Automatic Analysis of Radio Meteor Events Using Neural Networks
- 124 Downloads
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.
KeywordsAutomatic meteor detection Self-organizing map Multi-layered perceptron
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.
- S. Calders, H. Lamy, Brams: status of the network and preliminary results, in Proceedings of the International Meteor Conference (IMC 2011) (2012), pp. 73–76Google Scholar
- A. Georgescu, T. Georgescu, Romanian AllSky Network: basic deployment, in Proceedings of the 32nd International Meteor Conference (IMC 2013) (2013). 22–25 Aug 2013Google Scholar
- S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd edn. (Prentice Hall PTR, Upper Saddle River, NJ, 1998)Google Scholar
- H. Lamy, E. Gamby, S. Ranvier, Y. Geunes, S. Calders, J. de Kaiser, The BRAMS viewer: an on-line tool to access the BRAMS data, in Proceedings of the International Meteor Conference (IMC 2012) (2013). pp. 48–50Google Scholar
- D. Rumelhart, J. McClelland, PDP Research Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol I, Foundations (MIT Press, Cambridge, MA, 1986)Google Scholar
- E. Uriarte, F. Martín, Topology preservation in SOM, in World Academy of Science, Engineering and Technology, International Science Index 21, vol 2, no. 9 (2008), pp. 867–870Google Scholar