Robust adaptive beamforming with joint robustness against convariance uncertainty and steering vector mismatch

Abstract

We address a new adaptive beamforming algorithm with joint robustness against both covariance matrix uncertainty and steering vector (SV) mismatch, which needs neither the norm of the SV nor its error upper bound. Firstly, we propose a modified general linear combination (MGLC) algorithm to obtain a better estimation of covariance matrix. Then using the MGLC-based estimation, we formulate a quadratic convex optimization problem in view of the Capon power spectrum to obtain a refined SV. The proposed algorithm maximizes the beamformer output power under two constraints that the mismatch vector is orthogonal to the presumed SV and the refined SV does not converge to any interference SV. Simulation results demonstrate that the proposed beamformer outperforms the beamformers which only have robustness against single one of these two kinds of array mismatches.