A simple tridiagonal loading method for robust adaptive beamforming

Zhang, Ming; Chen, Xiaoming; Zhang, Anxue

doi:10.1016/j.sigpro.2018.11.019

Cited by 36 publications

(28 citation statements)

References 17 publications

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“…However, the optimal loading factor is hard to be selected. Although some robust Capon beamforming techniques [8, 9] are developed to compute the loading parameter automatically, they cannot provide good performance in high signal‐to‐noise ratio (SNR) cases. Eigenspace‐based techniques [10, 11] project the presumed desired signal steering vector (DSSV) into the signal‐plus‐interference subspace, which can attenuate the pointing error effect and can converge fast.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive beamforming for coprime array with steering vector estimation and covariance matrix reconstruction

Meng

Zhou

2020

IET Communications

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Section: Introductionmentioning

confidence: 99%

Robust adaptive beamforming for coprime array with steering vector estimation and covariance matrix reconstruction

Meng

Zhou

2020

IET Communications

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“…Those non-ideal factors will cause the mismatch between the actual and the presumed signal steering vector, which will seriously degrade the performance of traditional Capon beamforming [3].Therefore, there are various techniques developed to against mismatch problem. Popular approaches include diagonally loaded sample-matrix inversion (LSMI) method [4][5][6][7], eigenspace-based method [8,9], uncertainty set-based technique [10][11][12][13][14][15] and extended linearly constrained minimum variance technique [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…The LSMI method [4][5][6][7] and the eigenspace-based method [8,9] are quiet efficient in enhancing the robustness of the Capon beamformer. However, there is no guidance to accurately choose the diagonal loading factor.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive beamforming based on the direct biconvex optimization modeling

Zou

2020

Preprint

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It is well known that the performance of the minimum variance distortionless response beamformer is sensitive to steering vector mismatch, which motivates the development of robust adaptive beamforming(RAB). However, robust adaptive beamforming (RAB) is usually modeled as a nonconvex optimization problem. The most state-of-art methods solve it indirectly by approximating the nonconvex problem to the convex optimization problem, which causes the approximation errors and performance degradation. To circumvent this problem, a novel method that is against the mismatch of the signal look direction errors, which reformulates RAB as the biconvex form directly, is proposed. This method imposes ideal response constraints to guarantee the gain of the angular region in which the actual signal lies and suppresses the signals in the remaining region, and constructs a four-order problem. Then, an auxiliary variable is introduced to reformulate it as a biconvex problem without approximation process, which can be efficiently solved iteratively by the alternating direction method of multipliers (ADMM) algorithm. Simulation results show that the proposed method can obtain a better performance on the signal-to-interference-plus-noise (SINR) and flexible control of error range.

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“…To deal with these problems, many robust beamformers have been presented in recent years. These beamformers can be divided into the following categories: the diagonal loading (DL) methods [2–9], the eigenspace‐based methods [10–14], the sparse reconstruction methods [15–18], and the INCM reconstruction‐based [19–23] methods.…”

Section: Introductionmentioning

confidence: 99%

“…In [8], the identity matrix is replaced by the knowledge‐aided matrix to obtain the final INCM. In [9], Zhang et al use a symmetric tridiagonal matrix expanding the DL to the tridiagonal loading, which we believe that would bring further development to the loading matrix for constructing a suitable INCM. However, the performance of these DL methods degrades when the signal is contained in the sample snapshots.…”

Section: Introductionmentioning

confidence: 99%