A Two-Stage Nonlinear Shrinkage of the Sample Covariance Matrix for Robust Capon Beamforming

When the number of snapshots used to estimate the Sample covariance matrix (SCM) approaches infinity and the array steering vector is accurately known, the Standard Capon beamformer (SCB) can better suppress spatial noises than data-independent beamformers. On the contrary, the performance of the SCB may decrease. To solve this problem, we propose a two-stage shrinkage scheme for the SCM. Specifically, in the first stage, the SCM is enhanced by the General linear combination (GLC) method, which will be referred to as GLC-SCM; and in the second stage, the GLCSCM is further improved with the Exponential matrix (EM) method, which will be referred to as GLC-EM-SCM. Compared with the conventional methods, the proposed method can achieve higher signal-to-interference-noise ratio output and more accurate signal power estimate.