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A BLIND array CALIBRATION ALGORITHM USING A MOVING SOURCE

A BLIND ARRAY CALIBRATION ALGORITHM USING A MOVING SOURCE

引用

ieee sensor array and multichannel signal processing workshop

作者： Efstathopoulos, Georgios Manikas, Athanassios Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn London SW7 2AZ England

ISBN: (纸本)9781424422401

This paper is concerned with the problem of antenna array calibration in a non-stationary signal environment. In detail, this study focuses on calibration of the antenna array geometry by employing properties of the differential geometry of the array manifold vector. The proposed algorithm is totally blind, since the only available information to the array system is the received signal emitted by a moving source of unknown characteristics. The only assumption made is that the movement of the source has an angular component and that the position of one sensor with regards to the reference point is known. It is shown through Monte Carlo simulations that this method significantly improves the ability of the array system to estimate characteristics of the moving source, such as its direction of arrival (DOA), in addition to reducing the positioning errors of the array elements.

关键词： Calibration

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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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DETECTION AND LOCALIZATION OF EMITTERS IN THE PRESENCE OF MULTIPATH USING A UNIFORM LINEAR ANTENNA array

DETECTION AND LOCALIZATION OF EMITTERS IN THE PRESENCE OF MU...

引用

ieee sensor array and multichannel signal processing workshop

作者： Sarac, Ugur Harmanci, F. Kerem Akguel, Tayfun Istanbul Tech Univ Dept Elect & Commun Engn TR-80626 Istanbul Turkey Bogazici Univ Dept Elect & Elect Eng TR-80815 Bebek Turkey TUBITAK Natl Res Inst Elect & Cryptol Izmir Kocaeli Turkey TUBITAK Earth & Marine Sci Inst MRC Izmir Kocaeli Turkey

ISBN: (纸本)9781424422401

In this paper, we present a new application of the likelihood ratio test to solve the problem of enumeration and localization of multiple emitters (sources) with a uniform linear antenna array in a multi-path environment. First, spatial smoothing is applied to the antenna array data to decorrelate signals corresponding to each path in the estimated correlation matrix and the number of paths is then counted using an information theoretic criterion. Then, MUSIC is applied with this correlation matrix to estimate the angle of arrival of all possible incident paths. In our signal model, we assume that the correlation between signals from two paths is one if they originate from the same source but zero if they are from different sources. The likelihood ratio test is then applied to each path pair using this correlation model and the angles of arrival estimated by MUSIC in order to find out if the path pair originates from the same emitter, and hence the number of emitters and their angles of arrival are estimated. The proposed algorithm on the likelihood ratio test is compared with the correlation coefficient algorithm based on minimum variance beam-forming with a performance analysis.

关键词： Direction finding likelihood ratio antenna array MUSIC multipath

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

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

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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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POLYNOMIAL ROOTING-BASED DIRECTION FINDING FOR ARBITRARY array CONFIGURATIONS

POLYNOMIAL ROOTING-BASED DIRECTION FINDING FOR ARBITRARY ARR...

引用

ieee sensor array and multichannel signal processing workshop

作者： Costa, Mario Richter, Andreas Belloni, Fabio Koivunen, Visa Helsinki Univ Technol Dept Signal Proc & Acoust SMARAD CoE FIN-02015 Helsinki Finland

ISBN: (纸本)9781424422401

In this paper, we propose angle of arrival estimation algorithms for arbitrary array geometries. The proposed methods extend the root-WSF [1] and Modified Variable Projection (MVP) [2] algorithms to arbitrary array configurations. This is accomplished by employing the recently introduced Manifold Separation Technique (MST) [3], which stems from wavefield modelling [4]. The algorithms process the data in the element-space domain, i.e. no mapping of the data that introduces errors is required. Moreover, coherent sources can be handled. The proposed MST-based MVP algorithm shows a statistical performance close to the Cramer-Rao Lower Bound (CRLB) [5, 6]. The performance is illustrated using calibration data from two real-world arrays.

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LOCALIZATION IN sensor NETWORKS - A MATRIX REGRESSION APPROACH

LOCALIZATION IN SENSOR NETWORKS - A MATRIX REGRESSION APPROA...

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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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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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NON-MYOPIC sensor SCHEDULING FOR A CENTRALIZED sensor NETWORK

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

引用

ieee sensor array and multichannel signal processing workshop

作者： Shah, Himanshu Morrell, Darryl Arizona State Univ Dept Elect Engn Tempe Campus Tempe AZ 85287 USA Arizona State Univ Dept Engn Tempe AZ 85287 USA

ISBN: (纸本)9781424422401

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： Nonlinear programming

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