Dimension-Reduced Robust Adaptive Beamforming via Group Cyclic Minimization

Adaptive digital beamforming (ADBF) is a widely used anti-interference technology for radar and communication systems. However, as the scale of receiving array increases and the fast time-varying interference is applied, reducing the computational complexity of ADBF is demanded to meet realtime requirements. To that end, a dimension-reduced ADBF algorithm based on group cyclic minimization, named GCM-ADBF, is proposed for large-scale array systems. By grouping the weights to be optimized and introducing the groupwise blocking matrix, GCM-ADBF enjoys fast implementation with parallelizable low-dimensional matrix inversion and iterations involving only matrix multiplications and additions, as well as superior robustness against few training samples. Moreover, GCM-ADBF is free from pre-estimation of the number and directions of interferences. The proposed algorithm is available for arrays with arbitrary configurations, and is compatible with fully digital and hybrid analog-digital beamforming systems. The superiority is verified by numerical simulations.