Robust Capon Beamforming against Steering Vector Error Dominated by Large Direction-of-Arrival Mismatch for Passive Sonar

Yu, Haohui; Zou, Nan; Liang, Guolong

doi:10.3390/jmse7030080

Cited by 13 publications

(6 citation statements)

References 21 publications

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“…As a spatial filtering method, beamforming has attracted wide attention in the past years due to its importance to radar [1], wireless communication [2], sonar [3], medical imaging [4], astronomy [5], and mapping [6]. The data received by each array element in conventional beamforming (CBF) is delayed and summed up to achieve different gains for signals from different orientations.…”

Section: Introductionmentioning

confidence: 99%

Notice of Violation of IEEE Publication Principles: Robust Adaptive Beamforming Using Support Vector Machines

et al. 2020

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A robust adaptive beamformer based on the support vector machines is proposed to solve the rapid degradation of the performance of minimum variance distortionless response beamformer when the steering vector is mismatched. The proposed beamformer assumes that the angle error range in the steering vector can be estimated; the array only responds within this range, and the array response outside the angle error range is considered as the abnormal response. The proposed method is expressed in the form of support vector regression, and Vapnik's  -insensitive quadratic loss function which can improve the robustness of beamformer is used as a regular term to punish the abnormal response. The assumption that the array only responds within a certain angle range reflects the sparsity of support vector regression and improves the ability of the algorithm against steering vector mismatch because the energy conservation theorem holds that the response is large. The regular term ensures that a deep null exists in the direction of interference, the interference can be effectively suppressed. The iterative reweighted least square procedure can converge to the real solution of the SVM regression problem and is easy to calculate; thus, it is used to solve the algorithm proposed in this study. The effectiveness of the algorithm is verified through computer simulations.

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Section: Introductionmentioning

confidence: 99%

Notice of Violation of IEEE Publication Principles: Robust Adaptive Beamforming Using Support Vector Machines

et al. 2020

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“…When large errors occur, 1/ā H 0 R−1 ā0 is no longer equal to the SOI power but will be dominated by noise power. [49].…”

Section: Problem Background and The Signal Modelmentioning

confidence: 99%

Maximum Entropy-Based Interference-Plus-Noise Covariance Matrix Reconstruction for Robust Adaptive Beamforming

Mohammadzadeh

Nascimento

Lamare

et al. 2020

IEEE Signal Process. Lett.

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In this letter, we present a novel low-complexity adaptive beamforming technique using a stochastic gradient algorithm to avoid matrix inversions. The proposed method exploits algorithms based on the maximum entropy power spectrum (MEPS) to estimate the noise-plus-interference covariance matrix (MEPS-NPIC) so that the beamforming weights are updated adaptively, thus greatly reducing the computational complexity. MEPS is further used to reconstruct the desired signal covariance matrix and to improve the estimate of the desired signals's steering vector (SV). Simulations show the superiority of the proposed MEPS-NPIC approach over previously proposed beamformers.

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“…As an array signal processing method, beamforming has been widely used in many fields, such as communication [1], radar [2], sonar [3], imaging [4], mapping [5], and remote sensing [6]. In the conventional beamforming (CBF) method, the signals from different directions are delayed differently, and then all the delayed signals are summed up such that the signals from different directions obtain different magnifications.…”

Section: Introductionmentioning

confidence: 99%

Robust Beamforming Based on Weighted Vector Norm Regularization

Ren

Wang

Zhang³

et al. 2021

IEEE Access

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Steering vector mismatch rapidly degrades the performance of the minimum variance distortionless response beamformer. To solve this problem, a robust beamforming method based on weighted vector norm regularization is proposed. First, the factors affecting the robustness of the beamformer are analyzed. Second, by introducing the weighted vector norm, an optimization problem is constructed to increase the robustness of the beamformer. Furthermore, the regularization coefficient is provided to achieve a balance between the output power and the robustness of the beamformer. Meanwhile, a method of finding the appropriate regularization coefficient is provided. Then, simulations of an irregular arc array are carried out, showing that the proposed method is robust to the snapshot number. Finally, the results and data analysis indicate the effectiveness of the proposed method.

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Robust Capon Beamforming against Steering Vector Error Dominated by Large Direction-of-Arrival Mismatch for Passive Sonar

Cited by 13 publications

References 21 publications

Notice of Violation of IEEE Publication Principles: Robust Adaptive Beamforming Using Support Vector Machines

Notice of Violation of IEEE Publication Principles: Robust Adaptive Beamforming Using Support Vector Machines

Maximum Entropy-Based Interference-Plus-Noise Covariance Matrix Reconstruction for Robust Adaptive Beamforming

Robust Beamforming Based on Weighted Vector Norm Regularization

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