Study of Robust Adaptive Beamforming with Covariance Matrix Reconstruction Based on Power Spectral Estimation and Uncertainty Region

TL;DR

This paper introduces a robust adaptive beamforming method for uniform linear arrays that reconstructs the interference plus noise covariance matrix using power spectral estimation and an uncertainty region, improving performance across various SNRs.

Contribution

It presents a novel approach combining power spectral estimation with an interference uncertainty region for covariance matrix reconstruction in adaptive beamforming.

Findings

Performance close to optimal across a wide SNR range

Avoids full spectrum power estimation of interference directions

Updates interference region based on array data statistics

Abstract

In this work, a simple and effective robust adaptive beamforming technique is proposed for uniform linear arrays, which is based on the power spectral estimation and uncertainty region (PSEUR) of the interference plus noise (IPN) components. In particular, two algorithms are presented to find the angular sector of interference in every snapshot based on the adopted spatial uncertainty region of the interference direction. Moreover, a power spectrum is introduced based on the estimation of the power of interference and noise components, which allows the development of a robust approach to IPN covariance matrix reconstruction. The proposed method has two main advantages. First, an angular region that contains the interference direction is updated based on the statistics of the array data. Secondly, the proposed IPN-PSEUR method avoids estimating the power spectrum of the whole range of possible directions of the interference sector. Simulation results show that the performance of the proposed IPN-PSEUR beamformer is almost always close to the optimal value across a wide range of signal-to-noise ratios.