In EDS ansehen

Synthesis of Sparse Planar Arrays Using Matrix Mapping and Differential Evolution.

Gespeichert in:
Bibliographische Detailangaben
Titel: Synthesis of Sparse Planar Arrays Using Matrix Mapping and Differential Evolution.
Autoren: Heng Liu, Hongwei Zhao, Weimei Li, Bo Liu
Quelle: IEEE Antennas & Wireless Propagation Letters; 2016, Vol. 15, p1905-1908, 4p
Abstract: This letter proposes a differential evolution (DE) with matrix mapping for sparse rectangular planar array synthesis technique with multiple constraints. The multiple constraints include the number of elements, the planar array aperture, and the minimum spacing of adjacent elements. By introducing a novel matrix mapping between the element spacing and the variables of DE, the strong constrained optimization problem is transformed to a nonconstrained problem with only lower and upper limits, and the infeasible solutions are naturally avoided during the optimization process. The simulation results show the high efficiency and the robustness of the proposed method and indicate that our method can achieve better results than existing methods. [ABSTRACT FROM PUBLISHER]
Copyright of IEEE Antennas & Wireless Propagation Letters is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Datenbank: Complementary Index
Beschreibung
Abstract:This letter proposes a differential evolution (DE) with matrix mapping for sparse rectangular planar array synthesis technique with multiple constraints. The multiple constraints include the number of elements, the planar array aperture, and the minimum spacing of adjacent elements. By introducing a novel matrix mapping between the element spacing and the variables of DE, the strong constrained optimization problem is transformed to a nonconstrained problem with only lower and upper limits, and the infeasible solutions are naturally avoided during the optimization process. The simulation results show the high efficiency and the robustness of the proposed method and indicate that our method can achieve better results than existing methods. [ABSTRACT FROM PUBLISHER]
ISSN:15361225
DOI:10.1109/LAWP.2016.2542882