Modeling dominant mode rejection beamformer notch depth using random matrix theory

Abstract

Any practical tracking algorithm must mitigate the effect of both nonstationary environments and loud interfering sources. The dominant mode rejection (DMR) adaptive beamformer (ABF) is often used to address these challenges. The DMR beamformer periodically updates the sample covariance matrix (SCM) employed to compute the ABF weights. A tension arises between the large number of snapshots required for each SCM update to null loud interferers effectively and the small number of snapshots required for each SCM update to track nonstationarities. In practice, observed notch depths for DMR fall dramatically short of those predicted theoretically from ensemble statistics. This talk bridges the gap between theoretical and observed performance by presenting a simple linear asymptotic model for the DMR notch depth derived from random matrix theory results on the accuracy of the SCM eigenvectors. The model predicts the mean DMR notch depth as a function of the number of snapshots given the interferer-to-noise rati...