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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Online Access: | https://doi.org/10.1049/mia2.12052 |
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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 |
_version_ |
1721302792276017152 |