Adaptive Beamforming Using Projection Method in Large-Scale Antenna Arrays

Due to geometrical errors, complexity, and interference, the steering vector in large-scale antenna arrays needs to be accurately calculated. Beamforming methods based on adaptive processing can effectively overcome complexities and limitations of large-scale arrays. Here, we present the projection-based parallel linearly-constrained minimum variance (PBPLCMV) algorithm which calculates the steering vector of the desired signal through projecting the assumed steering vector onto the signal subspace. Then, the optimum weights of the adaptive beamformer are calculated using the parallel linear constrained minimum variance (PLCMV) algorithm. The simulation results show that for low input SNRs, the proposed method has better performance and output SINRs up to ۱۸ dB more than the output SINRs of the parallel robust recursive linearly constrained minimum variance (PRRLCMV).

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