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Earth, Moon, and Planets

, Volume 116, Issue 2, pp 101–113 | Cite as

Automatic Analysis of Radio Meteor Events Using Neural Networks

  • Victor Ştefan Roman
  • Cătălin Buiu
Article

Abstract

Meteor Scanning Algorithms (MESCAL) is a software application for automatic meteor detection from radio recordings, which uses self-organizing maps and feedforward multi-layered perceptrons. This paper aims to present the theoretical concepts behind this application and the main features of MESCAL, showcasing how radio recordings are handled, prepared for analysis, and used to train the aforementioned neural networks. The neural networks trained using MESCAL allow for valuable detection results, such as high correct detection rates and low false-positive rates, and at the same time offer new possibilities for improving the results.

Keywords

Automatic meteor detection Self-organizing map Multi-layered perceptron 

Notes

Acknowledgments

This work was supported by a grant from the Romanian National Authority for Scientific Research, CNDI-UEFISCDI, project number 205/2012. The work of V.Ş. Roman is supported by the Sectoral Operational Programme Human Resources Development (SOP-HRD), financed from the European Social Fund and the Romanian Government, under the contract number POSDRU/159/1.5/S/137390. V. Ş. Roman performed part of this work during a research stage at L’Institute d’Aéronomie Spatiale de Belgique and would like to thank Hervé Lamy for his kind support.

Supplementary material

11038_2015_9473_MOESM1_ESM.doc (378 kb)
Supplementary material 1 (DOC 377 kb)

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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Department of Automatic Control and Systems EngineeringPolitehnica University of BucharestBucharestRomania

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