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Iterative Robust Capon Beamforming with Adaptively Updated Array Steering Vector Mismatch Levels

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 tra...

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Detalles Bibliográficos
Autores principales: Zhang, Tao, Sun, Liguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4897530/
https://www.ncbi.nlm.nih.gov/pubmed/27355008
http://dx.doi.org/10.1155/2014/260875
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author Zhang, Tao
Sun, Liguo
author_facet Zhang, Tao
Sun, Liguo
author_sort Zhang, Tao
collection PubMed
description 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.
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spelling pubmed-48975302016-06-28 Iterative Robust Capon Beamforming with Adaptively Updated Array Steering Vector Mismatch Levels Zhang, Tao Sun, Liguo Int Sch Res Notices Research Article 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. Hindawi Publishing Corporation 2014-11-03 /pmc/articles/PMC4897530/ /pubmed/27355008 http://dx.doi.org/10.1155/2014/260875 Text en Copyright © 2014 T. Zhang and L. Sun. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhang, Tao
Sun, Liguo
Iterative Robust Capon Beamforming with Adaptively Updated Array Steering Vector Mismatch Levels
title Iterative Robust Capon Beamforming with Adaptively Updated Array Steering Vector Mismatch Levels
title_full Iterative Robust Capon Beamforming with Adaptively Updated Array Steering Vector Mismatch Levels
title_fullStr Iterative Robust Capon Beamforming with Adaptively Updated Array Steering Vector Mismatch Levels
title_full_unstemmed Iterative Robust Capon Beamforming with Adaptively Updated Array Steering Vector Mismatch Levels
title_short Iterative Robust Capon Beamforming with Adaptively Updated Array Steering Vector Mismatch Levels
title_sort iterative robust capon beamforming with adaptively updated array steering vector mismatch levels
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4897530/
https://www.ncbi.nlm.nih.gov/pubmed/27355008
http://dx.doi.org/10.1155/2014/260875
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