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Eigenvector-based Signal Subspace Estimation

Bibliographic Details
Title: Eigenvector-based Signal Subspace Estimation
Authors: Quijano, Jorge, Zurk, Lisa
Source: Electrical and Computer Engineering Faculty Publications and Presentations
Publisher Information: PDXScholar
Publication Year: 2015
Collection: Portland State University: PDXScholar
Subject Terms: Signal processing -- Data processing. Algorithms, Beamforming, Acoustics, Dynamics, and Controls, Electrical and Computer Engineering
Description: In this work, we explore the performance of a new algorithm for the estimation of signal and noise subspaces from limited data collected by a large-aperture sonar array. Based on statistical properties of scalar products between deterministic and complex random vectors, the proposed algorithm defines a statistically justified threshold to identify target-related features (i.e., wavefronts) embedded in the sample eigenvectors. This leads to an improved estimator for the signal-bearing eigenspace that can be applied to known eigenspace beamforming processors. It is shown that data projection into the improved subspace allows better detection of closely spaced targets compared to current subspace beamformers, which utilize a subset of the unaltered sample eigenvectors for subspace estimation. In addition, the proposed threshold gives the user control over the maximum number of false detections by the beamformer. Simulated data are used to quantify the performance of the signal subspace estimator according to a normalized metric that compares estimated and true signal subspaces. Improvement on beamforming resolution using the proposed method is illustrated with simulated data corresponding to a horizontal line array, as well as experimental data from the Shallow Water Array Performance experiment.
Document Type: text
Language: unknown
Relation: https://pdxscholar.library.pdx.edu/ece_fac/315
DOI: 10.1121/1.4920188
Availability: https://pdxscholar.library.pdx.edu/ece_fac/315
https://doi.org/10.1121/1.4920188
Rights: © 2015 Acoustical Society of America
Accession Number: edsbas.CD3E4179
Database: BASE
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  Data: Eigenvector-based Signal Subspace Estimation
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  Data: <searchLink fieldCode="AR" term="%22Quijano%2C+Jorge%22">Quijano, Jorge</searchLink><br /><searchLink fieldCode="AR" term="%22Zurk%2C+Lisa%22">Zurk, Lisa</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Signal+processing+--+Data+processing%2E+Algorithms%22">Signal processing -- Data processing. Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Beamforming%22">Beamforming</searchLink><br /><searchLink fieldCode="DE" term="%22Acoustics%22">Acoustics</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamics%22">Dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22and+Controls%22">and Controls</searchLink><br /><searchLink fieldCode="DE" term="%22Electrical+and+Computer+Engineering%22">Electrical and Computer Engineering</searchLink>
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  Data: In this work, we explore the performance of a new algorithm for the estimation of signal and noise subspaces from limited data collected by a large-aperture sonar array. Based on statistical properties of scalar products between deterministic and complex random vectors, the proposed algorithm defines a statistically justified threshold to identify target-related features (i.e., wavefronts) embedded in the sample eigenvectors. This leads to an improved estimator for the signal-bearing eigenspace that can be applied to known eigenspace beamforming processors. It is shown that data projection into the improved subspace allows better detection of closely spaced targets compared to current subspace beamformers, which utilize a subset of the unaltered sample eigenvectors for subspace estimation. In addition, the proposed threshold gives the user control over the maximum number of false detections by the beamformer. Simulated data are used to quantify the performance of the signal subspace estimator according to a normalized metric that compares estimated and true signal subspaces. Improvement on beamforming resolution using the proposed method is illustrated with simulated data corresponding to a horizontal line array, as well as experimental data from the Shallow Water Array Performance experiment.
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