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.
A simple and effective adaptive beamforming technique is proposed for uniform linear arrays, which are based on projection processing for covariance matrix construction and desired-signal steering vector estimation. The optimal minimum variance distortion-less response beamformer is closely achieved through approximating the interference-plus-noise covariance matrix by utilising the eigenvalue decomposition of the received signal's covariance matrix. Moreover, the direction-of-arrival (DOA) of the desired signal is estimated by maximising the beamformer output power in a certain angular sector. In particular, the proposed beamformer utilises the aforementioned DOA in order to estimate the desired-signal's steering vector for general steering vector mismatches. Simulation results indicate the better performance of the proposed method in the presence of the different errors compared with some of the recently introduced techniques.
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