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The performance of traditional reconstruction-based robust adaptive beamformers may degrade when the array is not well calibrated. This performance degradation is mainly caused by the Capon spectrum estimator which can not estimate the spatial power spectrum accurately. In contrast to existing approaches, we propose two new reconstruction-based robust adaptive beamformers by using the accurate iterative adaptive approach (IAA) spectrum to combat the covariance matrix uncertainties and the steering vector mismatches. The first one employs the low-complexity IAA (IAA-LC) algorithm to obtain the interference power estimates and reconstruct the interference-plus-noise covariance matrix (INCM) with the computational complexity further reduced. The second one reconstructs the INCM by updating the power estimates corresponding to each interference steering vector with the use of knowledge-aided IAA (IAA-KA) algorithm. The desired signal steering vector is corrected by searching for the direction corresponding to the maximum power, which circumvents the use of optimization program. Simulation results show that our proposed beamformers outperform the others and can achieve the robust performance in the cases of array model mismatches. [ABSTRACT FROM AUTHOR]

The performance of adaptive beamformers is susceptible to the steering vector mismatches and covariance matrix uncertainties. In contrast to traditional subspace methods, the proposed subspace method is robust against model mismatches. The signal-plus-interference covariance matrix (SICM) is reconstructed by projecting the enhanced covariance matrix onto the signal-plus-interference subspace which is formed using the property of covariance matrix. The array steering vector is adjusted by projecting the presumed steering vector onto the signal subspace which is structured using the major eigenvectors of the enhanced covariance matrix. The major eigenvectors are selected when the correlation coefficient between the presumed steering vector and the eigenvector exceeds a certain threshold. By combining the reconstructed SICM with the adjusted array steering vector, the proposed beamformer can provide robust performance. In comparison with many existing robust techniques, the proposed method performs well in both high and low signal-to-noise ratio scenarios. Several examples are carried out to illustrate the superiority of our method. [ABSTRACT FROM AUTHOR]