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

Random Matrix Theory Model for Mean Notch Depth of the Diagonally Loaded MVDR Beamformer for a Single Interferer Case