An Improved Sub-Array Adaptive Beamforming Technique Based on Multiple Sources of Errors

In this paper, a new robust adaptive beamforming method is proposed in order to improve the robustness against steering vector (SV) mismatches that arise from multiple types of array errors. First, the sub-array technique is applied in order to obtain the decoupled sample covariance matrix (DSCM), in which the auxiliary sensors are selected to decouple the array. The decoupled interference-plus-noise covariance matrix (DINCM) is reconstructed with the estimated interference SV and maximum eigenvalue of the DSCM. Furthermore, the desired signal SV is estimated as the corresponding eigenvector determined by the correlation coefficients of the assumed SV and eigenvectors. Finally, the optimal weighting vector is obtained by combining the reconstructed DINCM and the estimated desired signal SV. Our simulation results show significant signal-to-interference-plus-noise ratio (SINR) enhancement of the proposed method over existing methods under multiple types of array errors.

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High Performance Robust Adaptive Beamforming in the Presence of Array Imperfections

International Journal of Antennas and Propagation ◽

10.1155/2016/3743509 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12 ◽

Cited By ~ 6

Author(s):

Wenxing Li ◽

Xiaojun Mao ◽

Zhuqun Zhai ◽

Yingsong Li

Keyword(s):

Covariance Matrix ◽

High Performance ◽

Signal To Noise Ratio ◽

Estimation Method ◽

Adaptive Beamforming ◽

Steering Vector ◽

Steering Vector Estimation ◽

Sample Covariance ◽

Noise Ratio ◽

Robust Adaptive

A high performance robust beamforming scheme is proposed to combat model mismatch. Our method lies in the novel construction of interference-plus-noise (IPN) covariance matrix. The IPN covariance matrix consists of two parts. The first part is obtained by utilizing the Capon spectrum estimator integrated over a region separated from the direction of the desired signal and the second part is acquired by removing the desired signal component from the sample covariance matrix. Then a weighted summation of these two parts is utilized to reconstruct the IPN matrix. Moreover, a steering vector estimation method based on orthogonal constraint is also proposed. In this method, the presumed steering vector is corrected via orthogonal constraint under the condition where the estimation does not converge to any of the interference steering vectors. To further improve the proposed method in low signal-to-noise ratio (SNR), a hybrid method is proposed by incorporating the diagonal loading method into the IPN matrix reconstruction. Finally, various simulations are performed to demonstrate that the proposed beamformer provides strong robustness against a variety of array mismatches. The output signal-to-interference-plus-noise ratio (SINR) improvement of the beamformer due to the proposed method is significant.

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Robust adaptive beamforming with imprecise steering vector and noise covariance matrix due to finite sample size

2013 IEEE International Conference on Acoustics, Speech and Signal Processing ◽

10.1109/icassp.2013.6638366 ◽

2013 ◽

Cited By ~ 2

Author(s):

Mengyi Zhang ◽

Francisco Rubio ◽

Daniel P. Palomar ◽

Xavier Mestre

Keyword(s):

Sample Size ◽

Covariance Matrix ◽

Adaptive Beamforming ◽

Finite Sample ◽

Noise Covariance Matrix ◽

Steering Vector ◽

Robust Adaptive Beamforming ◽

Finite Sample Size ◽

Noise Covariance ◽

Robust Adaptive

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Low-Complexity Robust Adaptive Beamforming Based on INCM Reconstruction via Subspace Projection

Sensors ◽

10.3390/s21237783 ◽

2021 ◽

Vol 21 (23) ◽

pp. 7783

Author(s):

Yanliang Duan ◽

Xinhua Yu ◽

Lirong Mei ◽

Weiping Cao

Keyword(s):

Covariance Matrix ◽

Minimum Mean Square Error ◽

Low Complexity ◽

Training Sequence ◽

Adaptive Beamforming ◽

Projection Matrix ◽

Robust Adaptive Beamforming ◽

Sample Covariance ◽

Blocking Matrix ◽

Robust Adaptive

Adaptive beamforming is sensitive to steering vector (SV) and covariance matrix mismatches, especially when the signal of interest (SOI) component exists in the training sequence. In this paper, we present a low-complexity robust adaptive beamforming (RAB) method based on an interference–noise covariance matrix (INCM) reconstruction and SOI SV estimation. First, the proposed method employs the minimum mean square error criterion to construct the blocking matrix. Then, the projection matrix is obtained by projecting the blocking matrix onto the signal subspace of the sample covariance matrix (SCM). The INCM is reconstructed by replacing part of the eigenvector columns of the SCM with the corresponding eigenvectors of the projection matrix. On the other hand, the SOI SV is estimated via the iterative mismatch approximation method. The proposed method only needs to know the priori-knowledge of the array geometry and angular region where the SOI is located. The simulation results showed that the proposed method can deal with multiple types of mismatches, while taking into account both low complexity and high robustness.

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