Low-complexity implementation for worst-case optimization-based robust adaptive beamforming

2008 ◽  
Author(s):  
Biao Jiang
Keyword(s):  
Low Complexity ◽  
Adaptive Beamforming ◽  
Worst Case ◽  
Robust Adaptive Beamforming ◽  
Low Complexity Implementation ◽  
Robust Adaptive

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 Using a Low-Complexity Steering Vector Estimation and Covariance Matrix Reconstruction Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Pei Chen ◽  
Yongjun Zhao ◽  
Chengcheng Liu
Keyword(s):  
Covariance Matrix ◽  
Computational Cost ◽  
Reconstruction Algorithm ◽  
Low Complexity ◽  
Adaptive Beamforming ◽  
Superior Performance ◽  
Steering Vector ◽  
Robust Adaptive Beamforming ◽  
Steering Vector Estimation ◽  
Robust Adaptive

A novel low-complexity robust adaptive beamforming (RAB) technique is proposed in order to overcome the major drawbacks from which the recent reported RAB algorithms suffer, mainly the high computational cost and the requirement for optimization programs. The proposed algorithm estimates the array steering vector (ASV) using a closed-form formula obtained by a subspace-based method and reconstructs the interference-plus-noise (IPN) covariance matrix by utilizing a sampling progress and employing the covariance matrix taper (CMT) technique. Moreover, the proposed beamformer only requires knowledge of the antenna array geometry and prior information of the probable angular sector in which the actual ASV lies. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm and prove that this algorithm can achieve superior performance over the existing RAB methods.

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.

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