Study of Efficient Robust Adaptive Beamforming Algorithms Based on Shrinkage Techniques

TL;DR

This paper introduces low-complexity robust adaptive beamforming algorithms utilizing shrinkage techniques, improving estimation accuracy and computational efficiency in interference and noise scenarios.

Contribution

It develops a low-complexity adaptive RAB algorithm based on shrinkage methods, enhancing mismatch estimation and weight updating with reduced computational load.

Findings

The proposed algorithms outperform existing methods in simulation scenarios.

Shrinkage-based covariance estimation improves robustness.

Computational complexity is significantly reduced.

Abstract

This paper proposes low-complexity robust adaptive beamforming (RAB) techniques based on shrinkage methods. We firstly briefly review a Low-Complexity Shrinkage-Based Mismatch Estimation (LOCSME) batch algorithm to estimate the desired signal steering vector mismatch, in which the interference-plus-noise covariance (INC) matrix is also estimated with a recursive matrix shrinkage method. Then we develop low complexity adaptive robust version of the conjugate gradient (CG) algorithm to both estimate the steering vector mismatch and update the beamforming weights. A computational complexity study of the proposed and existing algorithms is carried out. Simulations are conducted in local scattering scenarios and comparisons to existing RAB techniques are provided.