Publication: Signal conditioning for the Kalman filter: application to satellite attitude estimation with magnetometer and sun sensors
Loading...
Official URL
Full text at PDC
Publication Date
2016-11
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI AG
Citations
Exportar
Abstract
Most satellites use an on-board attitude estimation system, based on available sensors. In the case of low-cost satellites, which are of increasing interest, it is usual to use magnetometers and Sun sensors. A Kalman filter is commonly recommended for the estimation, to simultaneously exploit the information from sensors and from a mathematical model of the satellite motion. It would be also convenient to adhere to a quaternion representation. This article focuses on some problems linked to this context. The state of the system should be represented in observable form. Singularities due to alignment of measured vectors cause estimation problems. Accommodation of the Kalman filter originates convergence difficulties. The article includes a new proposal that solves these problems, not needing changes in the Kalman filter algorithm. In addition, the article includes assessment of different errors, initialization values for the Kalman filter; and considers the influence of the magnetic dipole moment perturbation, showing how to handle it as part of the Kalman filter framework.
Description
© 2016 by the authors.
The authors would like to thank the support of the Nanosat Program of INTA institution and the specific research fund (FEI16/67) at the Complutense University of Madrid.
Unesco subjects
Keywords
Citation
1. Wertz, J.R. Spacecraft Attitude Determination and Control; Kluwer Academic Publisher: Dordrecht,The Netherlands, 1978.
2. Sidi, M.J. Spacecraft Dynamics & Control; Cambridge University Press: Cambridge, UK, 1997.
3. Kalman, R.E. A new approach to linear filtering and prediction problems. J. Basic Eng. 1960, 82, 35–45. [CrossRef]
4. Kalman, R.E.; Bucy, R.S. New results in linear filtering and prediction theory. J. Basic Eng. 1961, 83, 95–108. [CrossRef]
5. Diebel, J. Representing attitude: Euler angles, unit quaternions, and rotation vectors. Matrix 2006, 58, 1–35. 6. Hoots, F.R.; Roehrich, R.L. Models for Propagation of NORAD Element Sets. Spacetrack Report No. 3; December 1988. Available online: www.celestrak.com/NORAD/documentation/spacetrk.pdf (accessed on 2 August 2016).
7. Maus, S.; Macmillan, S.; McLean, S.; Hamilton, B.; Nair, M.; Thomson, A.; Rollins, C. The US/UK. World Magnetic Model for 2010–2015. NOAA Technical Report NESDIS/NGDC 2010. Available online: www.geomag.bgs.ac.uk/documents/WMM2010_Report.pdf (accessed on 2 August 2016).
8. Vallado, D.A. Fundamentals of Astrodynamics and Applications; Springer Science & Business Media: Berlin, Germany, 2003.
9. Wen, J.Y.; Kreutz-Delgado, K. The attitude control problem. IEEE Trans. Autom. Control 1991, 36, 1148–1162. [CrossRef]
10. Shuster, M.D. A survey of attitude representations. Navigation 1993, 8, 439–517.
11. Lefferts, E.J.; Markley, F.L.; Shuster, M.D. Kalman filtering for spacecraft attitude estimation. J. Guid. Control Dyn. 1982, 5, 417–429. [CrossRef]
12. Trawny, N.; Roumeliotis, S.I. Indirect Kalman Filter for 3D Attitude Estimation; Technical Report Number. 2005-002; Department of Computer Science & Engineering, University of Minnesota: Minneapolis, MN, USA, 2005; Volume 2.
13. Crassidis, J.L.;Markley, F.L. Predictive filtering for attitude estimation without rate sensors. Adv. Astronaut. Sci. 1996, 93, 1021–1038. [CrossRef]
14. Edwan, E.; Knedlik, S.; Loffeld, O. Angular motion estimation using dynamic models in a gyro-free inertial measurement unit. Sensors 2012, 12, 5310–5327. [CrossRef] [PubMed]
15. Hamzah, N.H.; Yaacob, S.; Muthusamy, H.; Hamzah, N. Comparative Study of Extended Kalman Filter and Particle Filter for Attitude Estimation in Gyroless Low Earth Orbit Spacecraft. In Advanced Computational. Methods for Knowledge Engineering; Springer International Publishing: Berlin, Germany, 2015; pp. 95–106.
16. Crespillo, O.G.; Cuccci, D.A.; Khaghani, M. Unscented Kalman Filter for Angular Rate Estimation in Gyro-Free Inertial System. In Proceedings of the ION GNSS+ 2016, Portland, OR, USA, 12–13 September 2016.
17. Yang, Y. Spacecraft attitude determination and control: Quaternion based method. Ann. Rev. Control 2012, 36, 198–219. [CrossRef]
18. Wahba, G. A least squares estimate of spacecraft attitude. SIAM Rev. 1965, 7, 409. [CrossRef]
19. Shuster, M.D.; Oh, S.D. Three-Axis attitude determination form vector observations. J. Guid. Control 1981, 4, 70–77. [CrossRef]
20. INTA NanoSat-1B at eoPortal Directory. Available online: https://directory.eoportal.org/web/eoportal/ satellite-missions/n/nanosat-1b (accessed on 2 August 2016).
21. Díaz-Michelena, M. Small Magnetic Sensors for Space Applications. Sensors 2009, 9, 2271–2288. [CrossRef] [PubMed]
22. Pita, L.C.; San Roman, S.E.; Giron-Sierra, J.M.; Barriga, J.R.; de Vicente, P.D.; Jerez, M.A. Getting more performance from INTA NanoSat-1B truncated pyramid Sun sensors. IEEE Sens. J. 2014, 14, 1867–1877. [CrossRef]
23. Polo, O.R.; Esteban, S.; Cercos, L.; Parra, P.; Angulo, M. End-to-end validation process for the INTA-Nanosat-1B Attitude Control System. Acta Astronaut. 2014, 93, 94–105. [CrossRef]