Robust Regularized Least-Squares Beamforming Approach to Signal Estimation

TL;DR

This paper introduces a robust regularized least-squares beamforming method that improves signal estimation by addressing covariance matrix inversion issues and steering vector uncertainties in adaptive array processing.

Contribution

A novel RLS-based beamforming approach that enhances robustness without prior information, outperforming existing algorithms in simulations.

Findings

Outperforms state-of-the-art beamformers in simulations

Provides robustness against covariance matrix ill-conditioning

Effectively handles steering vector uncertainties

Abstract

In this paper, we address the problem of robust adaptive beamforming of signals received by a linear array. The challenge associated with the beamforming problem is twofold. Firstly, the process requires the inversion of the usually ill-conditioned covariance matrix of the received signals. Secondly, the steering vector pertaining to the direction of arrival of the signal of interest is not known precisely. To tackle these two challenges, the standard capon beamformer is manipulated to a form where the beamformer output is obtained as a scaled version of the inner product of two vectors. The two vectors are linearly related to the steering vector and the received signal snapshot, respectively. The linear operator, in both cases, is the square root of the covariance matrix. A regularized least-squares (RLS) approach is proposed to estimate these two vectors and to provide robustness without exploiting prior information. Simulation results show that the RLS beamformer using the proposed regularization algorithm outperforms state-of-the-art beamforming algorithms, as well as another RLS beamformers using a standard regularization approaches.