Document Type


Date of Award


Degree Name

Doctor of Philosophy in Electrical Engineering - (Ph.D.)


Electrical and Computer Engineering

First Advisor

Hongya Ge

Second Advisor

Alexander Haimovich

Third Advisor

Ali Abdi

Fourth Advisor

Yun Q. Shi

Fifth Advisor

Eliza Zoi-Heleni Michalopoulou


In array signal processing over challenging environments, due to the non-stationarity nature of data, it is difficult to obtain enough number of data snapshots to construct an adaptive beamformer (ABF) for detecting weak signal embedded in strong interferences. One type of adaptive method targeting for such applications is the dominant mode rejection (DMR) method, which uses a reshaped eigen-decomposition of sample covariance matrix (SCM) to define a subspace containing the dominant interferers to be rejected, thereby allowing it to detect weak signal in the presence of strong interferences. The DMR weight vector takes a form similar to the adaptive minimum variance distortion-less response (MVDR), except with the SCM being replaced by the DMR-SCM.

This dissertation studies the performance of DMR-ABF by deriving the probability density functions of three important metrics: notch depth (ND), white noise gain (WNG), and signal-to-interference-and-noise ratio (SINR). The analysis contains both single interference case and multiple interference case, using subspace transformation and the random matrix theory (RMT) method for deriving and verifying the analytical results. RMT results are used to approximate the random matrice. Finally, the analytical results are compared with RMT Monte-Carlo based empirical results.



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