scholarly journals Robust Adaptive Beamforming against Signal Steering Vector Mismatch and Jammer Motion

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaojun Mao ◽  
Wenxing Li ◽  
Yingsong Li ◽  
Yaxiu Sun ◽  
Zhuqun Zhai
Keyword(s):  
Adaptive Beamforming ◽  
Projection Matrix ◽  
Diagonal Loading ◽  
Steering Vector ◽  
Output Performance ◽  
Sample Covariance ◽  
Steering Vector Mismatch ◽  
Simulation Results ◽  
Robust Adaptive ◽  
Analytical Expressions

Since adaptive beamformer suffers from output performance degradation in the presence of interference nonstationarity and signal steering vector mismatch, a novel robust null broadening adaptive beamforming is proposed. The proposed method is realized by the combination of projection transform and diagonal loading techniques. First, a new projection matrix with null broadening ability is constructed and then projects the array received data onto the projection matrix. With the diagonal loading technique, a new sample covariance matrix is obtained. The theoretical analysis shows that the projection transform operation can expand the incident direction of the interference and improve orthogonality between the signal-plus-interference and the noise subspaces; thus the proposed beamformer can effectively broaden the jammer null and enhance the null depth. The analytical expressions of the proposed algorithm are also provided, which are efficient and easily solved. Simulation results are presented and demonstrated that the proposed beamformer can provide strong robustness against signal steering vector mismatch and jammer motion.

scholarly journals High Performance Robust Adaptive Beamforming in the Presence of Array Imperfections

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
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.

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

Sensors ◽  
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.

scholarly journals Robust adaptive beamforming with enhancing the interference suppression capability

2019 ◽  
Vol 8 ◽  
Author(s):  
Linxian Liu ◽  
Yang Li
Keyword(s):  
Performance Optimization ◽  
Signal To Noise Ratio ◽  
Interference Suppression ◽  
Adaptive Beamforming ◽  
Training Data ◽  
Worst Case ◽  
Diagonal Loading ◽  
Steering Vector ◽  
Robust Adaptive Beamforming ◽  
Robust Adaptive

AbstractThe steering vector mismatch causes signal self-nulling for adaptive beamforming when the training data contain the desired signal component. To prevent signal self-nulling, many beamformers use robust technology, which is usually equivalent to the diagonal loading approach. Unfortunately, the diagonal loading approach achieves better signal enhancement at the cost of losing its interference suppression capability, especially at high input signal-to-noise ratio. In this paper, a novel robust adaptive beamforming method is developed to improve the interference suppression capability. The proposed beamformer is based on the worst-case performance optimization technology with a new estimated steering vector and a special set parameter. Firstly, a subspace which is orthogonal to the interference's steering vector is obtained by using the interference-plus-noise covariance matrix; then a new steering vector which is orthogonal to each interference's steering vector is estimated; finally, the beamformer's weight is solved with the worst-case performance optimization technology with a special set parameter. Theoretical analysis of the interference suppression principle is analyzed in detail, and some simulation results are presented to evaluate the performance of the proposed beamformer.

scholarly journals Robust Adaptive Beamforming Based on Worst-Case and Norm Constraint

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Hongtao Li ◽  
Ke Wang ◽  
Chaoyu Wang ◽  
Yapeng He ◽  
Xiaohua Zhu
Keyword(s):  
Performance Optimization ◽  
Adaptive Beamforming ◽  
Worst Case ◽  
Steering Vector ◽  
Robust Adaptive Beamforming ◽  
Simulation Results ◽  
Robust Adaptive ◽  
Norm Constraint

A novel robust adaptive beamforming based on worst-case and norm constraint (RAB-WC-NC) is presented. The proposed beamforming possesses superior robustness against array steering vector (ASV) error with finite snapshots by using the norm constraint and worst-case performance optimization (WCPO) techniques. Simulation results demonstrate the validity and superiority of the proposed algorithm.

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