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Robust adaptive beamforming using a Bayesian steering vector error model
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AI
DOI：10.1016/j.sigpro.2012.12.018.png)
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We propose a Bayesian approach to robust adaptive beamforming which entails considering the steering vector of interest as a random variable with some prior distribution. The latter can be tuned in a simple way to reflect how far is the actual steering vector from its presumed value. Two different priors are proposed, namely a Bingham prior distribution and a distribution that directly reveals and depends upon the angle between the true and presumed steering vector. Accordingly, a non-informative prior is assigned to the interference plus noise covariance matrix R, which can be viewed as a means to introduce diagonal loading in a Bayesian framework. The minimum mean square distance estimate of the steering vector as well as the minimum mean square error estimate of R are derived and implemented using a Gibbs sampling strategy. Numerical simulations show that the new beamformers possess a very good rate of convergence even in the presence of steering vector errors. (c) 2012 Elsevier B.V. All rights reserved.
Keyword:
Robust adaptive beamforming
Bayesian estimation
Bingham distribution
Gibbs sampling
期刊
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3.6
论文数:
10.0K
被引数:
1.7W
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