Robust adaptive beamforming based on low-rank and cross-correlation techniques

This work presents a cost-effective low-rank technique for designing robust adaptive beamforming (RAB) algorithms. The proposed technique is based on low-rank modelling of the mismatch and exploitation of the cross-correlation between the received data and the output of the beamformer. We construct a linear system of equations which computes the steering vector mismatch based on prior information about the level of mismatch, and then we employ an orthogonal Krylov subspace based method to iteratively estimate the steering vector mismatch in a reduced-dimensional subspace, resulting in the proposed orthogonal Krylov subspace projection mismatch estimation (OKSPME) method. Simulation results show excellent performance of OKSPME in terms of the beamformer output signal-to-interference-plus-noise ratio (SINR) as compared to existing RAB algorithms.