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GLOBECOM
2009
IEEE

Super-Gaussian Loading for Robust Beamforming

14 years 2 months ago
Super-Gaussian Loading for Robust Beamforming
— It is well known that the performance of adaptive beamformers may degrade in the presence of steering errors. In this context, diagonal loading is one of the most popular methods used for robust beamforming, and can be derived from an 2 norm constraint. Equivalently, this method assumes a white Gaussian prior on the beamforming vector, similar to ridge regression in statistical point of view. By changing the loading level, which can be treated as confidence of this prior distribution, a trade-off between robustness and adaptivity is obtained. In this article, we generalize this approach via p norms. We find that under different settings, it is not optimal to set p = 2 compared with other p ∈ [1, 2] with the loading level chosen in such a way that the prior variance is maintained. We derive an iterative form to calculate the beamformer, as well as an iterative online implementation. Convergence is observed in empirical simulations and discussed under certain conditions.
Jing Gu, Patrick J. Wolfe
Added 04 Sep 2010
Updated 04 Sep 2010
Type Conference
Year 2009
Where GLOBECOM
Authors Jing Gu, Patrick J. Wolfe
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