenna systems, the maximum achievable angular resolution of a realistic antenna array system is seriously limited and it is equivalent to the one that it could have an insect with compound eyes. It is clear the human eye is able to overcome this limit, and the idea is to learn how to introduce this to the design procedure of antenna array systems.

关键词： Humans sensor arrays Detectors Antenna arrays Directive antennas Lenses Eyes Insects Feeds Millimeter wave technology

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Taylor-series Technique for Source Localization using AoAs in the Presence of sensor Location Errors

Taylor-series Technique for Source Localization using AoAs i...

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sensor array and multichannel signal processing workshop

作者： Xiaoning Lu K.C. Ho Department of Electrical and Computer Engineering University of Missouri Columbia Columbia MO USA

Source location accuracy can be dramatically degraded by the inaccuracy in the sensor positions. The sensor position errors need to be taken into account in order to improve the source location estimate. This paper presents an algorithm based on the Taylor-series technique to estimate the location of an emitting source in the presence of random sensor position errors, where the data measurements are angle-of-arrivals (AoAs). The proposed algorithm jointly estimates the source and sensor positions iteratively by using Taylor-series linearization. Simulation results indicate that the proposed method approaches the CRLB accuracy for both near-field and far-field sources

关键词： Position measurement Direction of arrival estimation Computer errors sensor arrays Degradation Iterative algorithms Iterative methods Goniometers Noise shaping Shape

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Threshold Optimization for Distributed Detection using Particle Filtering Methods

Threshold Optimization for Distributed Detection using Parti...

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sensor array and multichannel signal processing workshop

作者： I. Kyriakides D. Cochran Department of Electrical Engineering Arizona State University Tempe AZ USA

Local processing on the nodes of a distributed sensing and processing system has the benefits of reducing the data volume transferred from the nodes to the fusion center, reducing both transmission power requirements and the computational burden on the fusion center. The individual nodes obtain measurements from the environment and transmit a quantized detection statistic to the fusion center. Quantization threshold levels need to be found for each sensor that maximize the performance of the system. We propose a global optimization method, the particle filtering optimization method, that uses particle filtering to propagate the values of the thresholds of a distributed detection system to sensor threshold values that are optimal with respect to some measure of system performance. We demonstrate, through simulations, the effectiveness of the particle filtering optimization method in finding the threshold of each of the sensors used in detection scenario

关键词： Optimization methods Filtering sensor systems Quantization sensor phenomena and characterization Bayesian methods Distributed computing Statistical distributions Particle measurements System performance

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Robust adaptive beamforming using variable loading

Robust adaptive beamforming using variable loading

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sensor array and multichannel signal processing workshop

作者： Jing Gu P.J. Wolfe Division of Engineering and Applied Sciences Harvard University Cambridge MA USA

It is well known that the performance of adaptive beamformers may degrade in the presence of steering errors or lack of training data. Diagonal loading of the sample covariance matrix is a popular technique applied to the minimum variance/minimum power distortionless response beamformer to increase robustness of the array system. However, this technique induces a trade-off between sidelobe suppression and the ability of the beamformer to adaptively cancel interference and reduce noise. Here we propose a new algorithm employing variable loading of the sample covariance matrix eigenvalues, which we show along with the standard diagonal loading technique to be a special case of a more general rational approximation problem. We also present an online implementation having computational complexity comparable to conventional methods, which in turn allows the weight vector to be efficiently updated. Simulation results indicate that in comparison with standard diagonal loading techniques, the proposed method exhibits enhanced robustness and performance.

关键词： array signal processing Covariance matrix Degradation Training data Noise robustness Interference cancellation Interference suppression Noise reduction Noise cancellation Approximation algorithms

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Real-Time Implementation of a Distributed Voice Activity Detector

Real-Time Implementation of a Distributed Voice Activity Det...

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sensor array and multichannel signal processing workshop

作者： V. Berisha Homin Kwon A. Spanias Department of Electrical Engineering Arizona State University Tempe AZ USA

Wireless sensor networks have been applied successfully in real-time distributed and collaborative sensing. In these situations, each sensor is responsible for extracting pertinent information from the surrounding environment and transmitting it to other sensors and/or to the main processing station. This is done while operating under several constraints, such as low computational capabilities, limited arithmetic precision, and the need to conserve power. In this paper, we present a low-complexity voice activity detector and a gender classifier for implementation on the Crossbow sensor motes. In addition, a decision fusion algorithm that resides at the base station is also implemented. A series of experiments that characterize the performance of the algorithms under varying conditions and in different environments are presented and several of the challenges we faced in developing this real-time implementation are discussed

关键词： Detectors Wireless sensor networks Acoustic sensors Collaboration sensor phenomena and characterization Base stations Data mining Arithmetic Hardware Noise level

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Analysis of Prior-Subspace Estimation Schemes

Analysis of Prior-Subspace Estimation Schemes

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sensor array and multichannel signal processing workshop

作者： G. Bouleux R. Boyer Laboratoire des Signaux et Système (LSS) CNRS-Université Paris-Sud France

In the context of the direction-of-arrival (DOA) estimation problem, we can sometimes assume that we have a priori knowledge of a subset of the DOA for several sources. In that situation, some works have proposed to tacking into account of this knowledge to improve the localization of the unknown sources. The key idea is based on an orthogonal deflation of the signal subspace. In a companion paper, we have derived and analyzed the CRB associated to this model and we have shown that the prior-knowledge of a subset of the DOA is beneficial only for coherent or highly correlated sources associated to closely-spaced DOA. In particular, for uncorrelated sources with closely-spaced DOA, the orthogonal deflation cannot help the estimation of the unknown DOA. Finally, we suggest that a possible solution could be to use oblique projectors. In this paper, we follow this line and we propose two algorithms based on the MinNorm principle

关键词： Direction of arrival estimation sensor arrays Narrowband Maximum likelihood estimation Multiple signal classification Uninterruptible power systems Radar Computational efficiency MIMO

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Unitary Cyclic MUSIC for Direction Finding in GPS Receivers

Unitary Cyclic MUSIC for Direction Finding in GPS Receivers

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sensor array and multichannel signal processing workshop

作者： M. Sahmoudi M.G. Amin Wireless Communications and Positioning Lab Center for Advanced Communications Villanova University Villanova PA USA

Several anti-jam techniques for GPS receivers rely on the direction-of-arrival (DOA) of the satellite signals. These angles change with moving GPS receiver platforms and, as such, they should be constantly estimated for the purpose of jammer nulling and beamforming. In this paper, we propose a new approach for directional finding for multi-antenna GPS receivers. This approach is based on a new unitary-cyclic subspace method, which utilizes the modulation and repetition properties of the GPS code to discriminate against jammer signals. We apply the forward-backward spatial smoothing technique to the cyclic array covariance matrix to decorrelate the direct path from multipath, so nulling of the undesired signals can occur. A unitary formulation is considered for real-valued eigendecomposition which aims at reducing the payload in the GPS receivers. Simulations are provided showing that the proposed approach has better performance than existing high-resolution methods that neither apply cyclostationary or unitary transformations for DOA estimation

关键词： Multiple signal classification Global Positioning System Direction of arrival estimation Jamming Satellites array signal processing Modulation coding Smoothing methods Covariance matrix Decorrelation

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Optimal Adaptive array and Angle Tracking for Multiple Targets - A Re-examination of Optimal array processing

Optimal Adaptive Array and Angle Tracking for Multiple Targe...

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sensor array and multichannel signal processing workshop

作者： Huaijin Gu Louisiana Tech University USA

ISBN: (纸本)1424403081

The optimal array for detecting the signal from a desired direction but contaminated by receiver noise and strong interference from different sources with unknown arriving-angles is re-examined. The well-known optimal array is obtained by inverting the covariance matrix of interference and noise to maximize the signal to interference and noise ratio (SINR). In practice, the covariance matrix is unknown and has to be estimated by a sample covariance matrix. The optimal array is thus estimated by inverting the sample covariance matrix. This procedure has been employed in optimal array research without challenge. However, it is shown in this paper that the estimated optimal array fails to yield the highest SINR in the case of unknown arriving-angles. Instead the highest SINR can be achieved by optimally estimating the arriving-angles of interference followed by a constrained matched filter, which maximizes the signal to noise ratio subject to canceling the interference from the estimated arriving-angles. In order to reduce the computational burden, an angle-tracking system for multiple targets is adopted to achieve the optimal estimation of arriving-angles. The resulting system of angle-tracking adaptive array offers the highest SINR at a computational burden only on 2 the order of N middot M 2 multiplications within a radar range-cellDeltatau, rather than N 3 multiplications in the well-known but questionable estimated optimal array. Here N is the number of sensors in the array and M the number of interference sources. Typically, N = 1,000 in a planar radar array, M = 2-10 and Deltatau = 1 mus. Numerical simulations confirm the theoretical results

关键词： Adaptive arrays Target tracking array signal processing signal to noise ratio Interference Covariance matrix sensor arrays Radar tracking signal detection Yield estimation

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On Proposal Functions for Cost-Reference Particle Filtering

On Proposal Functions for Cost-Reference Particle Filtering

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sensor array and multichannel signal processing workshop

作者： M.F. Bugallo M. Vemula P.M. Djuric Department of Electrical and Computer Engineering Stony Brook University Stony Brook NY USA

Standard particle filtering (SPF) schemes rely on the availability of probability distributions of the state and observation noises involved in the dynamic state space model. Cost reference particle filtering (CRPF) techniques have proven to be a viable and robust alternative in situations when the probability distributions of these noise processes are unknown. In this paper, we propose two new CRPF methods which use different proposal functions from the one of the original CRPF method. The proposed algorithms are applied to target tracking in a wireless sensor network. The performance of the proposed methods is demonstrated by computer simulations

关键词： Proposals Filtering Probability distribution Costs State-space methods Wireless sensor networks Computer simulation Decision support systems Noise measurement Time measurement

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