NDLI: Robust and rapid converging adaptive beamforming via a subspace method for the signal-plus-interferences covariance matrix estimation

Content Provider	IET Digital Library
Author	Rahmani, Mostafa Bastani, Mohammad Hasan
Abstract	The presence of the desired signal (DS) in the training snapshots makes the adaptive beamformer sensitive to any steering vector mismatch and dramatically reduces the convergence rate. Even the performance of the most of the existing robust adaptive beamformers is degraded when the signal-to-noise ratio (SNR) is increased. In this study, a high converging rate robust adaptive beamformer is proposed. This method is a promoted eigenspace-based beamformer. In this paper, a new signal-plus-interferences (SPI) covariance matrix estimator is proposed. The subspace of the ideal SPI covariance matrices is exploited and the estimated covariance matrix is projected into this subspace. This projection effectively reduces the covariance matrix estimation error and the proposed estimator yields a more accurate estimation of the SPI covariance matrix. In addition, a computationally efficient steering vector estimator has been proposed. To prevent the absence of the DS steering vector in the estimated SPI subspace, the estimated SPI covariance matrix is compensated. Hence, the proposed method can attain the optimal beamformer in the both high and low SNR cases. The numerical examples indicate that this method has excellent signal-to-interference plus noise ratio performance and offers a higher converging rate compared with the existing robust adaptive beamforming algorithms.
Starting Page	507
Ending Page	520
Page Count	14
ISSN	17519675
Volume Number	8
e-ISSN	17519683
Issue Number	Issue 5, Jul (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/8/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2013.0298
Journal	IET Signal Processing
Publisher Date	2014-01-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Signal Processing Algebra Array Signal Processing Convergence Rate Covariance Matrices Desired Signal DS Steering Vector Eigen Values And Eigen Function Eigenspace-based Beamformer Estimator Yields High Converging Rate Robust Adaptive Beamformer Interference Signal Processing And Detection Signal Processing Theory Signal-plus-interference Covariance Matrix Estimation Signal-to-interference Plus Noise Ratio Performance SNR SPI Covariance Matrix Estimator Steering Vector Estimator Mismatch SubSpace Method Training Snapshots
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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