Measure-Transformed MVDR Beamforming

This letter deals with the problem of robust beamforming in the presence of non-Gaussian impulsive noise. Under this framework, a new robust extension of the empirical MVDR beamformer is developed. The proposed extension is a plug-in estimate of a measure-transformed MVDR (MT-MVDR) beamformer, that operates by applying a transform to the probability distribution of the data. The considered transform is generated by a non-negative data-weighting function, called MT-function. We show that proper selection of the MT-function can result in significantly enhanced beamforming performance in the presence of impulsive noise, while maintaining the implementation simplicity of the empirical MVDR. The proposed beamformer is evaluated in simulation studies that illustrate its advantages as compared to the empirical MVDR and other robust alternatives.

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