Sidelobe suppression for adaptive beamforming with sparse constraint on beam pattern

Zhang, Y.; Ng, Boon Poh; Wan, Qun

doi:10.1049/el:20080415

Cited by 44 publications

(42 citation statements)

References 4 publications

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“…Figure 1 shows beam patterns of the standard LCMV beamformer, the sparse LCMV beamformer (3), and the TD-LCMV beamformer (6) of 1000 Monte Carlo simulations. It is obvious that the best sidelobe suppression performance is achieved by the TD-LCMV beamformer (6). the TD-LCMV beamformer has the lowest array gain level in sidelobe area, and provides the deepest nulls in the directions of interference, i.e., −30 • , 30 • and 70 • .…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The sparse LCMV beamformer is designed to minimize the total array output energy, subject to a linear distortionless constraint on the SOI, and encourage spare distabution of the array gains in the beam patern, and the corresponding weighting vector of the is given by [6] w S = arg min…”

Section: The Proposed Beamformermentioning

confidence: 99%

“…In order to provide sidelobe suppression for an LCMV beamformer, a sparse constraint on the beam pattern was recently proposed in [6,7]. The sparse beam pattern is added on all the array gains in the beam pattern.…”

Section: Introductionmentioning

confidence: 99%

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Total Difference Based Partial Sparse LCMV Beamformer

Liu¹,

Wan²

2010

PIER Letters

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Abstract-Recent research demonstrates that sparse beam pattern constraint can suppress the sidelobe level of the linear constraint minimum variance beamformer. Here we improve the standard beam pattern by replacing it with a combination of a total difference minimization constraint on the mainlobe and a standard C 1 norm minimization constraint on the sidelobe. As the new constraint matches the practical beam pattern better, the sidelobe level is further suppressed, while the robustness against the mismatch between the steering angle and the direction of arrival (DOA) of the desired signal, is maintained.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: The Proposed Beamformermentioning

confidence: 99%

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Total Difference Based Partial Sparse LCMV Beamformer

Liu¹,

Wan²

2010

PIER Letters

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“…Our previous work have also illustrated its superiority to conventional methods in spectral estimation [13], DOA estimation in Laplacian noise environment [14] and array beamforming [15]. In this paper, we extend sparse solution finding to DOA estimation and mutual coupling calibration.…”

Section: Introductionmentioning

confidence: 86%

Localization of Narrow Band Sources in the Presence of Mutual Coupling via Sparse Solution Finding

Zhang¹,

Wan²,

Huang³

2008

PIER

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Abstract-Making use of the Toeplitz structure of the mutual coupling matrix of the Uniform Linear Array (ULA), estimating the direction-of-arrival (DOA) of the sources as well as the mutual coupling coefficients of the array can be formulated as a linear inverse problem, where the solution is given by the Kronecker product of the vectors with respect to the DOAs and the mutual coupling coefficients. Through mathematical manipulation, these solution vectors can be decoupled. Estimation of the DOAs is cast into the framework of sparse solution finding. To derive the solution, an alternating minimization technique is presented. The proposed method is firstly developed based on the noise free observation covariance matrix, and can be generalized to directly using the snapshots. Using the proposed method, DOA estimation is feasible even in single snapshot case. The performance of the proposed methods with covariance matrix, single snapshot and multiple snapshots are illustrated by computer simulations. Their ability to resolve closely spaced targets and the applicability to correlated sources have also been demonstrated.

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“…Sparsity enforcing has been shown to be beneficial in terms of sidelobe suppression of the beampattern [4]; this paper however considers a fundamentally different formulation [5]. We illustrate that when there is no additive noise in the array observations, the ideal steering weights can be represented sparsely in terms of the manifold vectors.…”

Section: Introductionmentioning

confidence: 99%

Manifold sparse beamforming

Gözcü¹,

Asaei²,

Cevher³

2013

2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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Abstract-We consider the minimum variance distortionless response (MVDR) beamforming problems where the array covariance matrix is rank deficient. The conventional approach handles such rank-deficiencies via diagonal loading on the covariance matrix. In this setting, we show that the array weights for optimal signal estimation can admit a sparse representation on the array manifold. To exploit this structure, we propose a convex regularizer in a grid-free fashion, which requires semi-definite programming. We then provide numerical evidence showing that the new formulation can significantly outperform diagonal loading when the regularization parameters are correctly tuned.

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Sidelobe suppression for adaptive beamforming with sparse constraint on beam pattern

Cited by 44 publications

References 4 publications

Total Difference Based Partial Sparse LCMV Beamformer

Total Difference Based Partial Sparse LCMV Beamformer

Localization of Narrow Band Sources in the Presence of Mutual Coupling via Sparse Solution Finding

Manifold sparse beamforming

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