Computing vertical refractivity profiles by neural networks. Comparison with bulk model results - Université de Rennes Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Computing vertical refractivity profiles by neural networks. Comparison with bulk model results

Résumé

As ducting situations considerably modify the radar coverages in maritime situations, it is of major importance to characterize the corresponding refractivity profiles. A promising solution could be to use Neural Networks methods. Once trained by physical models they could be computationally efficient. Actually, the errors introduced by these techniques in terms of propagation results lies between 3 and 5 dB for a classical radar scenario. So other NN implementations with more hidden layers will have to be tested in the future.
Fichier non déposé

Dates et versions

hal-03827261 , version 1 (24-10-2022)

Identifiants

  • HAL Id : hal-03827261 , version 1

Citer

Jacques Claverie, Jean Motsch. Computing vertical refractivity profiles by neural networks. Comparison with bulk model results. IEEE USNC-URSI Radio Science Meeting / Joint IEEE Antennas-and-Propagation-Society (AP-S) International Symposium, Jul 2022, Denver, United States. ⟨hal-03827261⟩
6 Consultations
0 Téléchargements

Partager

Gmail Facebook X LinkedIn More