URGLQ: An Efficient Covariance Matrix Reconstruction Method for Robust Adaptive Beamforming

TL;DR

This paper introduces URGLQ, a covariance matrix reconstruction method that enhances adaptive beamforming robustness and accuracy by removing unwanted signals and efficiently approximating integrals, outperforming existing methods.

Contribution

The paper proposes a novel covariance matrix reconstruction approach using a projection matrix and Gauss-Legendre quadrature, improving robustness and reducing computational complexity in adaptive beamforming.

Findings

Outperforms compared methods in simulations and experiments.

Achieves performance close to the optimal beamformer.

Effectively removes unwanted signals and corrects steering vector mismatch.

Abstract

The computational complexity of the conventional adaptive beamformer is relatively large, and the performance degrades significantly due to the model mismatch errors and the unwanted signals in received data. In this paper, an efficient unwanted signal removal and Gauss-Legendre quadrature (URGLQ)-based covariance matrix reconstruction method is proposed. Different from the prior covariance matrix reconstruction methods, a projection matrix is constructed to remove the unwanted signal from the received data, which improves the reconstruction accuracy of the covariance matrix. Considering that the computational complexity of most matrix reconstruction algorithms is relatively large due to the integral operation, we proposed a Gauss-Legendre quadrature-based method to approximate the integral operation while maintaining accuracy. Moreover, to improve the robustness of the beamformer, the mismatch in the desired steering vector is corrected by maximizing the output power of the beamformer under a constraint that the corrected steering vector cannot converge to any interference steering vector. Simulation results and prototype experiments demonstrate that the performance of the proposed beamformer outperforms the compared methods and is much closer to the optimal beamformer in different scenarios.