Study of Robust Adaptive Beamforming Algorithms Based on Power Method Processing and Spatial Spectrum Matching

TL;DR

This paper introduces an efficient robust adaptive beamforming method that reconstructs interference-plus-noise covariance matrices using the power method, improving performance without eigenvalue decomposition.

Contribution

The proposed technique reconstructs covariance matrices using the power method and spatial spectrum matching, avoiding eigenvalue decomposition and enhancing robustness against model mismatches.

Findings

Improved beamforming performance in high SNR scenarios.

Effective interference-plus-noise covariance matrix reconstruction.

Validation through simulations showing superiority over existing methods.

Abstract

Robust adaptive beamforming (RAB) based on interference-plus-noise covariance (INC) matrix reconstruction can experience performance degradation when model mismatch errors exist, particularly when the input signal-to-noise ratio (SNR) is large. In this work, we devise an efficient RAB technique for dealing with covariance matrix reconstruction issues. The proposed method involves INC matrix reconstruction using an idea in which the power and the steering vector of the interferences are estimated based on the power method. Furthermore, spatial match processing is computed to reconstruct the desired signal-plus-noise covariance matrix. Then, the noise components are excluded to retain the desired signal (DS) covariance matrix. A key feature of the proposed technique is to avoid eigenvalue decomposition of the INC matrix to obtain the dominant power of the interference-plus-noise region. Moreover, the INC reconstruction is carried out according to the definition of the theoretical INC matrix. Simulation results are shown and discussed to verify the effectiveness of the proposed method against existing approaches.