Robust Adaptive Beamforming Based on Worst-Case Performance Optimization

2005 ◽  
pp. 49-89 ◽  
Author(s):  
Alex B. Gershman ◽  
Zhi-Quan Luo ◽  
Shahram Shahbazpanahi
Keyword(s):  
Performance Optimization ◽  
Adaptive Beamforming ◽  
Worst Case ◽  
Robust Adaptive Beamforming ◽  
Robust Adaptive

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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