A minimum variance beamformer with linear and quadratic constraints based on uniform linear antenna arrays

A novel adaptive beamforming algorithm is proposed based on a uniformly spaced linear antenna array by constraining its weight vector to a specific conjugate symmetric form. Two sets of constraints (linear and quadratic) are imposed on the beamformer while minimizing its output variance. Compared to the traditional linearly constrained minimum variance (LCMV) beamforming algorithm, the proposed one can achieve a faster convergence speed and a higher steady state SINR (signal to interference plus noise ratio) and is less sensitive to the change of its stepsize in terms of the final output SINR. Simulation results verify the effectiveness of the proposed algorithm.