Journal of Astronomy and Space Sciences

The Korean Space Science Society (한국우주과학회)
권호 표지
DOI QR코드

DOI QR Code

Geostationary Orbit Surveillance Using the Unscented Kalman Filter and the Analytical Orbit Model

  • Roh, Kyoung-Min (Division of Space Science, Korea Astronomy and Space Science Institute) ;
  • Park, Eun-Seo (Division of Space Science, Korea Astronomy and Space Science Institute) ;
  • Choi, Byung-Kyu (Division of Space Science, Korea Astronomy and Space Science Institute)
  • Received : 2010.09.17
  • Accepted : 2011.07.28
  • Published : 2011.09.15
Download PDF
⟨ Previous Next ⟩

Abstract

A strategy for geostationary orbit (or geostationary earth orbit [GEO]) surveillance based on optical angular observations is presented in this study. For the dynamic model, precise analytical orbit model developed by Lee et al. (1997) is used to improve computation performance and the unscented Kalman filer (UKF) is applied as a real-time filtering method. The UKF is known to perform well under highly nonlinear conditions such as surveillance in this study. The strategy that combines the analytical orbit propagation model and the UKF is tested for various conditions like different level of initial error and different level of measurement noise. The dependencies on observation interval and number of ground station are also tested. The test results shows that the GEO orbit determination based on the UKF and the analytical orbit model can be applied to GEO orbit tracking and surveillance effectively.

Keywords

References

  1. Africano JL, Stansbery EG, Kervin PW, The optical orbital debris measurement program at NASA and AMOS, AdSpR, 34, 892-900 (2004). http://dx.doi.org/10.1016/j.asr.2003.02.022
  2. Analytical Graphics, Satellite tool kit software verson 4.0 (Analytical Graphics, Malvern, 1998).
  3. Brouwer D, Clemence GM, Methods of celestial mechanics (Academic Press, New York, 1961), 273-307.
  4. CelesTrack, NORAD two-line element sets [Internet], cited 2011 Aug 19, available from http://www.celestrak.com/NORAD/elements/.
  5. Daum F, Nonlinear filters: beyond the Kalman filter, IAESM, 20, 57-69 (2005). http://dx.doi.org/10.1109/MAES.2005.1499276
  6. del Monte L, The ESA Space Situational Awareness initiative: contributing to a safer Europe, 2nd AAAF International Conference "Military Space: Questions in Europe", Paris, France, 17-19 Sep 2007.
  7. Flohrer T, Schildknecht R, Musci R, Stöveken, E., Performance estimation for GEO space surveillance, AdSpR, 35, 1226-1235 (2005). http://dx.doi.org/10.1016/j.asr.2005.03.101
  8. Hoots FR, Roehrich RL, Spacetrack report No. 3 (Defense Document Center, Alexandria, 1980).
  9. Julier SJ, Uhlmann, JK, Durrant-Whyte HF, A new approach for filtering nonlinear system, in Proceedings of the American Control Conference, Evanstion, IL, 21-23 Jun 1995.
  10. Lee B-S, Lee J-S, Lee J-C, Choi K-H, A new analytical ephemeris solution for the geostationary satellite and its application to Koreasat, SpT, 17, 299-309 (1997). http://dx.doi.org/10.1016/S0892-9270(97)00030-4
  11. Noordung H, Das problem der befahrung des weltraums: der raketenmotor (Richard Carl Schmidt & Co., Berlin, 1929), 72-73 (in English translated).
  12. Roh K-M, Park S-Y, Choi K-H, Orbit determination using the geomagnetic field measurement via the unscented Kalman filter, JSpRo, 44, 246-253 (2007). http://dx.doi.org/10.2514/1.23693
  13. Sabol C, Culp R, Improved angular observation in geosynchronous orbit determination, JGCD, 24, 123-130 (2001). https://doi.org/10.2514/2.4685
  14. Wan E, van der Merwe R, The unscented Kalman filter, in Kalman filtering and neural networks, ed. Haykin SS (Wiley, New York, 2001), 221-280.
  15. Yoon J-C, Lee K-H, Lee B-S, Kim B-Y, Choi K-H, et al., Geostationary orbit determination for time synchronization using analytical dynamic models, ITAES, 40, 1132-1146 (2004). http://dx.doi.org/10.1109/TAES.2004.1386869

Cited by

  1. Optical Orbit Determination of a Geosynchronous Earth Orbit Satellite Effected by Baseline Distances between Various Ground-based Tracking Stations I: COMS simulation case vol.32, pp.3, 2015, https://doi.org/10.5140/JASS.2015.32.3.221