Skip to main content
SpringerLink
Log in

Unscented Kalman filter-based method for spacecraft navigation using resident space objects

  • Original Paper
  • Published:
Aerospace Systems Aims and scope Submit manuscript

Abstract

The number of resident space objects (RSOs) in orbit has increased dramatically within the last 10 years. While RSOs pose a serious challenge to our continued use of space, these objects also provide an opportunity to improve on-orbit state estimation by detecting and identifying these objects using star trackers. While star trackers are currently used to determine the orientation of their host spacecraft, their utility could be expanded to both position and orientation estimation by harnessing RSO detections. In order for RSO-based optical navigation to be commercially viable, a reliable filter covariance estimate is required. This paper introduces an Unscented Kalman Filter (UKF) for estimating an observing spacecraft’s position and attitude based on RSO observations. To ensure that this filter is reliable, a new assessment technique is introduced where we compare the moving standard deviation and mean of the filter’s estimate error with the predicted error based on the filter’s covariance estimate. We demonstrate the utility of this assessment method by comparing the covariance trusts for our UKF and a previously developed Extended Kalman Filter.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Anheier NC, Chen C (2014) A new approach to space situational awareness using small ground-based telescopes. Tech. Rep. No. PNNL- 23994, Pacific Northwest National Laboratory, Richland, WA, https://doi.org/10.2172/1171901

  2. Brown RG, Hwang PYC (1997) Introduction to random signals and applied Kalman filtering, 3rd edn. Wiley, New York, p 204

    Google Scholar 

  3. Clemens S, Lee R, Harrison P, Soh W (2018) Feasibility of using commercial star trackers for on-orbit resident space object detection. In: AMOSTech Conf., Maui, HI

  4. Crassidis JL, Alonso R, Junkins JL (2000) Optimal attitude and position determination from line-of-sight measurements. J Astronaut Sci 48(2):391–408

    Article  Google Scholar 

  5. Darling J, Houtz N, Frueh C, DeMars K (2016) Recursive filtering of star tracker data. In: AIAA/AAS Astrodynamics Specialist Conf., Long Beach, CA

  6. Driedger M, Ferguson P (2018) Orbital navigation using resident space object observations. In: Proceedings of the 2018 Small Satellite Conf., Logan, UT

  7. Enright J, Jovanovic I, Kazemi L, Zhang H, Dzamba T (2018) Autonomous optical navigation using nanosatellite-class instruments: a Mars approach case study. Celest Mech Dyn Astr. https://doi.org/10.1007/s10569-017-9800-x

  8. Ferguson P (2003) Distributed estimation and control technologies for formation flying spacecraft. PhD thesis, Massachusetts Institute of Technology, Cambridge MA. Retrieved from http://hdl.handle.net/1721.1/82763

  9. Ferguson P (2018) Making space for everyone: a research program aimed at breaking down barriers to new technology adoption in space. In: CASI ASTRO, Quebec City, PQ

  10. Ferguson P, How J (2003) Decentralized estimation algorithms for formation flying spacecraft. In: Proceedings of AIAA Guidance, Navigation and Control Conf., Austin, TX, https://doi.org/10.2514/6.2003-5442

  11. Gelb A, Kasper JF, Nash RA, Price CF, Sutherland AA (1974) Applied optimal estimation. MIT press, Cambridge, MA, pp 182–189

  12. Hoag DG (1983) The history of Apollo onboard guidance, navigation, and control. J Guid 6(1):4–13. https://doi.org/10.2514/3.19795

    Article  MathSciNet  Google Scholar 

  13. Julier SJ (2002) The scaled unscented transformation. In: Proceedings of the 2002 American Control Conf., Anchorage, AK, https://doi.org/10.1109/ACC.2002.1025369

  14. Julier SJ, Uhlmann JK (1997) A new extension of the Kalman filter to nonlinear systems. In: The Proceedings of AeroSense: The 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls, Orlando, FL

  15. Junkins JL, Hughes DC, Wazni KP, Pariyapong V (1999) Vision based navigation for rendezvous, docking and proximity operations. In: 22nd Annual AAS Guidance and Control Conf., Breckenridge, CO

  16. ed K Fletcher (2017) Space debris: the ESA approach. Tech. rep., European Space Agency, available at: http://esamultimedia.esa.int/multimedia/publications/BR-336/. Accessed 2 Feb 2020

  17. Liou JC, Matney MJ, Anz-Meador PD, Kessler D, Jansen M, Theall JR (2001) The new NASA orbital debris engineering model ORDEM2000. Proc Third Eur Conf Space Debris 473:309–313

    Google Scholar 

  18. Owen J W, Dumont PJ, Jackman CD (2012) Optical navigation preparations for New Horizons Pluto flyby. In: 23rd International Symposium on Space Flight Dynamics, Pasadena, CA

  19. Park CW, Ferguson P, Pohlman N, How JP (2001) decentralized relative navigation for formation flying spacecraft using augmented CDGPS. In: Proceedings of Institute of Navigation GPS Conf., Salt Lake City, UT

  20. Riedel J, Owen J W, Stuve J, Synnott S, Vaughan R (1990) Optical navigation during the Voyager Neptune encounter. In: AIAA Astrodynamics Conf., Portland, OR, https://doi.org/10.2514/6.1990-2877

  21. Riesing K, Cahoy K (2015) Orbit determination from two line element sets of ISS-deployed cubesats. In: 29th Annual AIAA/USU Conf. on Small Satellites, Logan, UT

  22. Rososhansky M (2017) Nonlinear attitude and formation estimation spacecraft and multi-agent systems. PhD thesis, Ryerson University, Toronto, ON. Retrieved from https://digital.library.ryerson.ca/islandora/object/RULA:6905

  23. Simon D (2006) Optimal state estimation: Kalman, H infinity, and nonlinear approaches, John Wiley and Sons, chap 13: nonlinear Kalman filtering, p 400

  24. Sinclai D (2017) Second generation star tracker (ST-16RT2). Tech. rep, Sinclair Interplanetary, Toronto, ON

  25. Wan EA, van der Merwe R (2000) The unscented Kalman filter for nonlinear estimation. In: Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symp., Lake Louise, AB

  26. Wan EA, van der Merwe R (2001) Kalman filtering and neural networks. Wiley., chap Chapter 7 The Unscented Kalman Filter, pp 221–280. https://doi.org/10.1002/0471221546.ch7

Download references

Acknowledgements

The authors would like to thank the Natural Sciences and Engineering Research Council (NSERC) of Canada, the Canadian Space Agency, and Magellan Aerospace for supporting this research.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, University of Manitoba, E1-451 75A Chancellor’s Circle, Winnipeg, MB R3T 5V6, Canada

    Matthew Driedger, Michael Rososhansky & Philip Ferguson

Authors
  1. Matthew Driedger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Rososhansky
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Philip Ferguson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matthew Driedger.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Driedger, M., Rososhansky, M. & Ferguson, P. Unscented Kalman filter-based method for spacecraft navigation using resident space objects. AS 3, 197–205 (2020). https://doi.org/10.1007/s42401-020-00055-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42401-020-00055-w

Keywords

Search

Navigation