Iterative Robust Capon Beamforming with Adaptively Updated Array Steering Vector Mismatch Levels. (3rd November 2014)

• Record Type: Journal Article
• Authors: Zhang, Tao; Sun, Liguo
• Other Names: Liu Wei Academic Editor.
• Is Part Of: International scholarly research notices. Volume 2014(2014)
• Publication Date: 2014-11-03
• Subjects: Medical sciences -- Periodicals; Life sciences -- Periodicals; Medicine -- Periodicals; Science -- Periodicals; Biomedical Technology; Biological Science Disciplines; Medicine; Life sciences; Medical sciences; Medicine; Science; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; Electronic journals
• DOI: 10.1155/2014/260875
• Languages: English
• ISSNs: 2356-7872

Abstract

Abstract : The performance of the conventional adaptive beamformer is sensitive to the array steering vector (ASV) mismatch. And the output signal-to interference and noise ratio (SINR) suffers deterioration, especially in the presence of large direction of arrival (DOA) error. To improve the robustness of traditional approach, we propose a new approach to iteratively search the ASV of the desired signal based on the robust capon beamformer (RCB) with adaptively updated uncertainty levels, which are derived in the form of quadratically constrained quadratic programming (QCQP) problem based on the subspace projection theory. The estimated levels in this iterative beamformer present the trend of decreasing. Additionally, other array imperfections also degrade the performance of beamformer in practice. To cover several kinds of mismatches together, the adaptive flat ellipsoid models are introduced in our method as tight as possible. In the simulations, our beamformer is compared with other methods and its excellent performance is demonstrated via the numerical examples.