Robust Adaptive Beamforming Based on Linearly Modified Atomic-Norm Minimization With Target Contaminated Data

Zhang, Xinyu; Jiang, Weidong; Huang, Kai; Liu, Yongxiang; Li, Xiang

doi:10.1109/tsp.2020.3021257

Cited by 19 publications

(6 citation statements)

References 36 publications

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“…Remark The proposed method just needs to solve the power of the signals instead of their long complex amplitude vectors; thus our method works more efficiently than the method proposed in [46]. Moreover, unlike the practice in [45], this method can interpolate the virtual array and estimate the relevant parameters simultaneously based on the sparse prior, thereby getting rid of the bottlenecks in spatial‐spectrum‐based methods.…”

Section: Proposed Robust Beamforming Methodsmentioning

confidence: 99%

“…Motivated by the intrinsic sparsity of interferences [20,46], sparse recovery techniques are considered to enhance the noise robustness of the estimation without any prior information of interferences. Concretely, the virtual array interpolation is first introduced to exploit all DOFs of the CPA, which is subsequently transformed to a matrix completion issue via a Toeplitz step.…”

Section: Introductionmentioning

confidence: 99%

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Coprime array‐adaptive beamforming via atomic‐norm‐based sparse recovery

Cheng

Zhang

Liu

et al. 2021

IET Radar Sonar & Navi

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Coprime arrays (CPAs) have been found to be an effective configuration for adaptive beamforming (ABF). However, most ABF methods for CPAs are embedded with the spatial spectrum estimation technique, and therefore they usually deteriorate in the case of low signal-to-noise ratio (SNR), especially without prior information of interferences. To address this issue, a robust ABF method for CPAs via atomic-norm-based sparse recovery is proposed. This method begins with initialised virtual array interpolation to avoid aperture loss caused by 'holes', which is subsequently transformed to a matrix completion and denoising issue via a Toeplitz step. With the matrix's Hermitian Toeplitz structure and the intrinsic sparsity of interferences, sparse recovery for the interference covariance matrix is introduced by subtracting the target signal and noise from the received signal and implemented by atomic-norm minimisation. Concretely, this non-convex process is decomposed into two convex steps and solved iteratively. Unlike previous methods, this method can interpolate the virtual array and estimate the steering vector of the target and the noise power simultaneously; consequently, the interference-plus-noise covariance matrix is reconstructed with merely some trivial entry selections without the need for the spatial spectrum. Furthermore, the optimality conditions and boundedness in this method are proven theoretically. Simulation results demonstrate the effectiveness of the proposed method and its advantages over previous methods under those non-ideal conditions.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Proposed Robust Beamforming Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coprime array‐adaptive beamforming via atomic‐norm‐based sparse recovery

Cheng

Zhang

Liu

et al. 2021

IET Radar Sonar & Navi

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“…General RAB design strategies can be executed via two routes; one is to modify the covariance matrix, and the other is to update the SOI SV. The former route includes diagonal loading (DL) [7][8][9][10][11][12][13][14][15][16] and INCM reconstruction [17][18][19][20][21][22][23][24][25][26][27][28]. The latter route includes eigen-subspace projection (ESP) [29][30][31][32][33][34][35][36] and SV estimation (SVE) [37][38][39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Sequenced Steering Vector Estimation for Eigen-Subspace Projection-Based Robust Adaptive Beamformer

Chen

Sheng

2023

Electronics

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Robust adaptive beamforming (RAB) is essential in many applications to ensure signal-receiving quality when model errors exist. Eigen-subspace projection (ESP), one of the most popular RAB methods, can be used when there are arbitrary model errors. However, a major challenge of ESP is projection subspace selection. Traditional ESP (TESP) treats the signal subspace as the projection subspace; thus, source enumeration is required to obtain prior information. Another inherent defect is its poor performance at low signal-to-noise ratios (SNRs). To overcome these drawbacks, two improved ESP-based RAB methods are proposed in this study. Considering that a reliable signal-of-interest steering vector needs to be obtained via the subspace projection, the main idea underlying the proposed methods is to use sequenced steering vector estimation to invert the subspace dimension estimate for an arranged eigenvector matrix. As the proposed methods do not require source enumeration, they are simple to implement. Numerical examples demonstrate the effectiveness and robustness of the proposed methods in terms of output signal-to-interference-plus-noise ratio performance. Specifically, compared with TESP, the proposed methods present at least a 2.6 dB improvement at low SNRs regardless of the error models.

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“…Herein, the atomic norm-based optimization method does not necessitate prior knowledge of the target and interference guiding vectors. Furthermore, this method separates the interference subspace from the data, which can approach the optimal output signal-to-noise ratio performance [32][33][34]. However, this method also requires the determination of hyper-parameters during the solution process, and the choice of hyper-parameters has a significant impact on the performance of the method [35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

ADMM-Net for Beamforming Based on Linear Rectification with the Atomic Norm Minimization

Gong,

Zhang,

Ren

et al. 2023

Remote Sensing

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Target misalignment can cause beam pointing deviations and degradation of sidelobe performance. In order to eliminate the effect of target misalignment, we formulate the jamming sub-space recovery problem as a linearly modified atomic norm-based optimization. Then, we develop a deep-unfolding network based on the alternating direction method of multipliers (ADMM), which effectively improves the applicability and efficiency of the algorithm. By using the back-propagation process of deep-unfolding networks, the proposed method could optimize the hyper-parameters in the original atomic norm. This feature enables the adaptive beamformer to adjust its weight according to the observed data. Specifically, the proposed method could determine the optimal hyper-parameters under different interference noise matrix conditions. Simulation results demonstrate that the proposed network could reduce computational cost and achieve near-optimal performance with low complexity.

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Robust Adaptive Beamforming Based on Linearly Modified Atomic-Norm Minimization With Target Contaminated Data

Cited by 19 publications

References 36 publications

Coprime array‐adaptive beamforming via atomic‐norm‐based sparse recovery

Coprime array‐adaptive beamforming via atomic‐norm‐based sparse recovery

Sequenced Steering Vector Estimation for Eigen-Subspace Projection-Based Robust Adaptive Beamformer

ADMM-Net for Beamforming Based on Linear Rectification with the Atomic Norm Minimization

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