ECE Master of Science Thesis Defense and Research Component of PhD Qualifier Exam by: David Campos Anchieta
When: Friday,
August 13, 2021
1:00 PM
-
3:00 PM
Where: > See description for location
Cost: Free
Description: Topic: Improving The Robustness of The Dominant Mode Rejection Beamforming with Median Filtering
Location: Charlton College of Business Conference Room, CCB-115
Abstract:
Abraham's and Owsley's dominant mode rejection (DMR) beamformer modifies the Capon's minimum variance distortionless response (MVDR) to operate with low-rank sample covariance matrices (SCM). The DMR estimates the ensemble covariance matrix (ECM) from a low-rank SCM by replacing the eigenvalues of the noise subspace with an estimated noise power based on 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 real world implementations of the DMR. The proposed median DMR estimates the noise power from the median of the SCM eigenvalues, based on the Marchenko-Pastur probability distribution. The median estimator showed to be more robust to overestimation of the dominant subspace dimension, exhibiting a lower mean squared error than the mean estimator. Simulations show 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 also 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 1.1 dB.
Note: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
Advisor: Dr. John R. Buck
Committee Members: Dr. Dayalan P. Kasilingam and Dr. Paul J. Gendron, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Honglei Chen, Principal Engineer, The MathWorks, Inc.
*For further information, please contact Dr. John R. Buck via email at jbuck@umassd.edu.
Location: Charlton College of Business Conference Room, CCB-115
Abstract:
Abraham's and Owsley's dominant mode rejection (DMR) beamformer modifies the Capon's minimum variance distortionless response (MVDR) to operate with low-rank sample covariance matrices (SCM). The DMR estimates the ensemble covariance matrix (ECM) from a low-rank SCM by replacing the eigenvalues of the noise subspace with an estimated noise power based on 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 real world implementations of the DMR. The proposed median DMR estimates the noise power from the median of the SCM eigenvalues, based on the Marchenko-Pastur probability distribution. The median estimator showed to be more robust to overestimation of the dominant subspace dimension, exhibiting a lower mean squared error than the mean estimator. Simulations show 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 also 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 1.1 dB.
Note: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
Advisor: Dr. John R. Buck
Committee Members: Dr. Dayalan P. Kasilingam and Dr. Paul J. Gendron, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Honglei Chen, Principal Engineer, The MathWorks, Inc.
*For further information, please contact Dr. John R. Buck via email at jbuck@umassd.edu.
Contact:
ECE: Electrical & Computer Engineering Department 508.999.9164 http://www.umassd.edu/engineering/ece/
Topical Areas: Alumni, General Public, University Community, College of Engineering, Electrical and Computer Engineering