Low-rank robust adaptive beamforming techniques using joint iterative optimization

This work presents cost-effective low-rank techniques for designing robust adaptive beamforming (RAB) algorithms. At first, we introduce an orthogonal Krylov subspace projection mismatch estimation (OKSPME) method, in which a general linear equation is considered in large dimensions which aims to solve for the steering vector mismatch with known information, then we employ the idea of the full orthogonalization method (FOM), an orthogonal Krylov subspace based method, to iteratively estimate the steering vector mismatch in a reduced-dimensional subspace. An adaptive algorithm based on stochastic gradient and joint iterative optimization (JIO) dimensionality reduction technique is devised for beamforming large sensor arrays with low complexity. Simulations results show excellent performance in terms of the output signal-to-interference-plus-noise ratio (SINR) among all the compared RAB methods.