A structured gradient algorithm for adaptive beamforming

The constrained least-mean-squares (LMS) algorithm uses a noise estimate of the required gradient to adaptively estimate the weights of an optimal antenna array. The gradient is estimated by multiplying the array output with the array receiver outputs. An alternative scheme for estimating the required gradient is proposed. The proposed scheme uses a structured estimate of the array correlation matrix to estimate the gradient. This structure reflects the structure of the exact array correlation matrix and is obtained by a spatial averaging of the elements of the noisy array correlation matrix used in the standard algorithm. The authors compare the performance of the standard LMS algorithm with the proposed algorithm and shows that their algorithm has a better convergence performance