Fast and robust adaptive beamforming algorithms for large-scale arrays with small samples

•A fast and robust beamforming algorithm for the large scale sensor array under small sample case.•Low computational complexity and easily implemented.•An efficiently and free parameter loading factor calculation method.•The performance of the proposed algorithm is superior to that of many other rob...

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Bibliographic Details
Published in:Signal processing Vol. 188; p. 108223
Main Authors: Zhang, Xue-Jun, Xie, Hu, Feng, Da-Zheng, Zheng, Wei Xing, Hu, Hao Shuang
Format: Journal Article
Language:English
Published: Elsevier B.V 01.11.2021
Subjects:
Adaptive beamforming algorithm
Computation complexity
Large-scale sensor array
Linear kernel approach
Regularization technique
Large-scale sensor array
Adaptive beamforming algorithm
Computation complexity
Regularization technique
Linear kernel approach
ISSN:0165-1684, 1872-7557
Online Access:Get full text
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Summary:•A fast and robust beamforming algorithm for the large scale sensor array under small sample case.•Low computational complexity and easily implemented.•An efficiently and free parameter loading factor calculation method.•The performance of the proposed algorithm is superior to that of many other robust adaptive beamforming algorithms. The adaptive beamformer of large-scale sensor array mainly suffers from two limits. One limit is an insufficient number of training snapshots, which usually results in an ill-posed sample covariance matrix in many real applications. The other limit is the high computation complexity of the beamformer that severely restricts its online processing. To overcome these two limits, two fast and robust adaptive beamforming algorithms are proposed in this paper, which refers to the linear kernel approaches and formulates the weight vector as a linear combination of the training samples and the signal steering vector. The proposed algorithms only need to calculate a low-dimensional combination vector instead of the high-dimensional adaptive weight vector, which remarkably reduces the computation complexity. Moreover, regularization techniques are utilized to suppress the excessive variation of the combination vector caused by an underdetermined estimation of the Gram matrix. Experimental results show that the proposed algorithms achieve better performance and lower computation complexity than algorithms in the literature. Especially, like the kernel approaches, the proposed algorithms achieve good performance under the small sample case.
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2021.108223