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Polynomial rooting-based direction finding for arbitrary array configurations

Polynomial rooting-based direction finding for arbitrary arr...

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sensor array and multichannel signal processing workshop

作者： Mario Costa Andreas Richter Fabio Belloni Visa Koivunen Department of Signal Processing and Acoustics SMARAD CoE Helsinki University of Technology Finland

In this paper, we propose angle of arrival estimation algorithms for arbitrary array geometries. The proposed methods extend the root-WSF and modified variable projection (MVP) algorithms to arbitrary array configurations. This is accomplished by employing the recently introduced manifold separation technique (MST), which stems from wavefield modelling. 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). The performance is illustrated using calibration data from two real-world arrays.

关键词： arrays Calibration signal processing algorithms Direction of arrival estimation Manifolds Noise measurement Cost function

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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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sensor array and multichannel signal processing workshop

作者： Richard J. Kozick Brian M. Sadler Bucknell University Lewisburg PA USA Army Research Laboratory Adelphi MD USA

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： sensors Magnetic sensors Target tracking Magnetic field measurement Magnetometers Magnetic fields Magnetostatics

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Experiments in radio location estimation using an airbone array

Experiments in radio location estimation using an airbone ar...

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sensor array and multichannel signal processing workshop

作者： Hiroyuki Tsuji Jimeng Zheng Mostafa Kaveh New Generation Wireless Communications Research Center National Institute of Communications and Information Technologies Yokosuka Japan Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN USA

The problem of interest is the determination of the location of a licensed or unlicensed source of radio transmission from air. Accordingly, an experiment was conducted in 2008 involving a two-dimensional antenna array mounted on a Zeppelin NT airship. This paper gives an overview of the experiment and some results obtained to date by post-processing of the collected data. A combination of angles of arrival estimation of radio sources and the proposed attitude correction technique gives an accurate location estimation of a radio source located in an urban area. We also verified the feasibility of localization system using on-board array antennas through the experiment.

关键词： arrays Antenna arrays Estimation Radio transmitters Robustness Receiving antennas Accuracy

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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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Optimizing the performance of the partial adaptive concentric ring array in the presence of prior knowledge

Optimizing the performance of the partial adaptive concentri...

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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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NON-MYOPIC sensor SCHEDULING FOR A CENTRALIZED sensor NETWORK

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

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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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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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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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