A transform domain based Least Mean Mixed-Norm algorithm to improve adaptive beamforming

In this paper, an adaptive beamforming algorithm based on Least Mean Mixed Norm (LMMN) algorithm and self-orthogonalizing transform is presented. In the proposed algorithm, LMMN is used to improve the convergence rate of conventional LMS algorithm. Whereas, discreet sine transform is used as self-orthogonalizing transform to address the eigen-spread problem. The simulation results are the evidence of significantly improvement in the convergence rate and mean squared error (MSE) performance of the proposed algorithm.