Enhanced Adaptive Beamforming Using LMMN Algorithm with SMI Initialization

摘要

Adaptive beamforming is achieved using adaptive antenna array for which the weights of the array element currents are adjusted in order to filter out the interfering signals from undesired sources, while enhancing the signal of interest from the desired source. Adaptive beamforming algorithms are typically characterized in terms of their convergence properties and computational complexity. One practical adaptive algorithm is the Least Mean Squares (LMS) which is simple to implement. It does not require measurements of the pertinent correlation functions, nor does it require matrix inversion. However, the LMS algorithm converges slowly when compared with other complicated algorithms such as the Recursive Least Square (RLS). On the other hand, Sample Matrix Inversion (SMI) algorithm has a fast convergence behavior However, because its speedy convergence is achieved through the use of matrix inversion, the SMI algorithm is computationally intensive. Moreover, the SMI algorithm has a block adaptive approach for which it is required that the signal environment does not undergo significant change during the course of block acquisition. The purpose of this paper is to develop an enhanced adaptive beamforming algorithm based on LMMN but with SMI initialization to ensure faster convergence. It is shown that judicious choice of the LMMN algorithm mixing parameter provides an algorithm with intermediate performance between the two special cases of least mean squares (LMS) and least mean fourth (LMF) algorithms. It is shown that the developed LMMN algorithm along with SMI initialization provides better steady state performance than the LMS algorithm and better stability properties than the LMF algorithm.