2017
DOI: 10.1109/access.2017.2725450
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Robust Adaptive Beamforming Using Noise Reduction Preprocessing-Based Fully Automatic Diagonal Loading and Steering Vector Estimation

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Cited by 31 publications
(15 citation statements)
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“…Moreover, the robustness of binary tree SVMs is also very strong [17]. The combination of the two can effectively extract and classify the fault information of electronic circuits, confirming the feasibility and effectiveness of the method [18][19][20][21][22].…”
Section: Analysis Of Fault Diagnosis Results Of Ism Dae-svm Electronic Circuitmentioning
confidence: 89%
“…Moreover, the robustness of binary tree SVMs is also very strong [17]. The combination of the two can effectively extract and classify the fault information of electronic circuits, confirming the feasibility and effectiveness of the method [18][19][20][21][22].…”
Section: Analysis Of Fault Diagnosis Results Of Ism Dae-svm Electronic Circuitmentioning
confidence: 89%
“…The Doppler filter bank or partial pulse selection can be completed by means of the dimension reduction matrix T a [10]. Meanwhile, in order to guarantee the reversibility of the clutter temporal covariance matrix, the diagonal loading technique [23] must be used. Therefore, the temporal covariance matrix of the lth range cell can be estimated as…”
Section: A Adaptive Segmentation Of Nonstationary Clutter Regionmentioning
confidence: 99%
“…The adaptive beamforming problem [6,13,14] is essentially to design the optimal weight vector w that minimizes the interference-plus-noise output power while maintaining unity response of the desired signal. min…”
Section: Steering Vector Mismatch Estimation For R-iaslc Algorithmmentioning
confidence: 99%
“…In these years, improving the robustness of the beamformer against the mismatch of SV is becoming an essential requirement, and several contributions have been proposed to work on it [6,7]. Against the direction of arrival (DOA) [8,9] mismatch, the most common technique is to delimit one set of unity-gain constraints for a small range of angles around the presumed look direction.…”
Section: Introductionmentioning
confidence: 99%