Robust Adaptive Beamforming Based on a Novel Covariance Matrix Selection Strategy

A novel Robust Adaptive Beamforming (RAB) algorithm for the interference plus noise covariance matrix (INCM) selection is addressed. The proposed algorithm constructs the Eigenvector Selection Matrix (ESM) firstly, which is used to select the eigenvector and eigenvalue corresponding to the signal of interest (SOI). If the selected eigenvalue is greater than or equal to the maximum of several smaller eigenvalues of the sample covariance matrix (SCM), it is replaced with the minimum eigenvalue to reconstruct the INCM, and the calculated weight vector is projected into the SOI plus interferences subspace to enhance robustness. In the other case, to improve the efficiency of the algorithm, the SCM is selected as INCM to calculate the weight vector directly. The simulation results indicate that the proposed method is robust against types of mismatches to achieve superior performance.