Robust Adaptive Beamforming Based on Steering Vector Estimation and Interference Power Correction via Subspace Orthogonality

To address the issue of performance degradation in the Capon beamformer when the desired signal appears in the training data, the integration operation is introduced for the interference-plus-noise covariance matrix reconstruction, and the steering vector (SV) is estimated by solving a convex optimization problem in some robust adaptive beamforming papers. However, this approach suffers from high computational complexity and is limited by the resolution of the Capon spectrum. In light of this, our paper proposes a novel robust adaptive beamforming method. To overcome the limited resolution of the Capon spectrum, we introduce the multiple signal classification method to acquire the nominal SVs. Subsequently, the SVs are updated based on subspace orthogonality. The proposed method constructs orthogonal components from the nominal SVs to circumvent the convex problem and reduce computational complexity. Additionally, the interference powers are obtained using the Capon spectrum without the noise component via eigenvalue decomposition. This refinement improves the accuracy of the estimated interference powers. Simulation results demonstrate that the proposed method is effective and robust against common mismatch errors.