Oral Defense of Doctoral Dissertation

The College of Engineering and Computing, George Mason University

Bachelor of Science, Louisiana State University, 2004

Master of Science, George Mason University, 2010

Wednesday, August 17th, 2:00 - 3:00 PM EST

Room: ENGR 2901

Zoom Meeting Link: https://gmu.zoom.us/j/99674984203

Meeting ID: 996 7498 4203

All are invited to attend.

Dr. Kathleen Wage

Dr. Zhi Tian

Dr. Flavia Colonna

Dr. John Buck

Adaptive beamformers (ABFs) use a spatial covariance matrix that is estimated from data snapshots, i.e., temporal samples from each sensor, to mitigate directional interference and attenuate uncorrelated noise. Thus, ABFs improve
signal-to-interference-plus-noise ratio (SINR), an optimal criteria for many detection and estimation algorithms, over that of a

single sensor and often the conventional beamformer. SINR is a function of white noise gain (WNG), the beamformer's array gain versus spatial white noise, and interference leakage (IL), the interferer power in the beamformer
output. Dominant mode rejection (DMR) is a variant of the classic minimum variance distortionless response (MVDR) algorithm that replaces the

smallest sample covariance matrix (SCM) eigenvalues by their average. By not inverting the smallest eigenvalues, DMR achieves a higher WNG than MVDR. However, DMR still suppresses the loud interferers as the largest eigenvalues
are unmodified, yielding a higher SINR than MVDR.

This defense presents new analytical models of WNG and IL for the DMR ABF that are shown to match the sample mean, computed via Monte Carlo simulations, for a broad range of scenarios including with and without the signal of
interest (SOI) in the training data as well as known and overestimated number of interferers. Accurate predictions for the scenarios of interest required derivation of a new random matrix theory (RMT) spiked covariance model where the number of interferers
grows to infinity jointly with the SCM dimension and number of snapshots. The new RMT spiked covariance model more accurately predicts the SCM eigenspectrum, and hence the ABF metrics, when the number of snapshots is on the same

order or less than the dimension and there are a large number of interferers relative to the SCM dimension. Assuming the SOI is not in the training data and a known number of loud interferers, the analytical models show DMR
achieves an average SINR loss of -3 dB when the number of snapshots is approximately twice the number of interferers, an analogous result to

the Reed-Mallett-Brennan rule for MVDR.