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Optimization of Sparse Planar Arrays with Minimum Spacing and Geographic Constraints in Smart Ocean Applications

Sparse arrays can fix array aperture with a reduced number of elements to maintain resolution while reducing cost. However, grating lobe suppression, high peak side-lobe level reduction (PSLL), and constraints on the location of the array elements in the practical deployment of arrays are challengin...

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Autores principales: Hao, Shijie, Ge, Feng-Xiang, Yu, Xianxiang, Cui, Guolong, Ma, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339100/
https://www.ncbi.nlm.nih.gov/pubmed/30577503
http://dx.doi.org/10.3390/s19010011
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author Hao, Shijie
Ge, Feng-Xiang
Yu, Xianxiang
Cui, Guolong
Ma, Li
author_facet Hao, Shijie
Ge, Feng-Xiang
Yu, Xianxiang
Cui, Guolong
Ma, Li
author_sort Hao, Shijie
collection PubMed
description Sparse arrays can fix array aperture with a reduced number of elements to maintain resolution while reducing cost. However, grating lobe suppression, high peak side-lobe level reduction (PSLL), and constraints on the location of the array elements in the practical deployment of arrays are challenging problems. Based on simulated annealing, the element locations of a sparse planar array in smart ocean applications with minimum spacing and geographic constraints are optimized in this paper by minimizing the sum of PSLL. The robustness of the deployment-optimized spare planar array with mis-calibration is further considered. Numerical simulations show the effectiveness of the proposed solution.
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spelling pubmed-63391002019-01-23 Optimization of Sparse Planar Arrays with Minimum Spacing and Geographic Constraints in Smart Ocean Applications Hao, Shijie Ge, Feng-Xiang Yu, Xianxiang Cui, Guolong Ma, Li Sensors (Basel) Article Sparse arrays can fix array aperture with a reduced number of elements to maintain resolution while reducing cost. However, grating lobe suppression, high peak side-lobe level reduction (PSLL), and constraints on the location of the array elements in the practical deployment of arrays are challenging problems. Based on simulated annealing, the element locations of a sparse planar array in smart ocean applications with minimum spacing and geographic constraints are optimized in this paper by minimizing the sum of PSLL. The robustness of the deployment-optimized spare planar array with mis-calibration is further considered. Numerical simulations show the effectiveness of the proposed solution. MDPI 2018-12-20 /pmc/articles/PMC6339100/ /pubmed/30577503 http://dx.doi.org/10.3390/s19010011 Text en © 2018 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
Hao, Shijie
Ge, Feng-Xiang
Yu, Xianxiang
Cui, Guolong
Ma, Li
Optimization of Sparse Planar Arrays with Minimum Spacing and Geographic Constraints in Smart Ocean Applications
title Optimization of Sparse Planar Arrays with Minimum Spacing and Geographic Constraints in Smart Ocean Applications
title_full Optimization of Sparse Planar Arrays with Minimum Spacing and Geographic Constraints in Smart Ocean Applications
title_fullStr Optimization of Sparse Planar Arrays with Minimum Spacing and Geographic Constraints in Smart Ocean Applications
title_full_unstemmed Optimization of Sparse Planar Arrays with Minimum Spacing and Geographic Constraints in Smart Ocean Applications
title_short Optimization of Sparse Planar Arrays with Minimum Spacing and Geographic Constraints in Smart Ocean Applications
title_sort optimization of sparse planar arrays with minimum spacing and geographic constraints in smart ocean applications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6339100/
https://www.ncbi.nlm.nih.gov/pubmed/30577503
http://dx.doi.org/10.3390/s19010011
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