Improving the dominant mode rejection beamformer with median filtering

Abstract

Adaptive beamformers (ABFs) outperform conventional beamformers in detecting weak sources while attenuating background noise and strong interferers. Changing environments limit the number of snapshots that an ABF can average to estimate the sample covariance matrix (SCM). The dominant mode rejection (DMR) beamformer [Abraham and Owsley, Oceans (1990)] overcomes rank-deficient SCMs by imposing a structured covariance matrix which replaces the noise subspace eigenvalues by their average. However, the DMR beamformer often overestimates the dominant subspace dimension to avoid interferers contaminating the beamformer output. Overestimating the dominant subspace dimension introduces a bias to the background noise power estimate. This bias impairs the array gain and output signal to interferer and noise ratio (SINR). This talk proposes a modification to the DMR, replacing the average of the noise eigenvalues with an estimate of background noise power derived from the median of all eigenvalues. Simulations demonstrated that this new median-DMR beamformer improves the output SINR by up to 0.9dB compared to the standard DMR beamformer in scenarios which conservatively overestimated the dominant subspace dimension and suffered from a rank-deficient SCM. [Research supported by ONR 321US.]