Article information

2020 , Volume 25, ¹ 3, p.35-45

Chernikova O.S., Tolstikov A.S., Chetvertakova Y.S.

Application of adaptive identification methods for refining parameters of radiation pressure models

Purpose. The paper considers the problem of estimation of unknown parameters for various models of solar radiation based on adaptive modifications of the unscented Kalman filter. The estimations of the obtained parameters are used both in solar radiation models and in construction of trajectory of a navigation satellite.

Methodology. To solve the problem of parametric identification of stochastic nonlinear continuous-discrete systems, several adaptive modifications of the unscented Kalman filter are considered. The algorithms assume recurrent estimation of covariance matrices of system noise and measurements. The maximum likelihood method is used for parametric identification of stochastic nonlinear continuous-discrete systems. Adaptive modifications of the unscented Kalman filter are used in the construction of the identification criterion. Estimates of unknown parameters of various solar radiation models are found for the movement for the navigation satellite model as an example. The satellite orbital movement forecast is made.

Finding and value. The application of the adaptive parametric identification procedure allows calculating the estimates for the parameters of several models of the solar radiation with sufficient accuracy. The obtained results lead to significant improvement of quality of the prediction for satellite trajectory

[full text] [link to elibrary.ru]

Keywords: nonlinear stochastic continuous-discrete system, uscented Kalman filter, adaptive filtering, parametric identification, spacecraft motion model, radiation pressure model

doi: 10.25743/ICT.2020.25.3.005

Author(s):
Chernikova Oksana Sergeevna
PhD. , Associate Professor
Office: Novosibirsk State Technical University
Address: 630073, Russia, Novosibirsk, 20 Prospekt K. Marksa
E-mail: chernikova@corp.nstu.ru
SPIN-code: 5520-2336

Tolstikov Aleksander Sergeevich
Dr.
Position: Head of department
Office: Siberian Metrological Scientific-Research Institute, Novosibirsk State Technical University
Address: 630004, Russia, Novosibirsk, 4,Dimitrova aven.
Phone Office: (383) 210-11-85
E-mail: tolstikov@corp.nstu.ru

Chetvertakova Yuliya Sergeevna
Position: Student
Office: Novosibirsk State Technical University
Address: 630073, Russia, Novosibirsk, 20 Prospekt K. Marksa
E-mail: julia_ch98@mail.ru

References:

1. Grewal M.S., Andrews A.P. Kalman filtering: Theory and practice using MATLAB. 2nd ed. New York: John Wiley & Sons; 2001: 401.

2 Simon D. Kalman filtering with state constraints: a survey of linear and nonlinear algorithms. IET Control Theory & Applications. 2009; 4(8):1303–1318. DOI:10.1049/iet-cta.2009.0032.

3. Julier S.J., Uhlmann J.K. A new extension of the Kalman filter to nonlinear systems. In Proc. of AeroSense: The 11-th Intern. Symp. on Aerospace. Defence Sensing, Simulation and Control. 1997: 12.

4. Sarkka S. On unscented Kalman filtering for state estimation of continuous-time nonlinear systems. IEEE Transactions on Automatic Control. 2007; 52(9):1631–1641.

5. Mohamed A.H., Schwarz K.P. Adaptive Kalman filtering for INS/GPS. Journal of Geodesy. 1999; (73):193–203.

6. Gao W., Li J., Zhou J., Li Q. Adaptive Kalman filtering with recursive noise estimator for integratedSINS/DVL systems. The Journal of Navigation. 2015; 68(1):142–161.

7. Chernikova O.S. An adaptive unscented Kalman filter approach for state estimation of nonlinear continuous-discrete system. Actual Problems of Electronic Instrument Engineering (APEIE–2018). Novosibirsk. 2018; 1(4):37–40.

8. Hashlamon I., Erbatur K. A new adaptive extended Kalman filter for a class of nonlinear systems.J. of Applied and Comput. Mechanics. 2020; 6(1):1–12. DOI:10.22055/jacm.2019.28130.1455.

9. Astro¨m K.J. Maximum likelihood and prediction errors methods. Automatica. 1980; 16(5):551–574.

10. Scho¨n T. On computational methods for nonlinear estimation. Linko¨ping Studies in Science andTechnology. Thesis No. 1047. Linko¨ping, Sweden: Department of Electrical Engineering Link¨oping University; 2003: 159.

11. Sage A., Husa G.W. Adaptive filtering with unknown prior statistics. In Proc. of Joint AutomaticControl Conf. 1969: 760–769.

12. Zhao L., Wang X. An adaptive UKF with noise statistic estimator. 4th IEEE Conf. on IndustrialElectronics and Applications. 2009: 614–618.

13. Wang H., Fu G., Li J., Yan Z., Bian X. An Adaptive UKF Based SLAM Method for Unmanned Underwater Vehicle. Mathematical Problems in Engineering. 2013; (2013): 605981.

14. Jwo D.-J., Chung F.-C., Weng T.-P. Adaptive Kalman filter for navigation sensor fusion. Sensor Fusionand its Applications. In Tech Open. 2010: 66–90. DOI:10.5772/9957.

15. Deng Z., Yin L., Huo B., Xia Y. [15] . Deng Z., Yin L., Huo B., Xia Y. Adaptive Robust UnscentedKalman Filter via Fading Factor and Maximum Correntropy Criterion. Sensors. 2018; 18(8):2406.

16. Gayazov I.O. Empirical models of radiation pressure for satellite GPS and GLONASS. Transactionsof the Institute of Applied Astronomy RAS. 2000; (5):93–102. (In Russ.)

17. Chernikova O.S., Chubich V.M., Tolstikov A.S. Predicting orbital motion of the satellite based onquazimaximum estimation of the solar radiation parameters. Proc. SPIE 11208, 25th Intern. Symp. on Atmospheric and Ocean Optics: Atmospheric Physics, 1120875, 18 December 2019: 6.

18. Jun-ping C., Jie-xian W. Models of solar radiation pressure in the orbit determination of GPSsatellites. Chinese Astronomy and Astrophysic. 2007; (31):66–75.

19. Springer T. NAPEOS mathematical models and algorithms, Document ¹ DOPS-SYS-TN-0100-OPSGN, 1.0, 5 November 2009: 150.

20. Montenbruck O., Gill E. Satellite orbits: models, methods and applications. Berlin, Heidelberg: Springer-Verlag; 2000: 371. DOI:10.1007/978-3-642-58351-3.


Bibliography link:
Chernikova O.S., Tolstikov A.S., Chetvertakova Y.S. Application of adaptive identification methods for refining parameters of radiation pressure models // Computational technologies. 2020. V. 25. ¹ 3. P. 35-45
Home| Scope| Editorial Board| Content| Search| Subscription| Rules| Contacts
ISSN 1560-7534
© 2024 FRC ICT