Low-Complexity Iterative Adaptive Linearly Constrained Minimum Variance Beamformer

摘要

The closed-form solution of linearly constrained minimum variance (CF-LCMV) suffers heavy computational burden from two-matrix inversion when computing the optimal vector. CF-LCMV is not an adaptive beamformer and performs poorly with low signal-to-interference-plus-noise-ratio (SINR) and small number of snapshots. In this study, we derive a low-complexity iterative adaptive LCMV (IA-LCMV) algorithm based on conjugate gradient (CG) technique with threefold advantages: first, IA-LCMV can remarkably alleviate the complexity of CF-LCMV; second, IA-LCMV can adjust output adaptively with comparable convergence speed. Finally, it shows robust performance against low SINR and small number of snapshots. Simulation results demonstrate the efficacy of our proposed algorithms.