DOI QR코드

DOI QR Code

Precise Orbit Determination Based on the Unscented Transform for Optical Observations

  • Received : 2019.09.11
  • Accepted : 2019.11.27
  • Published : 2019.12.15

Abstract

In this study, the precise orbit determination (POD) software is developed for optical observation. To improve the performance of the estimation algorithm, a nonlinear batch filter, based on the unscented transform (UT) that overcomes the disadvantages of the least-squares (LS) batch filter, is utilized. The LS and UT batch filter algorithms are verified through numerical simulation analysis using artificial optical measurements. We use the real optical observation data of a low Earth orbit (LEO) satellite, Cryosat-2, observed from optical wide-field patrol network (OWL-Net), to verify the performance of the POD software developed. The effects of light travel time, annual aberration, and diurnal aberration are considered as error models to correct OWL-Net data. As a result of POD, measurement residual and estimated state vector of the LS batch filter converge to the local minimum when the initial orbit error is large or the initial covariance matrix is smaller than the initial error level. However, UT batch filter converges to the global minimum, irrespective of the initial orbit error and the initial covariance matrix.

Keywords

References

  1. Choi J, Jo JH, Yim HS, Choi EJ, Cho S, et al., Optical tracking data validation and orbit estimation for sparse observations of satellites by the OWL-Net, Sensors 18, 1868 (2018). https://doi.org/10.3390/s18061868
  2. Grewal MS, Andrews AP, Kalman Filtering: Theory and Practice Using MATLAB, 3rd ed. (Wiley, Hoboken, NJ, 2008).
  3. Haykin S, Kalman Filtering and Neural Networks (Wiley, New York, NY, 2001).
  4. Hughes SP, Qureshi RH, Cooley DS, Parker JJ, Grubb TG, Verification and validation of the general mission analysis tool (GMAT), in 2014 AIAA/AAS Astrodynamics Specialist Conference, San Diego, CA, 4-7 Aug 2014.
  5. Lee DJ, Alfriend KT, Sigma point filtering for sequential orbit estimation and prediction, J. Spacecr. Rockets 44, 388-398 (2007). https://doi.org/10.2514/1.20702
  6. Lee E, Park SY, Shin B, Cho S, Choi EJ, et al., Orbit determination of KOMPSAT-1 and cryosat-2 satellites using optical widefield patrol network (OWL-Net) data with batch least squares filter, J. Astron. Space Sci. 34, 19-30 (2017). https://doi.org/10.5140/JASS.2017.34.1.19
  7. NERC Space Geodesy Facility, Satellite CPF predictions (2019) [Internet], cited 2019 Feb 14, available from: http://sgf.rgo.ac.uk/qualityc/cpf_qc.html
  8. Park E, Park SY, Choi KH, Performance comparison of the batch filter based on the unscented transformation and other batch filters for satellite orbit determination, J. Astron. Space Sci. 26, 75-88 (2009). https://doi.org/10.5140/JASS.2009.26.1.075
  9. Park ES, Application of the batch filter based on the sigma point sampling to satellite orbit determination, PhD Dissertation, Yonsei University (2009).
  10. Park ES, Park SY, Roh KM, Choi KH, Satellite orbit determination using a batch filter based on the unscented transformation, Aerosp. Sci. Technol. 14, 387-396 (2010). https://doi.org/10.1016/j.ast.2010.03.007
  11. Park SY, Keum KH, Lee SW, Jin H, Park YS, et al., Development of a data reduction algorithm for optical wide field patrol, J. Astron. Space Sci. 30, 193-206 (2013). https://doi.org/10.5140/JASS.2013.30.3.193
  12. Pearlman MR, Degnan JJ, Bosworth JM, The international laser ranging service, Adv. Space Res. 30, 135-143 (2002). https://doi.org/10.1016/S0273-1177(02)00277-6
  13. Root BC, Validating and improving the orbit determination of Cryosat-2, Master's thesis, Delft University of Technology (2012).
  14. Smart WM, Green RM, Textbook on Spherical Astronomy (Cambridge University Press, New York, NY, 1977).
  15. Vallado DA, Fundamentals of Astrodynamics and Applications, 2nd ed. (Microcosm Press, El Segundo, CA, 2001).