Abstract
Adaptive beamformers (ABFs) suppress interferers by placing notches in the beampattern at interferer directions. This suppression improves the detection of weaker signals of interest even in the presence of strong interferers. The magnitude of the notch depth (ND) is an important parameter governing the adaptive gain obtained from using ABFs over conventional beamforming in the presence of interferers. This research derives models for the mean ND of a diagonally loaded minimum variance distortionless response (MVDR) beamformer for a single interferer case. The model describes the mean ND as a function of the 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 exploits random matrix theory (RMT) results on the behavior of the eigenvectors of the spiked covariance matrix. The RMT based ND model predictions are in close agreement with simulation results over a range of INR values and number of snapshots.