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

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 s...

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Bibliographic Details
Published in:International journal of antennas and propagation 2016-01, Vol.2016 (2016), p.1-9
Main Authors: Chen, Pei, Liu, Chengcheng, Zhao, Yongjun
Format: Article
Language:English
Subjects:
Adaptive algorithms
Algorithms
Arrays
Beamforming
Covariance matrix
Economic models
Mathematical analysis
Noise
Simulation
Software
Steering
Vectors (mathematics)
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Summary: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.
ISSN:1687-5869
1687-5877
DOI:10.1155/2016/2438183