Low-thrust maneuver anomaly detection of a cooperative asset using publicly available orbital data

Low-thrust maneuver anomaly detection of a cooperative asset using publicly available orbital data

Riccardo Cipollone, Pierluigi di Lizia

download PDF

Abstract. This work presents a novel method to estimate perturbations with respect to nominal maneuver planning by exploiting Two-Line-Element (TLE) data as initial step, then moving on to Global Positioning System (GPS) processed data. The case study is a low-thrust engine validation mission in Low Earth Orbit. The first algorithm exploits a couple of TLEs as boundary conditions to set up a least-squares problem and find the tangential thrust magnitude and firing duration to best fit the bounding orbital states, making use of Taylor differential algebra and Picard iterations. The second one makes use of a sequence of GPS states to apply multistep finite differences and a root-finding algorithm to retrieve information about both thrust profile and firing bounding times.

Low-Thrust Maneuver Estimation, Semi-analytic Method, Anomaly Detection

Published online 11/1/2023, 5 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Riccardo Cipollone, Pierluigi di Lizia, Low-thrust maneuver anomaly detection of a cooperative asset using publicly available orbital data, Materials Research Proceedings, Vol. 37, pp 625-629, 2023

DOI: https://doi.org/10.21741/9781644902813-136

The article was published as article 136 of the book Aeronautics and Astronautics

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

[1] Montaruli, Marco Felice, Purpura, Giovanni, Cipollone, Riccardo, De Vittori, Andrea, Di Lizia, Pierluigi, Massari, Mauro, Peroni, Moreno, Panico, Alessandro, Cecchini, Andrea, and Rigamonti Marco, ‘A Software Suite for Orbit Determination in Space Surveillance and Tracking Applications’, EUCASS-3AF 2022. https://doi.org/10.13009/EUCASS2022-7338
[2] ESA Space Debris Office, ‘Esa’s Annual Space Environment Report’, tech. rep., European Space Agency, April 2022
[3] Rong Li X. , Jilkov Vesselin P. , A Survey of Maneuvering Target Tracking—Part IV: Decision-Based Methods, Proceedings of SPIE Conference on Signal and Data Processing of Small Targets, Orlando, FL, USA, April 2002
[4] Pastor, G. Escribano, and D. Escobar, “Satellite maneuver detection with optical survey observations,” Advanced Maui Optical and Space Surveillance Technologies Conference, 2020.
[5] Wittig A., Di Lizia P., Armellin R., Makino K., Bernelli-Zazzera F., and Berz M., Propagation of large uncertainty sets in orbital dynamics by automatic domain splitting. Celestial Mechanics and Dynamical Astronomy, 122(3):239–261, 2015. https://doi.org/10.1007/s10569-015-9618-3
[6] Vitolo M., Maestrini M., Di Lizia P., Sampling-Based Strategy for On-Orbit Satellite Inspection, 25th Conference of the Italian Association of Aeronautics and Astronautics (AIDAA 2019)
[7] Fornberg, Bengt. ‘Generation of Finite Difference Formulas on Arbitrarily Spaced Grids’. Mathematics of Computation 51, no. 184 (1988): 699–706. https://doi.org/10.1090/S0025-5718-1988-0935077-0
[8] Killick R., P. Fearnhead, and I.A. Eckley. “Optimal detection of changepoints with a linear computational cost.” Journal of the American Statistical Association. Vol. 107, Number 500, 2012, pp.1590-1598. https://doi.org/10.1080/01621459.2012.737745