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Correction methods for navigation systems of unmanned aerial vehicles

Authors: Tang Ning
Published in issue: #9(38)/2019
DOI: 10.18698/2541-8009-2019-9-528


Category: Informatics, Computer Engineering and Control | Chapter: System Analysis, Control, and Information Processing, Statistics

Keywords: unmanned aerial vehicle, inertial navigation system, navigation complex, navigation system error, error compensation, estimation algorithms, control, integration, prediction
Published: 01.10.2019

The description of the types of unmanned aerial vehicles (UAVs) and their navigation systems is provided. Different methods of correction for inertial navigation systems (INS) are described. The main types and sources of errors are analyzed. The issues of improving the accuracy of the navigation complex (NC) of UAVs re-entering the atmosphere are considered. Special attention is paid to the INS error analysis, as well as the gyros drift rate and Schuler oscillations. Algorithmic methods for error compensation in navigation systems are considered. Methods for using algorithms of INS autonomous correction and also estimation, control, integration and prediction algorithms are presented. Comparison of various prediction algorithms is performed: a variety of neural networks, self-organization algorithms and genetic algorithms.


References

[1] Selezneva M.S. Razrabotka algoritmov kompleksirovaniya navigatsionnykh sistem letatel’nykh apparatov. Diss. kand. tekh. nauk. [Development of algorithms for integration of aircraft navigation systems. Kand. tech. sci. diss.]. Moscow, Bauman MSTU Publ., 2017 (in Russ.).

[2] Selezneva M.S., Ogloblina Yu.S. [Developing self-organizing model with high observability degree]. Nauchnyy vzglyad. Tr. mezhd. nauch.-prakt. konf. [Scientific view. Proc. Int. Sci.-Pract. Conf.]. Moscow, MGOU Publ., 2015, pp. 250–253 (in Russ.).

[3] Astrom K.J., McAvoy T.J. Intelligent control: an overview and evaluation. In: Handbook of intelligent control. Van Nostrand Reinhold, 1992.

[4] Shashurin V.D., Selezneva M.S., Neusypin K.A. The formation technology of the navigation complex action acceptor through the use of dynamic system synthesis. Avtomatizatsiya. Sovremennye tekhnologii, 2018, vol. 72, no. 3, pp. 121–126 (in Russ.).

[5] Shen K., Selezneva M.S., Neusypin K.A., et al. A novel variable structure measurement system with intelligent components for flight vehicles. Metrol. Meas. Syst., 2017, no. 2, pp. 347–356.

[6] Noureldin A., Karamat T.B., Georgy J. Fundamentals of inertial navigation, satel-lite-based positioning and their integration. Springer-Verlag, 2013.

[7] Neusypin K.A., Selezneva M.S., Tsibizova T.Yu. Diagnostics algorithms for flight vehicles navigation complex. RusAutoCon, 2018. DOI: 10.1109/RUSAUTOCON.2018.8501679 URL: https://ieeexplore.ieee.org/document/8501679

[8] Klychnikov V.V., Selezneva M.S., Neusypin K.A., et al. Using the federal Kalman filter to correct aircraft navigation systems. Avtomatizatsiya. Sovremennye tekhnologii, 2018, vol. 72, no. 9, pp. 428–432 (in Russ.).

[9] Kay Sh., Neusypin K.A., Selezneva M.S., et al. Research on high-precision measurement systems of modern aircraft. Izvestiya vysshikh uchebnykh zavedeniy. Aviatsionnaya tekhnika, 2018, no. 2, pp. 124–130 (in Russ.). (Eng. version: Russ. Aeronaut., 2018, vol. 61, no. 2, pp. 279–286. DOI: 10.3103/S1068799818020186 URL: https://link.springer.com/article/10.3103/S1068799818020186)

[10] Kalman R.E., Ho Y.C., Narendra K.S. Controllability of linear dynamical systems. Contributions to the Theory of Differential Equations, 1963, vol. 1, no. 2, pp. 189–213.

[11] Shakhtarin B.I., Shen K., Neusypin K.A. Modification of the nonlinear Kalman filter in a correction scheme of aircraft navigation systems. J. Commun. Technol. Electron., 2016, vol. 61, no. 11, pp. 1252–1258. DOI: 10.1134/S1064226916110115 URL: https://link.springer.com/article/10.1134%2FS1064226916110115

[12] Ivanov M.V., Selezneva M.S., Neusypin K.A. Application of the Kalman filter and genetic algorithm for an active monitoring system of the gas phase content in a flotation apparatus. Avtomatizatsiya. Sovremennye tekhnologii, 2017, vol. 71, no. 11, pp. 503–509 (in Russ.).

[13] Proletarskiy A.V., Chzhan L., Selezneva M.S., et al. Methods of the state variables criterion of the degree of observability using of in the federative Kalman filter. Pribory i sistemy. Upravlenie, kontrol’, diagnostika [Instruments and Systems: Monitoring, Control, and Diagnostics], 2018, no. 8, pp. 9–18 (in Russ.).