A comparison of initialization schemes for blind adaptive beamforming

Many blind adaptive beamforming algorithms require the selection of one or more non-zero initial weight vectors. Proper selection of the initial weight vectors can speed algorithm convergence and help ensure convergence to the desired solutions. Three alternative initialization approaches are compared here, all of which depend only on second order statistics of the observed data. These methods are based on Gram-Schmidt orthogonalization, eigendecomposition, and QR decomposition of the observed data covariance matrix. We show through computer simulation that the eigendecomposition approach yields the best performance