Robust widely linear adaptive MVDR beamformer based on interference-plus-noise covariance matrix and steering vector estimation
Yonghua Wang, Lei Huang, Yunmei Shi · 2015
In this paper, a robust widely linear adaptive beamformer is proposed for noncircular signals, which is able to efficiently reconstruct the interference-plus-noise covariance matrix (IN-CM) and estimate the extended steering vector. Estimation of the covariance matrix is based on the minimum variance distortionless response (MVDR) spatial spectrum integrated over a region which excludes the direction of the desired signal. Where after, the steering vector of the desired signal is determined using the low-complexity oracle approximating shrinkage (OAS) technique. Moreover, the proposed algorithm only requires the a priori knowledge of the angular sector of the desired signal and array structure. Simulation results show that our solution provides larger signal-to-intereference-plus-noise ratio than several conventional methods.