Robust Adaptive Beamforming via Covariance Matrix Reconstruction and Interference Power Estimation

The performance of the traditional Capon beamformer degrades sharply when the signal of interest (SOI) appears in the training data. To reduce the impact of SOI on the Capon beamformer, two methods for the interference-plus-noise covariance matrix (INCM) reconstruction are proposed in this letter. The proposed-1 method is based on the integral of the Capon spectrum without the residual noise power. In the proposed-2 method, the interference power is estimated via the orthogonality between different sparse steering vectors (SVs) to project the sample covariance matrix for the INCM reconstruction. Meanwhile, the inverse of INCM is obtained by eigenvalue decomposition and the SV of SOI is updated by the principal eigenvector of the reconstructed SOI covariance matrix (SCM). Simulation results show that the proposed methods are robust against some mismatch errors.