Adaptive beamforming algorithm based on direction vector rotation and joint iterative optimization

A robust reduced-rank algorithm named RJIO-DA (Robust Joint Iterative Optimization-Direction Adaptive) is proposed for adaptive beamforming in large array scenarios. Based on a MVDR (Minimum Variance Distortionless Response) framework, the proposed algorithm jointly updates a transforming matrix and a reduced-rank filter. In order to reduce the complexity and improve the performance, each column of the transforming matrix is regarded as a direction vector on each dimension of the subspace and is updated individually. In addition, the limited uncertainties caused by the direction error can be overcome with the proposed algorithm. The simulation results show that RJIO-DA algorithm has lower complexity and faster convergence compared with the conventional reduced-rank algorithms.