Self-orthogonalizing algorithms for adaptive beamforming

The convergence rate of the traditional LMS algorithm for adaptive beamforming is highly dependent on the eigen-structure of the correlation matrix of the signals arriving at the antenna array elements. A poor eigen-structure of the correlation matrix will lead to a bad convergence. In this paper, we applied self-orthogonalizing transform - the Karhunen-Loeve transform (KLT) to the input signal vector before feeding it into the LMS algorithm, analyzed the influencing factors of the eigen-spread of the correlation matrix, and compared the KLT-LMS algorithm with the traditional LMS algorithm. The simulation results illustrate that the convergence rate of the self-orthogonalizing algorithm is independent of the eigen-structure of the correlation matrix and the KLT-LMS algorithm outperforms the traditional LMS algorithm.