.png){kind=icon}
Return
Robust adaptive beamforming using a Bayesian steering vector error model
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2012.12.018.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Robust adaptive beamforming
Bayesian estimation
Bingham distribution
Gibbs sampling
Journal
IF:
3.6
Papers:
10.0K
Citations:
1.7W
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSaveShareSave
View more