Doubly constrained robust Capon beamformer

Li, Jian; Stoica, Petre; Wang, Zhisong

doi:10.1109/tsp.2004.831998

Cited by 366 publications

(148 citation statements)

References 22 publications

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“…However, this seemingly surprising should be re-examined under the following grounds. It has been noted recently that negative diagonal loading may outcome as a possible solution to the doubly constrained robust Capon beamformer of [7]. Hence, negative diagonal loading might not be such an unexpected result.…”

Section: Optimisation For a Given Steering Vector Errormentioning

confidence: 97%

“…In the worst-case approaches [4 -7], the loading level depends on the size of the ellipsoid in which the actual steering vector is expected to lie. However, it is not clear how to choose the ellipsoid's size although some proposals are hinted at in [7]. A meaningful way of selecting the loading level is to fix the white noise gain (WNG), as suggested in [8], since diagonal loading corresponds to constraining the WNG.…”

Section: Introductionmentioning

confidence: 99%

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Steering vector errors and diagonal loading

Vincent¹,

Besson²

2004

IEE Proc., Radar Sonar Navig.

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Diagonal loading is one of the most widely used and effective methods to improve robustness of adaptive beamformers. The authors consider its application to the case of steering vector errors, i.e. when there exists a mismatch between the actual steering vector of interest and the presumed one. More precisely, the problem addressed is that of optimally selecting the loading level with a view to maximising the signal-to-interference-plus-noise ratio in the presence of steering vector errors. First, an expression is derived for the optimal loading for a given steering vector error and it is shown that this loading is negative. Next, random steering errors are considered and the optimal loading is averaged with respect to the probability density function of the steering vector errors, yielding a very simple expression for the average optimal loading. Numerical simulations attest to the validity of the analysis and show that diagonal loading with the derived optimal loading factor provides a performance close to optimum.

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Section: Optimisation For a Given Steering Vector Errormentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Steering vector errors and diagonal loading

Vincent¹,

Besson²

2004

IEE Proc., Radar Sonar Navig.

View full text Add to dashboard Cite

show abstract

“…(26) is 0.112, and the parameter of the norm constraint η is set to 0.118 according to (28). The parameter of the spherical uncertainty set in DCRCB is set as recommended in [19] ≥ max…”

Section: Simulation Resultsmentioning

confidence: 99%

“…An approach based on the optimization of the worst-case performance of the robust beamfomer (WCPRB) in the spherical uncertainty set of steering vector of SOI is developed in [18]. The doubly constrained robust Capon beamformer (DCRCB) which is introduced in [19] achieves robustness against DOA mismatch by enforcing a double constraint on the array steering vector, viz. a constant norm constraint and a spherical uncertainty set constraint on the steering vector of SOI.…”

Section: Introductionmentioning

confidence: 99%

Magnitude Constraint Minimum Variance Beamformer With Conjugate Symmetric Constraint and Norm Constraint

Zhao¹,

Li²,

Liu³

2014

PIER M

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Abstract-In this paper, an improved robust minimum variance beamformer against direction of arrival (DOA) mismatch and finite sample effect is proposed. Multiple inequality magnitude constraints are imposed to broaden the main lobe of beampattern. The conjugate symmetric structure of the optimal weight is utilized to transform the non-convex inequality magnitude constraints into convex ones. A quadratic constraint on the norm of weight is introducing to make further improvement on robustness against DOA mismatch and finite sample effect. The proposed beamforming problem can be reformulated in the form of the second order cone programming and solved efficiently by interior point method. Simulation results show that the proposed beamformer outperforms several other adaptive beamformers.

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“…Li et al propose a Capon beamforming approach with the norm inequality constraint (NICCB) to improve the robustness against array steering vector errors and noise [19]. The exact solution is given, and optimal loading level can be computed via the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Robust LCMP Beamformer With Negative Loading

Liu¹,

Yang²

2012

PIER

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Abstract-The adaptive linearly constrained minimum power (LCMP) beamformer can improve the robustness of the Capon beamformer. And quadratic constraints on the weighting vector of the LCMP beamformer can improve the robustness to pointing errors and to random perturbations in sensor parameters. But how to solve it and how to select the constraint parameters are its key problems. In this paper, the Lagrange multiplier method is proposed to solve the LCMP beamformer under quadratic inequality constraint (QIC). The problem of finding the optimal weight vector is solved, and the choice of the quadratic constraint parameter is analyzed and the selected bound is also given. Since the quadratic equality constraint (QEC) is stronger than the quadratic inequality constraint (QIC), the performance of the QECLCMP beamformer is more robust than that of the QICLCMP beamformer. Therefore, the QECLCMP beamformer is proposed and is solved effectively. Numerical examples attest the correctness and the efficiency of the proposed algorithms. And the results show that the QECLCMP beamformer has the advantage of overcoming the steering vector mismatch, namely the optimal negative loading has the preferable robustness.

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Doubly constrained robust Capon beamformer

Cited by 366 publications

References 22 publications

Steering vector errors and diagonal loading

Steering vector errors and diagonal loading

Magnitude Constraint Minimum Variance Beamformer With Conjugate Symmetric Constraint and Norm Constraint

Robust LCMP Beamformer With Negative Loading

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