Abstract
The paper presents intelligent telemetry data analysis software module and methods of onboard equipment of small satellites. The suggested software module consists of feature selection, data preprocessing, clustering and predicting software components. The software components are based on the genetic algorithm based feature selection method, dynamic streaming clustering method, neural Kohonen self-organizing map and image processing based clustering and predicting methods for telemetry data of onboard equipment of small satellites. The computational experiments and testing of developed methods and software tools were performed on the processed telemetry data from the navigation device of onboard equipment of a small satellite and showed enough high efficiency and good results.
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Acknowledgments
The research described in this paper is partially supported by the Russian Foundation for Basic Research (grants 15-07-08391, 15-08-08459, 16-07-00779, 16-08-00510, 16-08-01277, 16-29-09482-ofi-i, 17-08-00797, 17-06-00108, 17-01-00139, 17-20-01214), grant 074-U01 (ITMO University), project 6.1.1 (Peter the Great St. Petersburg Politechnic University) supported by Government of Russian Federation, Program STC of Union State “Monitoring-SG” (project 1.4.1-1, project 6MCГ/13-224-2), state order of the Ministry of Education and Science of the Russian Federation №2.3135.2017/K, state research 0073–2014–0009, 0073–2015–0007, International project ERASMUS +, Capacity building in higher education, № 73751-EPP-1-2016-1-DE-EPPKA2-CBHE-JP, Innovative teaching and learning strategies in open modelling and simulation environment for student-centered engineering education.
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Skobtsov, V., Novoselova, N., Arhipov, V., Potryasaev, S. (2017). Intelligent Telemetry Data Analysis of Small Satellites. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds) Cybernetics and Mathematics Applications in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol 574. Springer, Cham. https://doi.org/10.1007/978-3-319-57264-2_36
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DOI: https://doi.org/10.1007/978-3-319-57264-2_36
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