Skip to main content
Log in

Multi-Fidelity Orbit Determination with Systematic Errors

  • Original Article
  • Published:
The Journal of the Astronautical Sciences Aims and scope Submit manuscript

Abstract

Multi-fidelity approaches to orbit-state probability density prediction reduce computation time, but introduce a systematic error in the single-point prediction of a spacecraft state. An estimate of the systematic error may be quantified using cross-validation. Credibilistic filters based on Outer Probability Measures (OPMs) enable a principled and unified representation of random and systematic errors in object tracking. The quantified error of the multi-fidelity approach defines an OPM-based transition kernel, which is used in a credibilistic filter to account for the systematic error in the orbit determination process. An approach based on automatic domain splitting is proposed to reduce the error beyond what is normally achievable with multi-fidelity methods. A proof-of-concept for the approach is demonstrated for a simulated scenario tracking a newly-detected space object in low-Earth orbit via two ground stations generating radar measurements. An OPM-based definition of the admissible region combined with the multi-fidelity credibilistic filter establishes custody of the object.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Notes

  1. If the system is deterministic, i.e., has no random uncertainty, then its probability measure (and associated PDF) concentrate all their mass to a single element of the state space, representing the true state of the system.

References

  1. Armellin, R., Di Lizia, P., Bernelli-Zazzera, F., Berz, M.: Asteroid close encounters characterization using differential algebra: the case of Apophis. Celest. Mech. Dyn. Astron. 107(4), 451–470 (2010)

    Article  MathSciNet  Google Scholar 

  2. Balch, M.S., Martin, R., Ferson, S.: Satellite conjunction analysis and the false confidence theorem. arXiv:1706.08565 (2018)

  3. Delande, E.D., Houssineau, J., Jah, M.K.: A new representation of uncertainty for data fusion in SSA detection and tracking problems. In: Proceedings of the 21st International Conference on Information Fusion (2018)

  4. Delande, E., Houssineau, J., Jah, M.: Physics and human-based information fusion for improved resident space object tracking. Adv. Space Res. 62 (7), 1800–1812 (2018)

    Article  Google Scholar 

  5. Delande, E.D., Jah, M.K., Jones, B.A.: A new representation of uncertainty for collision assessment. In: AAS/AIAA Space Flight Mechanics Meeting, Kaanapali, HI (2019)

  6. DeMars, K.J., Cheng, Y, Bishop, R.H., Jah, M.K.: Methods for splitting gaussian distributions and applications within the AEGIS filter. In: 2012 AAS/AIAA Space Flight Mechanics Meeting, Charleston, SC (2012)

  7. DeMars K.J., Jah, M.K.: Probabilistic initial orbit determination using Gaussian mixture models. J. Guid. Control Dyn. 36(5), 1324–1335 (2013)

    Article  Google Scholar 

  8. Dormand, J.R., Prince, R.J.: A family of embedded Runge-Kutta formulae. J. Comput. Appl. Math. 6(1), 19–26 (1980)

    Article  MathSciNet  Google Scholar 

  9. Farnocchia, D., Tommei, G., Milani, A., Rossi, A.: Innovative methods of correlation and orbit determination for space debris. Celest. Mech. Dyn. Astron. 107(1-2), 169–185 (2010)

    Article  MathSciNet  Google Scholar 

  10. Folkner W.M., Williams, J.G., Boggs, D.H., Park, R.S., Kuchynka, P.: The planetary and lunar ephemerides DE430 and DE431. IPN Progress Report 42-196, Jet Propulsion Laboratory, California Institute of Technology. http://ipnpr.jpl.nasa.gov/progress_report/42-196/196C.pdf (2009)

  11. Ghanem, R.G., Higdon, D., Owhadi, H., eds: Handbook of Uncertainty Quantification. Springer International Publishing, Switzerland (2017)

    Book  Google Scholar 

  12. Hampton, J., Fairbanks, H.R., Narayan, A., Doostan, A.: Practical error bounds for a non-intrusive bi-fidelity approach to parametric/stochastic model reduction. J. Comput. Phys. 368, 315–332 (2018)

    Article  MathSciNet  Google Scholar 

  13. Horwood J.T., Aragon, N.D., Poore, A.B.: Gaussian sum filters for space surveillance: Theory and simulations. J. Guida. Control Dyn. 34(6), 1839–1851 (2011)

    Article  Google Scholar 

  14. Houssineau, J., Bishop, A.N.: Smoothing and filtering with a class of outer measures. SIAM/ASA J. Uncertain. Quantif. 6(2), 845–866 (2018)

    Article  MathSciNet  Google Scholar 

  15. Jones, B.A.: Multi-fidelity methods for orbit determination. In: Proceedings of the 2018 Advanced Maui Optical and Space Surveillance Technologies Conference, Wailea, Maui, Hawaii (2018)

  16. Jones, B.A., Delande, E.D., Zucchelli, E.M., Jah, M.K.: Multi-fidelity orbit uncertainty propagation with systematic errors. In: Proceedings of the 2019 Advanced Maui Optical and Space Surveillance Technologies Conference, Wailea, Maui, Hawaii (2019)

  17. Jones, B.A., Doostan, A., Born, G.H.: Nonlinear propagation of orbit uncertainty using non-intrusive polynomial chaos. J. Guid. Control Dyn. 36(2), 430–444 (2013)

    Article  Google Scholar 

  18. Jones, B.A., Weisman, R.: Multi-fidelity orbit uncertainty propagation. Acta Astronaut. 155, 406–417 (2019)

    Article  Google Scholar 

  19. Julier, S., Uhlmann, J.K.: Unscented filtering and nonlinear estimation. Proc. IEEE 92(3), 401–422 (2004)

    Article  Google Scholar 

  20. Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME–J. Basic Eng. 82(Series D), 35–45 (1960)

    Article  MathSciNet  Google Scholar 

  21. Narayan, A., Gittelson, C., Xiu, D.: A stochastic collocation algorithm with multifidelity models. SIAM J. Sci. Comput. 36(2), A495–A521 (2014)

    Article  MathSciNet  Google Scholar 

  22. Park, I., Fujimoto, K., Scheeres, D.J.: Effect of dynamical accuracy for uncertainty propagation of perturbed keplerian motion. J. Guid. Control Dyn. 38(12), 2287–2300 (2015)

    Article  Google Scholar 

  23. Park, I., Scheeres, D.J.: Optimization of hybrid method for uncertainty propagation of non-keplerian motion. In: AIAA/AAS Astrodynamics Specialist Conference, pp 2016–5630. AIAA, Long Beach California (2016)

  24. Petit, G., Luzum, B.: IERS conventions (2010) IERS Technical Note 36, International Earth Rotation and Reference Systems Service (IERS), Frankfurt am Main, Germany (2010)

  25. Pirovano, L., Santeramo, D.A, Armellin, R., Lizia, P., Wittig, A.: Probabilistic data association: the orbit set. Celest. Mech. Dyn. Astron. 132(2), 1–27 (2020)

    Article  MathSciNet  Google Scholar 

  26. Poore, A.B., Aristoff, J.M., Horwood, J.T., Armellin, R., Cerven, W.T., Cheng, Y., Cox, C.M., Erwin, R.S., Frisbee, J.H., Hejduk, M.D, Jones, B.A., Pierluigi, D.L., Scheeres, D.J., Vallado, D.A., Weisman, R: Covariance and uncertainty realism in space surveillance and tracking. Technical Report AD1020892, Air Force Space Command Astrodynamics Innovation Committee (2016)

  27. Standish, E.M., Newhall, X.X., Williams, J.G., Folkner, W.M.: JPL planetary and lunar ephemerides, DE403/LE403. Interoffice memorandum IOM 314.10-127, Jet Propulsion Laboratory (1995)

  28. Tapley, B.D., Ries, J.C., Bettadpur, S.V., Chambers, D., Cheng, M., Condi, F., Poole, S.: The GGM03 mean earth gravity model from GRACE. In: American Geophysical Union, Fall Meeting, Abstract No. G42A-03 (2007)

  29. Tapley, BD., Schutz, B.E., Born, G.H.: Statistical Orbit Determination, 1st edn. Elsevier Academic Press, Burlington, MA (2004)

    Google Scholar 

  30. Tapley, B.D., Watkins, M.M., Ries, J.C., Davis, G.W., Eanes, R.J., Poole, S.R., Rim, H.J., Schutz, B.E., Shum, C.K., Steven Nerem, R., Lerch, F.J., Marshall, J.A., Klosko, S.M., Pavlis, N.K., Williamson, R.W.: The joint gravity model 3. J. Geophys. Res. 101(B12), 28029–28050 (1996)

    Article  Google Scholar 

  31. Tuggle, K., Zanetti, R.: Automated splitting gaussian mixture nonlinear measurement update. J. Guid. Control Dyn. 41(3), 725–734 (2018)

    Article  Google Scholar 

  32. Vittaldev, V., Russell, R.P.: Multidirectional Gaussian mixture models for nonlinear uncertainty propagation. Comput. Model. Eng. Sci. 111(1), 83–117 (2016)

    Google Scholar 

  33. Worthy, J.L., Holzinger, M.J.: Incorporating uncertainty in admissible regions for uncorrelated detections. J. Guid. Control Dyn. 38(9), 1673–1689 (2015)

    Article  Google Scholar 

  34. Worthy, IIIJ.L., Holzinger, M.J.: Use of uninformative priors to initialize state estimation for dynamical systems. Adv. Space Res. 60(7), 1373–1388 (2017)

    Article  Google Scholar 

  35. Zhu, X., Narayan, A., Xiu, D.: Computational aspects of stochastic collocation with multifidelity models. SIAM/ASA J. Uncertain. Quantif. 2(1), 444–463 (2014)

    Article  MathSciNet  Google Scholar 

Download references

Funding

This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-19-1-0404.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Enrico M. Zucchelli.

Ethics declarations

Conflict of interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article belongs to the Topical Collection: Advanced Maui Optical and Space Surveillance Technologies (AMOS 2018 & 2019) Guest Editors: James M. Frith, Lauchie Scott, Islam Hussein

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zucchelli, E.M., Delande, E.D., Jones, B.A. et al. Multi-Fidelity Orbit Determination with Systematic Errors. J Astronaut Sci 68, 695–727 (2021). https://doi.org/10.1007/s40295-021-00267-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40295-021-00267-y

Keywords

Navigation