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Non-Stationary Noise Analysis of Magnetic Sensors using Allan Variance

100%
Draganová K., Moucha V., Volčko T., Semrád K.
Acta Physica Polonica A
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2017
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vol. 131
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issue 4
1126-1128
EN
The presented paper describes methodology for the non-stationary noise analysis of the magnetic sensors' data using the dynamic Allan variance. The methodology was developed for the characterization of clock behavior. In the article the theory is applied for the magnetic sensors noise analysis and verified by the simulations and experiments. Results of the data analysis are graphically presented and statistically evaluated and prove the correctness of the initial hypothesis and confirm suitability of the dynamic Allan variance approach for magnetic sensors with the non-stationary noise behavior.
2
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Calibration of Magnetometer for Small Satellites Using Neural Network

88%
Kliment T., Praslička D., Lipovský P., Draganová K., Závodský O.
Acta Physica Polonica A
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2017
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vol. 131
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issue 4
1129-1131
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.
3
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Noise Analysis of Magnetic Sensors Using Allan Variance

76%
Draganová K., Kmec F., Blažek J., Praslička D., Hudák J., Laššák M.
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vol. 126
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issue 1
394-395
EN
The article deals with the noise analysis of magnetic sensors using Allan variance. In comparison with the standard variance, based on the variations around the average value, Allan variance provides a measure of the behavior of the variability of a quantity as it is averaged over different measurement time periods, which results in a better convergence and the possibility to distinguish types of noise directly. A significant advantage of this method is that there is no need of any transformations and according to the IEEE recommendation the Allan variance approach is the preferred method for stochastic error identification and the noise type determination in inertial sensors (accelerometers and gyroscopes). Considering that magnetic sensors become even more often a part of inertial measurements units, the goal of this article is to prove that the method using the Allan variance analysis can be very successfully applied also for noise analysis in magnetic sensors.
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