Robust Widely Linear Beamforming via an IAA Method for the Augmented IPNCM Reconstruction

Abstract

Based on the reconstruction of the augmented interferenceplus-noise (IPN) covariance matrix (CM) and the estimation of the desired signal's extended steering vector (SV), we propose a novel robust widely linear (WL) beamforming algorithm. Firstly, an extension of the iterative adaptive approach (IAA) algorithm is employed to acquire the spatial spectrum. Secondly, the IAA spatial spectrum is adopted to reconstruct the augmented signal-plus-noise (SPN) CM and the augmented IPNCM. Thirdly, the extended SV of the desired signal is estimated by using the iterative robust Capon beamformer with adaptive uncertainty level (AU-IRCB). Compared with several representative robust WL beamforming algorithms, simulation results are provided to confirm that the proposed method can achieve a better performance and has a much lower complexity.

Oblique Projection-Based Covariance Matrix Reconstruction and Steering Vector Estimation for Robust Adaptive Beamforming

“…Multiple methods were proposed in [2,[9][10][11] to compute the DL levels automatically without specifying any additional user parameters [3]. However, these methods suffer from performance degradation at high signal-to-noise ratio (SNR) [12], which is a common problem with this category of method. The ESB beamformers are performed by projecting the nominal SV onto an estimated subspace projector to mitigate the adverse impact caused by noisy disturbance [13][14][15].…”