scholarly journals User–Parameter–Free Robust Adaptive Beamforming Algorithm for Vector–Sensor Arrayswithin the Hypercomplex Framework

2013 ◽  
Vol 64 (2) ◽  
pp. 100-105
Author(s):  
Xiaoming Gou ◽  
Zhiwen Liu ◽  
Jingyan Ma ◽  
Yougen Xu
Keyword(s):  
Numerical Simulations ◽  
Sensor Arrays ◽  
Optimal Solution ◽  
Adaptive Beamforming ◽  
Model Errors ◽  
Vector Sensor ◽  
Two Component ◽  
Maximal Correlation ◽  
Robust Adaptive ◽  
Beamforming Algorithm

The major flaw of the conventional diagonal loading (DL) method is that it is unclear to choose appropriate DL levels or user-parameters (UPs), though several remarkable contributions have been made to regularize model errors without UPs. An UP-free algorithm for two-component vector-sensor arrays, which is robust to steering vector errors, is considered. The algorithm is within the hypercomplex framework using quaternions, and the optimal solution is found at the maximal correlation between the quaternionic and complex outputs. The performance of the proposed beamformer is illustrated via numerical simulations and is compared with several other UP-free adaptive beamformers

Robust Adaptive Beamforming Applied to Planar Array

2013 ◽  
Vol 347-350 ◽  
pp. 3930-3933
Author(s):  
Hai Yan Song ◽  
Jie Shi ◽  
Bo Sheng Liu ◽  
Ye Tian ◽  
Jun Ye
Keyword(s):  
Natural Extension ◽  
Side Lobe ◽  
Adaptive Beamforming ◽  
Interference Rejection ◽  
Vector Error ◽  
Second Order Cone ◽  
Planar Array ◽  
Robust Adaptive ◽  
Steering Vector Error ◽  
Beamforming Algorithm

Capon beamforming has better resolution and interference rejection capability. However, its performance will seriously degrade due to noise, array steering vector error, and other factors. In this paper, a robust Capon beamforming applied to a planar array is described. It is shown that the proposed method is the natural extension of the original Vector Optimization Robust Beamforming algorithm to the case of a planar array, and can be reformulated as a convex second-order cone program and solved by SEDUMI. Computer simulation has shown that the proposed method has better performance than other conventional methods, such as narrower main lobe and lower side lobe.

scholarly journals An optimized 2D-Robust Adaptive Beamforming algorithm based on Matrix Completion in sparse array

2018 ◽  
Vol 208 ◽  
pp. 01003
Author(s):  
Jiaying Di ◽  
Wen Hu ◽  
Mengxia Li ◽  
Hongtao Li
Keyword(s):  
Matrix Completion ◽  
Adaptive Beamforming ◽  
Singular Value ◽  
Spectral Norm ◽  
Sparse Array ◽  
Robust Adaptive Beamforming ◽  
Robust Adaptive ◽  
Singular Value Thresholding ◽  
Beamforming Algorithm ◽  
Norm Error

The sparse arrays can reduce the cost of beamforming, it greatly reduces the number of actual array elements. However, it also brings about the problem of information loss. A 2D-robust adaptive beamforming algorithm in sparse array based on Singular Value Thresholding algorithm is proposed. At first, a signal model of planar array is established based on Matrix Completion, which can be proved to meet Null Space Property. Then the Genetic Algorithm is used to optimize the sparse array, which is determined to reduce the Spectral Norm Error of Matrix Completion and make the array recovered closer to the full array. In the case of sparse array, the missing information is restored by using the theory of Singular Value Thresholding, and then the restored signal is used to design the digital beamformer weights. This algorithm significantly reduces the Spectral Norm Error and forms robust adaptive beam.

Joint Adaptive Beamforming of Interference Covariance Matrix Reconstruction and Steering Vector Estimation

2013 ◽  
Vol 791-793 ◽  
pp. 2092-2095
Author(s):  
Xiao Ke Huang ◽  
Luo Kun Liu ◽  
You Ming Sun ◽  
Jin Jin Yang
Keyword(s):  
Covariance Matrix ◽  
Adaptive Beamforming ◽  
High Signal ◽  
Steering Vector ◽  
Matrix Reconstruction ◽  
Steering Vector Estimation ◽  
Covariance Matrix Reconstruction ◽  
Vector Estimation ◽  
Robust Adaptive ◽  
Beamforming Algorithm

Proposed a robust adaptive beamforming algorithm for the beamformers performance degradation in high Signal-to-Noise Ratios (SNRs), which is based on interference covariance matrix reconstruction and steering vector estimation. The simulation results prove the effectiveness.

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