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An adaptive beamforming algorithm named robust joint iterative optimizationdirection adaptive(RJIO-DA) is proposed for large-array scenarios.Based on the framework of minimum variance distortionless response(MVDR),the proposed algorithm jointly updates a transforming matrix and a reduced-rank filter.Each column of the transforming matrix is treated as an independent direction vector and updates the weight values of each dimension within a subspace.In addition,the direction vector rotation improves the performance of the algorithm by reducing the uncertainties due to the direction error.Simulation results show that the RJIO-DA algorithm has lower complexity and faster convergence than other conventional reduced-rank algorithms.
Based on the framework of minimum variance distortionless response (MVDR), the proposed algorithm jointly updates a transforming matrix and a reduced-rank filter (RJIO-DA) .Each column of the transforming matrix is treated as an independent direction vector and updates the weight values of each dimension within a subspace. In addition, the direction vector rotation improves the performance of the algorithm by reducing the uncertainties due to the direction error. Simulation results show that the RJIO-DA algorithm has lower complexity and faster convergence than other conventional reduced-rank algorithms.