  Adaptive beamformers (ABFs) suppress interferers by placing a notch in the
beampattern at the interferer direction. This suppres- sion improves detection
of a weaker signals in the presence of strong interferers. Hence the notch
depth plays a crucial role in determining the adaptive gain obtained from using
ABF over conventional beam- forming. This research derives models for the mean
notch depth of a diagonally loaded MVDR ABF for a single interferer case. The
model describes the mean notch depth as a function of number of snapshots, the
number of sensors in the array, the interferer to noise ratio (INR) level, the
interferer direction and the diagonal loading level. The derivation uses random
matrix theory results on the be- havior of the eigenvectors of sample
covariance matrix. The notch depth predicted by the model is shown to be in
close agreement with simulation results over a range of INRs and snapshots.
