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The presence of the desired signal (DS) in the training snapshots makes the adaptive beamformer sensitive to any steering vector mismatch and dramatically reduces the convergence rate. Even the performance of the most of the existing robust adaptive beamformers is degraded when the signal‐to‐noise ratio (SNR) is increased. In this study, a high converging rate robust adaptive beamformer is proposed. This method is a promoted eigenspace‐based beamformer. In this paper, a new signal‐plus‐interferences (SPI) covariance matrix estimator is proposed. The subspace of the ideal SPI covariance matrices is exploited and the estimated covariance matrix is projected into this subspace. This projection effectively reduces the covariance matrix estimation error and the proposed estimator yields a more accurate estimation of the SPI covariance matrix. In addition, a computationally efficient steering vector estimator has been proposed. To prevent the absence of the DS steering vector in the estimated SPI subspace, the estimated SPI covariance matrix is compensated. Hence, the proposed method can attain the optimal beamformer in the both high and low SNR cases. The numerical examples indicate that this method has excellent signal‐to‐interference plus noise ratio performance and offers a higher converging rate compared with the existing robust adaptive beamforming algorithms. [ABSTRACT FROM AUTHOR]

The presence of the desired signal (DS) in the training snapshots makes the adaptive beamformer sensitive to any steering vector mismatch and dramatically reduces the convergence rate. The objective of the present study is to propose a new adaptive beamformer which is robust against direction‐of‐arrival (DOA) mismatch and its convergence rate is not sensitive to the presence of the DS. This method is applicable to the arrays with specific structure such as the linear array. Our approach is based on the DS elimination from the training snapshots and the sub‐array beamforming technique. To accomplish this goal, a blocking matrix which converts the primary data to the DS‐free data is synthesized. The Synthesis process is based on the desired degree of freedom and the uncertainty of DOA of the DS. Using the signal‐free data, the beamforming vector is calculated through the presented algorithm. Owing to elimination of the DS from the training snapshots and performing the adaptive operations in the sub‐array level, our algorithm has high convergence rate and excellent performance even in cases with the small sample size. Simulations show that the proposed beamformer can achieve a much better performance in terms of output SINR compared to the existing ones. [ABSTRACT FROM AUTHOR]

The presence of the desired signal (DS) in the training snapshots makes the adaptive beamformer sensitive to any steering vector mismatch and dramatically reduces the convergence rate. The objective of the present study is to propose a new adaptive beamformer which is robust against direction-of-arrival (DOA) mismatch and its convergence rate is not sensitive to the presence of the DS. This method is applicable to the arrays with specific structure such as the linear array. Our approach is based on the DS elimination from the training snapshots and the sub-array beamforming technique. To accomplish this goal, a blocking matrix which converts the primary data to the DS-free data is synthesized. The Synthesis process is based on the desired degree of freedom and the uncertainty of DOA of the DS. Using the signal-free data, the beamforming vector is calculated through the presented algorithm. Owing to elimination of the DS from the training snapshots and performing the adaptive operations in the sub-array level, our algorithm has high convergence rate and excellent performance even in cases with the small sample size. Simulations show that the proposed beamformer can achieve a much better performance in terms of output SINR compared to the existing ones. [ABSTRACT FROM AUTHOR]