Troposcatter transmission loss prediction based on particle swarm optimisation

Abstract Tropospheric scatter is a promising method for over‐the‐horizon propagation. Transmission loss caused by the three mainstream troposcatter mechanisms is analysed, namely turbulent incoherent scattering theory, coherent reflection by stable layers theory, and incoherent reflection by irregul...

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Main Authors: Dizhe Yuan, Xihong Chen
Format: Article
Language:English
Published: Wiley 2021-02-01
Series:IET Microwaves, Antennas & Propagation
Online Access:https://doi.org/10.1049/mia2.12052
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spelling doaj-4bf56dc83b5645e8b9064fdc8da286a02021-07-14T13:20:51ZengWileyIET Microwaves, Antennas & Propagation1751-87251751-87332021-02-0115333234110.1049/mia2.12052Troposcatter transmission loss prediction based on particle swarm optimisationDizhe Yuan0Xihong Chen1Air Force Engineering University Xi'an ChinaAir Force Engineering University Xi'an ChinaAbstract Tropospheric scatter is a promising method for over‐the‐horizon propagation. Transmission loss caused by the three mainstream troposcatter mechanisms is analysed, namely turbulent incoherent scattering theory, coherent reflection by stable layers theory, and incoherent reflection by irregular layers theory. Then an experiment is conducted to explore the relationships among the three mechanisms. Based on this experiment, the troposcatter transmission loss prediction model is established in different climate zones by a particle swarm optimisation algorithm and experimental data from the global troposcatter databank. The simulation shows that this model is more effective than the existing International Telecommunication Union‐Radiocommunication Sector (ITU‐R) P.617, P.452, and P.2001. Furthermore, by analysing the training parameters' proportion of the new model in different climate zones, the specific composition of three troposcatter mechanisms can be obtained.https://doi.org/10.1049/mia2.12052
collection DOAJ
language English
format Article
sources DOAJ
author Dizhe Yuan
Xihong Chen
spellingShingle Dizhe Yuan
Xihong Chen
Troposcatter transmission loss prediction based on particle swarm optimisation
IET Microwaves, Antennas & Propagation
author_facet Dizhe Yuan
Xihong Chen
author_sort Dizhe Yuan
title Troposcatter transmission loss prediction based on particle swarm optimisation
title_short Troposcatter transmission loss prediction based on particle swarm optimisation
title_full Troposcatter transmission loss prediction based on particle swarm optimisation
title_fullStr Troposcatter transmission loss prediction based on particle swarm optimisation
title_full_unstemmed Troposcatter transmission loss prediction based on particle swarm optimisation
title_sort troposcatter transmission loss prediction based on particle swarm optimisation
publisher Wiley
series IET Microwaves, Antennas & Propagation
issn 1751-8725
1751-8733
publishDate 2021-02-01
description Abstract Tropospheric scatter is a promising method for over‐the‐horizon propagation. Transmission loss caused by the three mainstream troposcatter mechanisms is analysed, namely turbulent incoherent scattering theory, coherent reflection by stable layers theory, and incoherent reflection by irregular layers theory. Then an experiment is conducted to explore the relationships among the three mechanisms. Based on this experiment, the troposcatter transmission loss prediction model is established in different climate zones by a particle swarm optimisation algorithm and experimental data from the global troposcatter databank. The simulation shows that this model is more effective than the existing International Telecommunication Union‐Radiocommunication Sector (ITU‐R) P.617, P.452, and P.2001. Furthermore, by analysing the training parameters' proportion of the new model in different climate zones, the specific composition of three troposcatter mechanisms can be obtained.
url https://doi.org/10.1049/mia2.12052
work_keys_str_mv AT dizheyuan troposcattertransmissionlosspredictionbasedonparticleswarmoptimisation
AT xihongchen troposcattertransmissionlosspredictionbasedonparticleswarmoptimisation
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