Morphogenetic Metasurface Engineering for Computational Localization Applications - Systèmes RF
Communication Dans Un Congrès Année : 2023

Morphogenetic Metasurface Engineering for Computational Localization Applications

Raymundo Amorim
  • Fonction : Auteur
Yann Marie-Joseph
  • Fonction : Auteur
Cyril Decroze
  • Fonction : Auteur
Thomas Fromenteze
  • Fonction : Auteur

Résumé

This paper presents a bio-inspired generative model allowing the emergence of self-organized spatial patterns exploited for the design of frequency-diverse metasurface antennas. The latter radiate patterns that are weakly correlated in space and frequency are adapted to the direction of arrival detection applications operating in a simplified hardware-oriented approach. The generative model studied automates the design of these metasurfaces by ensuring statistical control of certain key properties. To this end, demonstration antennas are evaluated in terms of quality factor and radiation efficiency. In line with previous demonstrations of computational localization techniques, a set of simulations is proposed to study the performance of new classes of frequency-diversity metasurfaces, referred to as morphogenetic.
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Dates et versions

hal-04853369 , version 1 (22-12-2024)

Identifiants

Citer

Raymundo Amorim, Yann Marie-Joseph, Cyril Decroze, Thomas Fromenteze. Morphogenetic Metasurface Engineering for Computational Localization Applications. 2023 IEEE Conference on Antenna Measurements and Applications (CAMA), Nov 2023, Genoa, Italy. pp.631-635, ⟨10.1109/CAMA57522.2023.10352654⟩. ⟨hal-04853369⟩
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