  In this letter, we present a novel low-complexity adaptive beamforming
technique using a stochastic gradient algorithm to avoid matrix inversions. The
proposed method exploits algorithms based on the maximum entropy power spectrum
(MEPS) to estimate the noise-plus-interference covariance matrix (MEPS-NPIC) so
that the beamforming weights are updated adaptively, thus greatly reducing the
computational complexity. MEPS is further used to reconstruct the desired
signal covariance matrix and to improve the estimate of the desired signals's
steering vector (SV). Simulations show the superiority of the proposed
MEPS-NPIC approach over previously proposed beamformers.
