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CLC number: TN911.7
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2016-10-19
Cited: 1
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Low complexity robust adaptive beamforming for general-rank signal model with positive semidefinite constraint
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Yu-Tang Zhu, Jun-yong Liu, Yong-Bo Zhao, Jun Liu, Peng-Lang Shui. Low complexity robust adaptive beamforming for general-rank signal model with positive semidefinite constraint[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(11): 1245-1252.
@article{title="Low complexity robust adaptive beamforming for general-rank signal model with positive semidefinite constraint",
author="Yu-Tang Zhu, Jun-yong Liu, Yong-Bo Zhao, Jun Liu, Peng-Lang Shui",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="17",
number="11",
pages="1245-1252",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601112"
}
%0 Journal Article
%T Low complexity robust adaptive beamforming for general-rank signal model with positive semidefinite constraint
%A Yu-Tang Zhu
%A Jun-yong Liu
%A Yong-Bo Zhao
%A Jun Liu
%A Peng-Lang Shui
%J Frontiers of Information Technology & Electronic Engineering
%V 17
%N 11
%P 1245-1252
%@ 2095-9184
%D 2016
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601112
TY - JOUR
T1 - Low complexity robust adaptive beamforming for general-rank signal model with positive semidefinite constraint
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A1 - Jun-yong Liu
A1 - Yong-Bo Zhao
A1 - Jun Liu
A1 - Peng-Lang Shui
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 17
IS - 11
SP - 1245
EP - 1252
%@ 2095-9184
Y1 - 2016
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1601112
Abstract: We propose a low complexity robust beamforming method for the general-rank signal model, to combat against mismatches of the desired signal array response and the received signal covariance matrix. The proposed beamformer not only considers the norm bounded uncertainties in the desired and received signal covariance matrices, but also includes an additional positive semidefinite constraint on the desired signal covariance matrix. Based on the worst-case performance optimization criterion, a computationally simple closed-form weight vector is obtained. Simulation results verify the validity and robustness of the proposed beamforming method.
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