Steering vector non-identifiability in covariance matrix fitting based beamforming

2012 ◽  
Author(s):  
Michael Rubsamen ◽  
Marius Pesavento
Keyword(s):  
Covariance Matrix ◽  
Steering Vector ◽  
Matrix Fitting

Multibaseline InSAR height estimation through joint covariance matrix fitting

2009 ◽  
Vol 3 (5) ◽  
pp. 474 ◽  
Author(s):  
N. Liu ◽  
L.-R. Zhang ◽  
X. Liu ◽  
Y. Zhou
Keyword(s):  
Covariance Matrix ◽  
Height Estimation ◽  
Matrix Fitting

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.

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