Super-Gaussian loading for robust beamforming

Authors:
Jing Gu;Patrick J. Wolfe
Affiliations:
Harvard University, Cambridge, MA;Harvard University, Cambridge, MA
Venue:
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Year:
2009

Citing 10
Cited 0

Finite Algorithms in Optimization and Data Analysis

Finite Algorithms in Optimization and Data Analysis
Automatic robust adaptive beamforming via ridge regression

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Performance analysis of beamformers using generalized loading of the covariance matrix in the presence of random steering vector errors

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Theory and application of covariance matrix tapers for robustadaptive beamforming

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Finite sample size effect on minimum variance beamformers: optimum diagonal loading factor for large arrays

IEEE Transactions on Signal Processing
Performance analysis of the minimum variance beamformer

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Performance analysis of the minimum variance beamformer in thepresence of steering vector errors

IEEE Transactions on Signal Processing
On robust Capon beamforming and diagonal loading

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A Bayesian approach to robust adaptive beamforming

IEEE Transactions on Signal Processing
Robust adaptive beamforming using worst-case performance optimization: a solution to the signal mismatch problem

IEEE Transactions on Signal Processing

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Abstract

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 l2 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 lp 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.