Design of Robust Adaptive Beamforming Algorithms Based on Low-Rank and Cross-Correlation Techniques

TL;DR

This paper introduces low-rank, cost-effective robust adaptive beamforming algorithms that utilize cross-correlation and Krylov subspace methods to improve performance in large sensor arrays.

Contribution

It proposes a novel orthogonal Krylov subspace projection mismatch estimation (OKSPME) method and adaptive algorithms based on SG and CG techniques for efficient beamforming.

Findings

Excellent SINR performance in simulations

Cost-effective for high-dimensional data

Avoids costly matrix inversions

Abstract

This work presents cost-effective low-rank techniques for designing robust adaptive beamforming (RAB) algorithms. The proposed algorithms are based on the exploitation of the cross-correlation between the array observation data and the output of the beamformer. Firstly, we construct a general linear equation considered in large dimensions whose solution yields the steering vector mismatch. 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, resulting in the proposed orthogonal Krylov subspace projection mismatch estimation (OKSPME) method. We also devise adaptive algorithms based on stochastic gradient (SG) and conjugate gradient (CG) techniques to update the beamforming weights with low complexity and avoid any costly matrix inversion. The main advantages of the proposed low-rank and mismatch estimation techniques are their cost-effectiveness when dealing with high dimension subspaces or large sensor arrays. Simulations results show excellent performance in terms of the output signal-to-interference-plus-noise ratio (SINR) of the beamformer among all the compared RAB methods.