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In this paper, we describe a fully automatic method using latent root regression based on the generalized sidelobe canceler (GSC) parameterization of the minimum variance beamformer. The proposed method gives a theoretically optimal solution in mean-squared error (MSE) sense (minimized MSE solution) by choosing a linear combination of individual latent root regression predictors in the GSC formulation...
In this letter, we propose shrinkage-based Capon (S-Capon) and APES (S-APES) spectral estimators by minimizing the mean-square error (MSE) of standard Capon and APES in a linear regression framework. The proposed methods are shown to give more accurate spectral estimates but lower resolution than the methods they based on. We combine Capon with the proposed S-Capon and S-APES to overcome the resolution...
In this paper, a novel robust adaptive beamformer based on principal component regression (PCR) is derived. Unlike many existing methods, the proposed method is completely automatic (or so-called parameter-free), which means, it do not need the choice of user parameters. The performance of our approach is illustrated by numerical simulations and compared to other robust adaptive beamformers. The simulation...
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