Robust UKF Insensitive to Measurement Faults for Pico Satellite Attitude Estimation
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33122
Robust UKF Insensitive to Measurement Faults for Pico Satellite Attitude Estimation

Authors: Halil Ersin Soken, Chingiz Hajiyev

Abstract:

In the normal operation conditions of a pico satellite, conventional Unscented Kalman Filter (UKF) gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunction in the estimation system, UKF gives inaccurate results and diverges by time. This study, introduces Robust Unscented Kalman Filter (RUKF) algorithms with the filter gain correction for the case of measurement malfunctions. By the use of defined variables named as measurement noise scale factor, the faulty measurements are taken into the consideration with a small weight and the estimations are corrected without affecting the characteristic of the accurate ones. Two different RUKF algorithms, one with single scale factor and one with multiple scale factors, are proposed and applied for the attitude estimation process of a pico satellite. The results of these algorithms are compared for different types of measurement faults in different estimation scenarios and recommendations about their applications are given.

Keywords: attitude algorithms, Kalman filters, robustestimation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1332062

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1628

References:


[1] M. L. Psiaki, F. Martel, and P. K. Pal, "Three-axis attitude determination via Kalman filtering of magnetometer data," Journal of Guidance, Control, and Dynamics, vol.13, pp. 506-514, 1990.
[2] S. J. Julier, J. K. Uhlmann and H. F. Durrant-Whyte, "A new approach for filtering nonlinear systems," in Proc. of American Control Conference, vol.3, pp. 1628-1632, 1995.
[3] P. Sekhavat, Q. Gong, and I. M. Ross, "NPSAT I parameter estimation using unscented Kalman filter," Proceedings of 2007 American Control Conference, New York, pp. 4445-4451, 2007.
[4] J. L. Crassidis, and F. L. Markley, "Unscented filtering for spacecraft attitude estimation," Journal of Guidance, Control, and Dynamics, vol. 26, pp. 536-542, 2003.
[5] C. Hide, T. Moore, and M. Smith, "Adaptive kalman filtering algorithms for integrating GPS and low cost INS," in Proc. of Position Location and Navigation Symposium, Monterey, USA, 2004, pp.227-233.
[6] Ch. Hajiyev, "Adaptive filtration algorithm with the filter gain correction applied to integrated INS/Radar altimeter," Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, vol.221, pp. 847-885, 2007.
[7] C. Hu, W. Chen, Y. Chen, and D. Liu, "Adaptive Kalman filtering for vehicle navigation," Journal of Global Positioning Systems, vol.2, pp. 42-47, 2003.
[8] K.H. Kim, J.G. Lee, and C.G. Park, "Adaptive two-stage Kalman filter in the presence of unknown random bias," International Journal of Adaptive Control and Signal Processing, vol.20, pp. 305-319, 2006.
[9] Y. Geng, and J. Wang, "Adaptive estimation of multiple fading factors in Kalman filter for navigation applications," GPS Solutions, vol.12, pp. 273-279, 2008.
[10] J. Xu, Y. Jing, G.M. Dimirovski and Y. Ban, "Two-stage unscented Kalman filter for nonlinear systems in the presence of unknown random bias" In Proc. of the American Control Conference, Seattle, USA, 2008, pp. 3530-3535.
[11] J. R. Wertz, Spacecraft Attitude Determination and Control, Dordrecht, Holland: Kluwer Academic Publishers, 1988.