Robust minimum variance beamforming

Lorenz, Robert; Boyd, Stephen

doi:10.1109/tsp.2005.845436

Cited by 676 publications

(381 citation statements)

References 27 publications

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“…A similar effect occurs in the case that the error between ideal array covariance matrix and estimated array covariance matrix is too large when the number of snapshots used to estimate the array covariance matrix is small [7,8]. Thus, many techniques called robust Capon beamformer (RCB) have been proposed to improve the robustness of the standard Capon beamformer against the DOA mismatch and estimation error of the array covariance matrix in the past decades ( [9][10][11][12][13][14][15][16], and many references therein).…”

Section: Introductionmentioning

confidence: 94%

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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Section: Introductionmentioning

confidence: 94%

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

Zhao¹,

Li²,

Liu³

2014

PIER M

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“…Similar ideas of using the worst-case robust optimization have been successfully applied to related signal processing problems such as: robust filtering [18], [19], robust parameter estimation [20], [21], robust matched filtering [22], [23], robust minimum variance beamforming [24]- [28].…”

Section: Related Workmentioning

confidence: 99%

Robust Chebyshev FIR Equalization

Mutapcic

Kim

Boyd

2007

IEEE GLOBECOM 2007-2007 IEEE Global Telecommunications Conference

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Abstract-In Chebyshev finite-impulse response (FIR) equalization, we design an FIR filter that minimizes the Chebyshev equalization error, i.e., the maximum absolute deviation between the equalized and the desired frequency response functions, assuming the unequalized response function is known exactly. In robust Chebyshev FIR equalization, we take into account uncertainty in the unequalized response function, described as a set of possible values for the unequalized response at each frequency, by designing an FIR filter that minimizes worst-case Chebyshev equalization error over all possible unequalized response functions. When the uncertainty in unequalized response function is described by a complex uncertainty ellipsoid, at each frequency, we show that the robust Chebyshev FIR equalization design problem can be formulated as a semidefinite program (SDP), and therefore efficiently (and globally) solved. When the uncertainty is given by a complex disk, the design problem can be formulated as a second-order cone program (SOCP), which can be solved almost as fast as the nominal Chebyshev equalization problem (ignoring uncertainty). The robust equalizer design method is demonstrated with a numerical example.

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“…Furthermore, in [12], an analytical expression of the optimal loading factor is derived by maximizing the output SINR in the presence of random steering vector error, the main disadvantage is that the obtained negative loading factor may lead to a rank-deficient problem if it equals an eigenvalue of the correlation matrix. On the other hand, it follows from [13][14][15] that the loading factor can be calculated based on the uncertainty set of the steering vector. However, one still needs to specify the parameter related to the size of the uncertainty set, and it may be difficult to choose the parameter in practice.…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Beamforming Using a Fully Data-Dependent Loading Technique

Lee

2012

PIER B

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Abstract-This paper deals with adaptive array beamforming in the presence of errors due to steering vector mismatch and finite sample effect. Diagonal loading (DL) is one of the widely used techniques for dealing with these errors. However, the main drawback of DL techniques is that there is not an easy and reliable manner to determine the required loading factor. Recently, serval DL approaches proposed the so-called automatic scheme on computing the required loading factor. In this paper, we propose a fully data-dependent loading to overcome the difficulties. The novelty is that the proposed method does not require any additional sophisticated scheme to choose the required loading. The loading factor can be completely obtained from the received array data. Analytical formulas for evaluating the performance of the proposed method under random steering vector error are further derived. Simulation results are provided to confirm the validity of the proposed method and make comparison with the existing DL methods.

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Robust minimum variance beamforming

Cited by 676 publications

References 27 publications

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

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

Robust Chebyshev FIR Equalization

Robust Adaptive Beamforming Using a Fully Data-Dependent Loading Technique

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