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Open Astronomy

formerly Baltic Astronomy

Editor-in-Chief: Barbuy, Beatriz


IMPACT FACTOR 2018: 0.350

CiteScore 2018: 0.24

SCImago Journal Rank (SJR) 2018: 0.202
Source Normalized Impact per Paper (SNIP) 2018: 0.144

ICV 2017: 121.03

Open Access
Online
ISSN
2543-6376
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Volume 27, Issue 1

Issues

A classification of meteor radio echoes based on artificial neural network

Mikhail Danilov
  • Corresponding author
  • Institute of physics, Department of radio physics, Kazan Federal University, 18th Kremloyvskaya Str., Kazan, Russian Federation
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Arkadi Karpov
  • Institute of physics, Department of radio physics, Kazan Federal University, 18th Kremloyvskaya Str., Kazan, Russian Federation
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2018-12-13 | DOI: https://doi.org/10.1515/astro-2018-0037

Abstract

An artificial neural network is described for classification of meteor trails into the distinct overdense, intermediate and underdense trail categories. The neural network was trained and on model data obtained using the “KAMET” program and tested on real data. The best result of classification success rate of 95% without according to the heights of the formation of meteor trails. Results of classification with according to the heights of the formation of meteor trails are 82% - 91%.

Keywords: Meteor radio echoes; Classification algorithms; Artificial neural networks; Radiowave propagation

References

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About the article

Received: 2018-02-04

Accepted: 2018-09-17

Published Online: 2018-12-13

Published in Print: 2018-12-01


Citation Information: Open Astronomy, Volume 27, Issue 1, Pages 318–325, ISSN (Online) 2543-6376, DOI: https://doi.org/10.1515/astro-2018-0037.

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© by Mikhail Danilov, Arkadi Karpov, published by De Gruyter. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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