2011
DOI: 10.1109/tsp.2010.2096222
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Iterative Robust Minimum Variance Beamforming

Abstract: Based on worst-case performance optimization, the recently developed adaptive beamformers utilize the uncertainty set of the desired array steering vector to achieve robustness against steering vector mismatches. In the presence of large steering vector mismatches, the uncertainty set has to expand to accommodate the increased error. This degrades the output signal-to-interference-plus-noise ratios (SINRs) of these beamformers since their interference-plus-noise suppression abilities are weakened. In this pape… Show more

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Cited by 112 publications
(86 citation statements)
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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%
“…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%
“…It is easy to expressã S by R and w from (26) [26] a S = α Rw WCB (27) where α = αã H S R −1ã S . The equivalent steering vectorã S should be scaled by the fact that the norm ofã S equals √ M, that is…”
Section: Feedback Loop Relationship Between Steering Vector and Weighmentioning
confidence: 99%
“…Firstly, when SNR of desired signal is very low, the updated steering vector cannot be able to converge to its actual value, even if it may converge to interferences or noise peaks. To avoid the desired signal's steering vector deviating its actual value too large, we add the following stopping criterion 2 [27] …”
Section: The Stopping Criterionmentioning
confidence: 99%
“…to the points in Z, to form the setZ, (c) use techniques in, e.g., [22], to solve (20) for M and g, (d) the "Flat MVE" is defined by B = U 1 M −1 and centerā = Bg + z 1 .…”
Section: B Flat Ellipsoidal Uncertainty Setsmentioning
confidence: 99%