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2017 | 131 | 4 | 1129-1131
Article title

Calibration of Magnetometer for Small Satellites Using Neural Network

Content
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Languages of publication
EN
Abstracts
EN
The article presents the scalar calibration method that uses a neural network for the determination of parameters of the inverse model of the vector magnetometer. Utilization of the one layered, feed-forward neural network with the back propagation algorithm has suppressed the systematic errors of the vector magnetometers, namely the multiplicative, additive, orthogonality and linearity errors. Methodology shown in the article was designed and used for a pre-flight calibration of the magnetometer used in the first Slovak satellite skCUBE, where the magnetometer performs stabilization and navigation tasks. The experiment was performed in a 3D Helmholtz coil system, where the Earth magnetic field was suppressed and at the same time the stimulation field was created. Suppression of the Earth magnetic field was achieved by special positioning of the satellite. Honeywell HMC 5883L was used for the verification of the methodology.
Keywords
EN
Publisher

Year
Volume
131
Issue
4
Pages
1129-1131
Physical description
Dates
published
2017-04
Contributors
author
  • Department of Aviation Technical Studies, Faculty of Aeronautics, Technical University of Košice, Rampová 7, 041 21 Košice, Slovakia
author
  • Department of Aviation Technical Studies, Faculty of Aeronautics, Technical University of Košice, Rampová 7, 041 21 Košice, Slovakia
author
  • Department of Aviation Technical Studies, Faculty of Aeronautics, Technical University of Košice, Rampová 7, 041 21 Košice, Slovakia
author
  • Department of Aviation Technical Studies, Faculty of Aeronautics, Technical University of Košice, Rampová 7, 041 21 Košice, Slovakia
author
  • Slovak Organization for Space Activities (SOSA), Zámocká 18, 811 01 Bratislava, Slovakia
  • Department of Telecommunications and Multimedia, Faculty of Electrical Engineering, University of Žilina, Univerzitná 1, 010 26 Žilina, Slovakia
References
  • [1] N. Olsen, L. Toffner-Clausen, T.J. Sabaka, P. Brauer, J.M.G. Merayo, J.L. Jorgensen, J.-M. Léger, O.V. Nielsen, F. Primdahl, T. Risbo, Earth Planets Space 55, 11 (2003), doi: 10.1186/BF03352458
  • [2] J.C. Springmann, J.W. Cutler, Acta Astronaut. 99, 201 (2014), doi: 10.1016/j.actaastro.2014.02.026
  • [3] J.L. Crassidis, K.-L. Lai, R.R. Harman, J. Guid. Control Dyn. 28, 115 (2005), doi: 10.2514/1.6278
  • [4] M. Lovera, Proc. Am. Control Conf. 2015-July, 1867 (2015), doi: 10.1109/ACC.2015.7171005
  • [5] T. Kliment, D. Praslicka, K. Draganova, J. Blazek, J. Electr. Eng. 66, 157 (2015)
  • [6] R. Rojas, Neural Networks, Springer, Berlin 1996, doi: 10.1007/978-3-642-61068-4
  • [7] A. Zikmund, P. Ripka, J. Electr. Eng. 61, 39 (2013)
  • [8] J. Blazek, J. Hudak, D. Praslicka, Sens. Actuat. A Phys. 59, 287 (1997), doi: 10.1016/S0924-4247(97)80191-4
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.bwnjournal-article-appv131n4170kz
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