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A High-Resolution and Low-Complexity DOA Estimation Method with Unfolded Coprime Linear Arrays

The direction-of-arrivals (DOA) estimation with an unfolded coprime linear array (UCLA) has been investigated because of its large aperture and full degrees of freedom (DOFs). The existing method suffers from low resolution and high computational complexity due to the loss of the uniform property an...

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Autores principales: He, Wei, Yang, Xiao, Wang, Yide
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6982721/
https://www.ncbi.nlm.nih.gov/pubmed/31905998
http://dx.doi.org/10.3390/s20010218
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author He, Wei
Yang, Xiao
Wang, Yide
author_facet He, Wei
Yang, Xiao
Wang, Yide
author_sort He, Wei
collection PubMed
description The direction-of-arrivals (DOA) estimation with an unfolded coprime linear array (UCLA) has been investigated because of its large aperture and full degrees of freedom (DOFs). The existing method suffers from low resolution and high computational complexity due to the loss of the uniform property and the step of exhaustive peak searching. In this paper, an improved DOA estimation method for a UCLA is proposed. To exploit the uniform property of the subarrays, the diagonal elements of the two self-covariance matrices are averaged to enhance the accuracy of the estimated covariance matrices and therefore the estimation performance. Besides, instead of the exhaustive peak searching, the polynomial roots finding method is used to reduce the complexity. Compared with the existing method, the proposed method can achieve higher resolution and better estimation performance with lower computational complexity.
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spelling pubmed-69827212020-02-28 A High-Resolution and Low-Complexity DOA Estimation Method with Unfolded Coprime Linear Arrays He, Wei Yang, Xiao Wang, Yide Sensors (Basel) Article The direction-of-arrivals (DOA) estimation with an unfolded coprime linear array (UCLA) has been investigated because of its large aperture and full degrees of freedom (DOFs). The existing method suffers from low resolution and high computational complexity due to the loss of the uniform property and the step of exhaustive peak searching. In this paper, an improved DOA estimation method for a UCLA is proposed. To exploit the uniform property of the subarrays, the diagonal elements of the two self-covariance matrices are averaged to enhance the accuracy of the estimated covariance matrices and therefore the estimation performance. Besides, instead of the exhaustive peak searching, the polynomial roots finding method is used to reduce the complexity. Compared with the existing method, the proposed method can achieve higher resolution and better estimation performance with lower computational complexity. MDPI 2019-12-30 /pmc/articles/PMC6982721/ /pubmed/31905998 http://dx.doi.org/10.3390/s20010218 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
He, Wei
Yang, Xiao
Wang, Yide
A High-Resolution and Low-Complexity DOA Estimation Method with Unfolded Coprime Linear Arrays
title A High-Resolution and Low-Complexity DOA Estimation Method with Unfolded Coprime Linear Arrays
title_full A High-Resolution and Low-Complexity DOA Estimation Method with Unfolded Coprime Linear Arrays
title_fullStr A High-Resolution and Low-Complexity DOA Estimation Method with Unfolded Coprime Linear Arrays
title_full_unstemmed A High-Resolution and Low-Complexity DOA Estimation Method with Unfolded Coprime Linear Arrays
title_short A High-Resolution and Low-Complexity DOA Estimation Method with Unfolded Coprime Linear Arrays
title_sort high-resolution and low-complexity doa estimation method with unfolded coprime linear arrays
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6982721/
https://www.ncbi.nlm.nih.gov/pubmed/31905998
http://dx.doi.org/10.3390/s20010218
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