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Navigation complex with adaptive non-linear Kalman filter for unmanned flight vehicle

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A navigation complex of an unmanned flight vehicle of small class is considered. Increasing the accuracy of navigation definitions is done with the help of a nonlinear Kalman filter in the implementation of the algorithm on board an aircraft in the face of severe limitations on the performance of the special calculator. The accuracy of the assessment depends on the available reliable information on the model of the process under study, which has a high degree of uncertainty. To carry out high-precision correction of the navigation complex, an adaptive non-linear Kalman filter with parametric identification was developed. The model of errors of the inertial navigation system is considered in the navigation complex, which is used in the algorithmic support. The procedure for identifying the parameters of a non-linear model represented by the SDC method in a scalar form is used. The developed adaptive non-linear Kalman filter is compact and easy to implement on board an aircraft.
Rocznik
Strony
541--550
Opis fizyczny
Bibliogr. 17 poz., rys., tab., wzory
Twórcy
  • Bauman Moscow State Technical University, Faculty of Computer Science and Control Systems, Moscow, 105005, Russia
  • Bauman Moscow State Technical University, Faculty of Computer Science and Control Systems, Moscow, 105005, Russia
  • Bauman Moscow State Technical University, Faculty of Computer Science and Control Systems, Moscow, 105005, Russia
Bibliografia
  • [1] Moir, I., Seabridge, A.G. (2008). Aircraft Systems: Mechanical, electrical and avionics subsystems integration. 3rd edition, Chichester: John Willey and Sons Ltd.
  • [2] Selezneva, M.S., Neusypin, K.A. (2016). Development of a measurement complex with intelligent component. Measurement Techniques, 59(9), 916-922.
  • [3] Julier, S.J., Uhlmann, J.K. (1997). A New Extension of the Kalman Filter to Nonlinear Systems. Defense Sensing, Simulation and Controls, 3068, 182-193.
  • [4] Kalman, R.E. (1960). A new approach to linear filtering and prediction problems. Trans. ASME. Ser. D, Journal of Basic Engeneering, 82, 35-45.
  • [5] Mirosław, Ś., Magdalena, D. (2015). Application of Kalman filter in navigation process of automated guided vehicles. Metrol. Meas. Syst., 22(2), 443-454.
  • [6] Salychev, O.S. (2012). MEMS-based inertial navigation: Expectations and reality. Moscow: Bauman MSTU Press.
  • [7] Groves, P.D., Handley, R.J., Runnalls, A.R. (2006). Optimising the integration of terrain-referenced navigation with INS and GPS. Journal of Navigation, 59(1), 71-89.
  • [8] Carlson, N.A. (1990). Federated square root filter for decentralized parallel processors. IEEE Transactions on Aerospace and Electronic Systems, 26(2), 517-525.
  • [9] Xing, Z.R., Xia, Y.Q. (2016). Distributed federated Kalman filter fusion over multi-sensor unreliable networked systems. IEEE Transactions on Circuits and Systems, 63(8), 1714-1725.
  • [10] Verhaegen, M., Verdult, V. (2007). Filtering and system identification: a least squares approach. Cambridge University Press.
  • [11] Shen K., et al. (2018). Quantifying observability and analysis in integrated navigation. Navigation, Journal of the Institute of Navigation, 65(1), 169-181.
  • [12] Shen, K., Selezneva, M.S., Neusypin, K.A., Proletarsky, A.V. (2017). Novel variable structure measurement system with intelligent components for flight vehicles. Metrol. Meas. Syst., 24(1), 347-356.
  • [13] Shen, K., Proletarsky, A.V., Neusypin, K.A. (2016). Algorithms of constructing models for compensating navigation systems of unmanned aerial vehicles. 2016 International Conference on Robotics and Automation Engineering, Aug. 27-29, Jeju-Do, South Korea, 104-108.
  • [14] Tayfun, Ç. (2008). State-Dependent Riccati Equation (SDRE) Control: A Survey Tayfun Cimen Proceedings of the 17th World Congress. The International Federation of Automatic Control. Seoul, Korea, Jul. 6-11, 3761-3775.
  • [15] Prošek, J., Šímová, P. (2019). UAV for mapping shrubland vegetation: Does fusion of spectral and vertical information derived from a single sensor increase the classification accuracy? International Journal of Applied Earth Observation ad Geoinformation 75, 151-162.
  • [16] Qiao, Y., Zhang, Y., Du, X. (2018). A Vision-Based GPS-Spoofing Detection Method for Small UAVs. Proc. - 13th International Conference on Computational Intelligence and Security, CIS 2017, 312-316.
  • [17] Afanas’ev, V.N., Kaperko, A.F., Kulagin, V.P., Kolyubin, V.A. (2017). Method of adaptive filtering in the problem of restoring parameters of cosmic radiation. Automation and Remote Control, 78(3), 397-411.
Uwagi
EN
1. The work was performed in the framework of the State Mission of the Ministry of Education and Science of the Russian Federation (Project No. 2.7486.2017/BC) .
PL
2. Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-938ca428-ae07-47d7-8cdd-4c355ab948ef
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