Robust Adaptive Beamforming via Covariance Matrix Reconstruction Under Colored Noise
Huichao Yang, Pengyu Wang, Zhongfu Ye
Abstract
Aimed at the performance degradation of the standard Capon beamformer (SCB) when the signal of interest (SOI) appearing in the training data under the colored noise, a novel interference-plus-noise covariance matrix (INCM) reconstruction method is proposed in this letter. The colored noise covariance matrix (CNCM) is estimated by the cross Capon power in the noise sector and the interference power is obtained via the quasi-orthogonality based on the conventional beamforming (CBF) between different sparse steering vectors (SVs) to project the sample covariance matrix for the INCM reconstruction. Then, the SV of the SOI is updated by the principal eigenvector of the reconstructed SOI covariance matrix. Simulation results show that the proposed method is robust against some mismatch errors under the colored noise.