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    A Distributionally Robust Minimum Variance Beamformer Design

    Access Status
    Fulltext not available
    Authors
    Li, Bin
    Rong, Yue
    Sun, Jie
    Teo, Kok Lay
    Date
    2017
    Collection
    • Curtin Research Publications
    Type
    Journal Article
    Metadata
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    Abstract

    IEEE This paper is concerned with a robust minimum variance beamformer design. To hedge the mismatch between the true and the assumed steering vectors, a distributionally robust beamformer (DR-beamformer) is proposed. The tractable reformulation of this beamformer is developed. Compared with the existing robust beamformers (worst-case robust beamformer, Gaussian robust beamformer), the proposed robust beamformer does not assume full knowledge of the channel mismatch. Therefore, it is more flexible in practice and more general in formulation. In addition, the relationships of the proposed robust beamformer with the existing ones are investigated. The performance gain of the DR-beamformer over the other robust beamformers is highlighted through numerical simulations.

    Citation
    Li, B. and Rong, Y. and Sun, J. and Teo, K.L. 2017. A Distributionally Robust Minimum Variance Beamformer Design. IEEE Signal Processing Letters.
    Source Title
    IEEE Signal Processing Letters
    URI
    http://hdl.handle.net/20.500.11937/62307
    DOI
    10.1109/LSP.2017.2773601
    Department
    Department of Mathematics and Statistics

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