Computationally efficient beamforming on real experimental data

We investigate processing Santa Barbara channel experiment (SBCX) data, using Beamforming algorithms that utilize a computationally efficient covariance matrix (CECM) estimate. The algorithms using the CECM are the Bartlett beamformer (CBF) algorithms, the minimum variance distortionless response (MVDR) and a white noise gain constrained MVDR (WNGC-MVDR) and a multiple signal characterization algorithm (MUSIC). The processing shows that for all the beamforming algorithms using the CECM in lieu of the sample covariance matrix (SCM) exhibited comparable performance, often overlapping at a reduced computational cost of greater then 20%.