Robust Adaptive Beamforming with Subspace Projection and Covariance Matrix Reconstruction

Abstract

In this paper, we present a subspace projection and covariance matrix reconstruction (SPCMR) algorithm for adaptive beamforming to improve the robustness against large SV mismatch. The SPCMR algorithm consists of two parts: projection subspaces estimation and interference-plus-noise covariance matrix (INCM) reconstruction. Specifically, we estimate two projection subspaces containing the signal component and obtain the signal SV from their intersection. The first projection subspace is estimated from the constructed signal covariance matrix via the distortionless responses principle. The second one is gotten according to the subspace proximity between the nominal signal SV and the eigenvectors of the sample covariance matrix. Subsequently, the interference SVs are estimated by using the Capon spatial estimator, and each interference power is obtained via the oblique projectors. After that, an accurate INCM is reconstructed, and the SPCMR beamformer is proposed. The simulation results show that the SPCMR algorithm is robust to several model mismatches and outperforms other adaptive algorithms.