Improving the Robustness of the Dominant Mode Rejection Beamformer With Median Filtering

Abraham’s and Owsley’s dominant mode rejection (DMR) beamformer modifies Capon’s minimum variance distortionless response beamformer to force suitable constraints in the covariance matrix estimation process to reduce degrees of freedom. DMR estimates the ensemble cov...

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Main Authors: David Campos Anchieta, John R. Buck
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9950235/
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author David Campos Anchieta
John R. Buck
author_facet David Campos Anchieta
John R. Buck
author_sort David Campos Anchieta
collection DOAJ
description Abraham’s and Owsley’s dominant mode rejection (DMR) beamformer modifies Capon’s minimum variance distortionless response beamformer to force suitable constraints in the covariance matrix estimation process to reduce degrees of freedom. DMR estimates the ensemble covariance matrix (ECM) from a low-rank sample covariance matrix (SCM) by replacing the eigenvalues of the noise subspace with the sample mean of those same eigenvalues. This estimated noise power is negatively biased when the dominant subspace dimension is overestimated, which is common in practical implementations of the DMR. The proposed median DMR exploits the Marchenko-Pastur distribution to estimate the noise power from the median of the SCM eigenvalues. Simulations found that the median estimator was more robust to overestimating the dominant subspace dimension, exhibiting a lower mean squared error than the mean estimator. Simulations also found that the median DMR improves the white noise gain (WNG) when compared to the standard DMR in snapshot deficient scenarios with overestimated interferer subspace dimension. Higher WNG implies increased robustness to array perturbations. This work compares the median DMR to standard DMR in simulations with perturbed array element phase responses in a scenario with two interferers and background white noise. The median DMR preserved deeper notches than standard DMR in this scenario, increasing the output signal-to-noise ratio by roughly 1 dB.
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spelling doaj.art-dcda7bfb1c264b3ca029292b67f63f9f2022-12-22T04:18:37ZengIEEEIEEE Access2169-35362022-01-011012014612015410.1109/ACCESS.2022.32219549950235Improving the Robustness of the Dominant Mode Rejection Beamformer With Median FilteringDavid Campos Anchieta0https://orcid.org/0000-0003-2653-7247John R. Buck1https://orcid.org/0000-0002-2809-8164Department of Electrical and Computer Engineering, University of Massachusetts Dartmouth, Dartmouth, MA, USADepartment of Electrical and Computer Engineering, University of Massachusetts Dartmouth, Dartmouth, MA, USAAbraham’s and Owsley’s dominant mode rejection (DMR) beamformer modifies Capon’s minimum variance distortionless response beamformer to force suitable constraints in the covariance matrix estimation process to reduce degrees of freedom. DMR estimates the ensemble covariance matrix (ECM) from a low-rank sample covariance matrix (SCM) by replacing the eigenvalues of the noise subspace with the sample mean of those same eigenvalues. This estimated noise power is negatively biased when the dominant subspace dimension is overestimated, which is common in practical implementations of the DMR. The proposed median DMR exploits the Marchenko-Pastur distribution to estimate the noise power from the median of the SCM eigenvalues. Simulations found that the median estimator was more robust to overestimating the dominant subspace dimension, exhibiting a lower mean squared error than the mean estimator. Simulations also found that the median DMR improves the white noise gain (WNG) when compared to the standard DMR in snapshot deficient scenarios with overestimated interferer subspace dimension. Higher WNG implies increased robustness to array perturbations. This work compares the median DMR to standard DMR in simulations with perturbed array element phase responses in a scenario with two interferers and background white noise. The median DMR preserved deeper notches than standard DMR in this scenario, increasing the output signal-to-noise ratio by roughly 1 dB.https://ieeexplore.ieee.org/document/9950235/Adaptive beamformer (ABF)dominant mode rejection (DMR)median filtering random matrix theory (RMT)sample covariance matrix (SCM)
spellingShingle David Campos Anchieta
John R. Buck
Improving the Robustness of the Dominant Mode Rejection Beamformer With Median Filtering
IEEE Access
Adaptive beamformer (ABF)
dominant mode rejection (DMR)
median filtering random matrix theory (RMT)
sample covariance matrix (SCM)
title Improving the Robustness of the Dominant Mode Rejection Beamformer With Median Filtering
title_full Improving the Robustness of the Dominant Mode Rejection Beamformer With Median Filtering
title_fullStr Improving the Robustness of the Dominant Mode Rejection Beamformer With Median Filtering
title_full_unstemmed Improving the Robustness of the Dominant Mode Rejection Beamformer With Median Filtering
title_short Improving the Robustness of the Dominant Mode Rejection Beamformer With Median Filtering
title_sort improving the robustness of the dominant mode rejection beamformer with median filtering
topic Adaptive beamformer (ABF)
dominant mode rejection (DMR)
median filtering random matrix theory (RMT)
sample covariance matrix (SCM)
url https://ieeexplore.ieee.org/document/9950235/
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