Abstract
When the training snapshots are contaminated by the desired signal, the performance of the orthogonal projection (OP) approach degrades significantly. Therefore, an improved OP adaptive beamforming is proposed by reconstructing interference covariance matrix to eliminate the desired signal cancellation effect. In the proposed method, by integrating the Capon spatial spectrum over a region separated from the desired signal direction, the interference-plus-noise covariance matrix is reconstructed first to remove the desired signal component from the sample covariance matrix. Subsequently, the interference subspace is estimated by eigenvalue decomposition, and then the adaptive weight vector is calculated using the OP algorithm to eliminate the noise perturbation. Simulation results show that the performance of the proposed algorithm is almost the same as the optimum beamforming.
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