Hostname: page-component-546b4f848f-w58md Total loading time: 0 Render date: 2023-06-01T13:35:58.926Z Has data issue: false Feature Flags: { "useRatesEcommerce": true } hasContentIssue false

Autonomous navigation for Mars probes using only satellite-to-satellite tracking measurements by singularity-avoiding orbit elements

Published online by Cambridge University Press:  11 February 2022

Pengbin Ma*
Affiliation:
State Key Laboratory of Astronautic Dynamics, Xi'an Satellite Control Center, Xi'an, China.
Jie Yang
Affiliation:
State Key Laboratory of Astronautic Dynamics, Xi'an Satellite Control Center, Xi'an, China.
Hengnian Li
Affiliation:
State Key Laboratory of Astronautic Dynamics, Xi'an Satellite Control Center, Xi'an, China.
Zhibin Zhang
Affiliation:
State Key Laboratory of Astronautic Dynamics, Xi'an Satellite Control Center, Xi'an, China. Department of Automation, University of Science and Technology of China, Hefei, China.
Hexi Baoyin
Affiliation:
School of Aerospace Engineering, Tsinghua University, Beijing, China
*
*Corresponding author. E-mail: map_bin@163.com

Abstract

This paper proposes a novel autonomous navigation method for Mars-orbiting probes. Satellite-to-satellite tracking (SST) between two probes is generally deemed to involve autonomous measurements with no dependence on any external observation sites on the Earth. For the conventional two-body dynamic model, it is well known that the orbit states cannot be estimated by merely using such SST measurements. Considering the effects of third-body gravitation perturbation and the weak Mars tesseral harmonics perturbation, autonomous navigation with SST measurements alone becomes weakly observable and may be achieved by some nonlinear filtering techniques. Two significant improvements are made to mitigate the nonlinearity brought by the dynamic models. First, singularity-avoiding orbit elements are selected to represent the dynamic models in order to reduce the intensity of the nonlinearity which cannot be overcome by the traditional position–velocity state expression. Second, the unscented Kalman filter method is effectively utilised to avoid the linearised errors calculated by its extended Kalman filter counterpart which may exceed the tesseral harmonics perturbation. A constellation, consisting of one low-orbit probe and one high-orbit probe, is designed to realise the autonomous orbit determination of both participating Mars probes. A reliable navigation solution is successfully obtained by Monte Carlo simulation runs. It shows that the errors of the semimajor axes of the two Mars probes are less than 10 m and the position errors are less than 1 km.

Type
Research Article
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Carreau, M. (2016). Curiosity's Mars find suggests early explosive volcanism. Aerospace Daily & Defense Report, Parma, Italy, June, 27.Google Scholar
Chobotov, V. A. (2002). Orbital Mechanics, 3rd ed. Reston, VA: AIAA, Inc.Google Scholar
Daum, F. (2005). Nonlinear filters: beyond the Kalman filter. IEEE Aerospace and Electronic Systems Magazine, 20, 5769.CrossRefGoogle Scholar
Doody, D. (2009). Deep Space Craft - An Overview of Interplanetary Flight. New York, USA: Springer.10.1007/978-3-540-89510-7CrossRefGoogle Scholar
Genova, A., Goossens, S. and Lemoine, F. G. (2016). Seasonal and static gravity field of Mars from MGS, Mars Odyssey and MRO radio science. Icarus, 272, 228242.CrossRefGoogle Scholar
Hill, K. and Born, G. (2007). Autonomous interplanetary orbit determination using satellite-to-satellite tracking. Journal of Guidance, Control, and Dynamics, 30, 679686.CrossRefGoogle Scholar
Hill, K. and Born, G. (2008). Autonomous orbit determination from lunar halo orbits using crosslink range. Journal of Spacecraft and Rockets, 45, 548553.CrossRefGoogle Scholar
Hill, K., Lot, M. W. and Born, G. H. (2006). Linked Autonomous Interplanetary Satellite Orbit Navigation. AIAA/AAS Astrodynamics Specialist Conference, Advances in the Astronautical Sciences: Astrodynamics, Orlando, FL, USA.Google Scholar
Hirt, C., Claessens, S. J. and Kuhn, M. (2012). Kilometer-resolution gravity field of Mars: MGM2011. Planetary and Space Science, 67, 147154.CrossRefGoogle Scholar
Jiang, X., Yang, B. and Li, S. (2018). Overview of China's 2020 Mars mission design and navigation. Astrodynamics, 2, 111.CrossRefGoogle Scholar
Julier, S. J. and Uhlmann, J. K. (1997). A New Extension of the Kalman Filter to Nonlinear Systems. Signal Processing, Sensor Fusion, and Target Recognition VI, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 3068, 182193.10.1117/12.280797CrossRefGoogle Scholar
Julier, S. J. and Uhlmann, J. K. (2004). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3), 401422.CrossRefGoogle Scholar
Karimi, R. R. and Mortari, D. (2015). Interplanetary autonomous navigation using visible planets. Journal of Guidance, Control and Dynamics, 38, 11511156.CrossRefGoogle Scholar
Lemoine, F. G., Smith, D. E. and Rowlands, D. (2001). An improved solution of the gravity field of Mars (GMM-2B) from Mars Global Surveyor. Journal of Geophysical Research: Planets, 106, 2335923376.CrossRefGoogle Scholar
Liu, L. (1977). A method of calculation [of] the perturbation of artificial satellites. Chinese Astronomy and Astrophysics, 1, 6378.Google Scholar
Liu, Y. and Liu, L. (2001). Orbit determination using satellite-to-satellite tracking data. Chinese Journal of Astronomy and Astrophysics, 1, 281286.CrossRefGoogle Scholar
Ma, P., Jiang, F. and Baoyin, H. (2015). Autonomous navigation of Mars probes by combining optical data of viewing Martian moons and SST data. The Journal of Navigation, 68, 10191040.CrossRefGoogle Scholar
Ma, P., Wang, T., Jiang, F., Mu, J. and Baoyin, H. (2017). Autonomous navigation of Mars probes by single X-ray pulsar measurement and optical data of viewing Martian moons. The Journal of Navigation, 70, 1832.CrossRefGoogle Scholar
Martin-Mur, T. J., Kruizinga, G. L., Burkhart, P. D., Abilleira, F., Wong, M. C. and Kangas, J. A. (2014). Mars science laboratory interplanetary navigation. Journal of Spacecraft and Rockets, 51(4), 10141028.CrossRefGoogle Scholar
Ning, X., Wang, F. and Fang, J. (2016). Implicit UKF and its observability analysis of satellite stellar refraction navigation system. Aerospace Science and Technology, 54, 4958.CrossRefGoogle Scholar
Sanderson, K. (2010). Mars rover Spirit (2003–10). Nature, 463, 600.CrossRefGoogle Scholar
Steffes, S. R. and Barton, G. (2017). Deep Space Autonomous Navigation Options for Future Missions. AIAA SPACE and Astronautics Forum and Exposition, Orlando, FL, USA.CrossRefGoogle Scholar
Tapley, B. D., Schutz, B. E. and Born, G. H. (2004). Statistical Orbit Determination. California, USA: Elsevier Academic Press.Google Scholar
Thisdell, D. (2013). India's Mangalyaan makes for Mars. Flight International, 184, 14.Google Scholar
Vallado, D. A. (2013). Fundamentals of Astrodynamics and Applications, 4th ed. Hawthorne, CA, USA: Microcosm Press.Google Scholar
Xin, M., Fang, J. and Ning, X. (2013). An overview of the autonomous navigation for a gravity-assist interplanetary spacecraft. Progress in Aerospace Sciences, 11, 5666.Google Scholar
Xiong, K., Wei, C. and Liu, L. (2014). High-accuracy autonomous navigation based on inter-satellite range measurement. Chinese Aerospace Control and Application, 6, 1641.Google Scholar
VanDyke, M. C., Schwartz, J. L. and Hall, C. D. (2004). Unscented Kalman filtering for spacecraft attitude state and parameter estimation. Proceedings of the AAS/AIAA Space Flight Mechanics Conference, Hawaii, USA.Google Scholar