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Widely linear beamforming is known to outperform the conventional adaptive beamforming by fully employing the non-circularity of non-circular signals. In this paper, a robust widely linear beamformer (WLB) is proposed to achieve satisfactory output and robustness in low computational complexity. The augmented signal-of-interest (SOI) covariance matrix is built by performing integration over SOI angular sector. The principal eigenvector of the augmented SOI covariance matrix is taken as the SOI extended steering vector. The received signal vector is projected into the interference-plus-noise subspace to remove the SOI component and derive the augmented interference covariance matrix. The augmented noise covariance matrix is added to the derived augmented interference covariance matrix so that the augmented interference-plus-noise covariance matrix can be reconstructed. The proposed WLB possesses lower computational complexity than the state-of-the-art WLBs due to the avoidance of convex optimization programming. Numerical simulations illustrate the effectiveness of the proposed WLB. [ABSTRACT FROM AUTHOR]

The distribution of the received signals in many array processing applications is noncircular. Although optimal widely linear beamformer (WLB) can provide the best performance for noncircular received signals, its performance degrades severely under model mismatches in practical applications. As a remedy, we propose a robust WLB by using precise reconstruction of extended interference-plus-noise covariance matrix (EINCM) and low-complexity estimation of extended desired signal steering vector (EDSSV). We propose to first determine the steering vectors, powers, and noncircularity coefficients of all signals and the noise power. In contrast to the previous reconstruction methods using the integration over a wide angular sector, we reconstruct the interference-plus-noise covariance matrix (INCM) and the pseudo INCM accurately according to their definitions. By using INCM and pseudo INCM, we can precisely reconstruct the EINCM. We propose to estimate the EDSSV by intersecting two extended subspaces, which are respectively formed by eigendecomposing the extended sample covariance matrix and the extended desired signal covariance matrix. Unlike the convex optimization methods, the proposed EDSSV estimation does not require any optimization programming and yields a solution with closed expression in low computational complexity. Simulation results show that the proposed robust WLB provides near optimal performance under several model mismatch cases. [ABSTRACT FROM AUTHOR]