Robust blind beamforming algorithm using joint multiple matrix diagonalization (JMMD)

Abstract

We propose a new joint multiple matrix diagonalization algorithm for robust blind beamforming. This new algorithm is based on the iterative eigen- decomposition of cumulant matrices. Therefore it can avoid the stability and misadjustment problems arising among the conventional steepest-descent approaches for constant- modulus or cumulant optimization. Our Monte Carlo simulations show that our proposed algorithm significantly outperforms the JADE algorithm based on the Givens rotation for PSK source signals in terms of signal-to- interference-and-noise ratio for a wide variety of signal-to- noise ratios. Keywords-blind beamforming; eigen decomposition;JADE algorithm; cumulant; constant modulus I. INTRODUCTION The traditional beamforming techniques such as the minimum variance distortionless response (MVDR) approach (1) and the linear constrained minimum variance (LCMV) method (1) are based on the a priori knowledge of the directional vector associated with the desired source signal and may be quite sensitive to the perturbation of this vector. The perturbation of the vector direction is caused by the unknown deformation of the antenna array, the drifting effect in the electronics or the multipath propagation. Consequently, those factors strictly limit the performance of MVDR and LCMV beamformers in practice.