Sage Journals: Discover world-class research

Abstract

The lack of ground-tracking resources has become a primary bottleneck for the Chinese BeiDou navigation satellite system. As crosslinks have been widely recognized as a promising augmentation for the autonomous navigation of the global navigation satellite system, this article studies a new decentralized data fusion method for orbit determination of crosslink-augmented satellite constellations. In the new solution, the system is modeled as a probabilistic graphical model, the dynamical Bayesian network, and a graphical method named junction tree is introduced to analyze the structure of the system. By dividing and arranging the predictions and estimations of junction tree in a proper sequence, a new decentralized solution with centralized equivalent precision is designed. As the solution requires satellites in the constellation cooperating with each other through communications and measurements, it is named junction-tree-based cooperative orbit determination. Simulation results indicate that junction-tree-based cooperative orbit determination has centralized equivalent precision and good robustness after satellite failures, whereas centralized solutions such as extended Kalman filter may suffer from processing center malfunctions. Junction-tree-based cooperative orbit determination could not only serve as an optional choice for global navigation satellite system autonomous navigation but also be used as a general scheme for decentralized data fusion in cooperative systems such as unmanned aerial vehicle formations and so on.

Keywords

Cooperative orbit determination satellite constellation crosslink dynamical Bayesian network junction tree decentralized data fusion

Get full access to this article

View all access options for this article.

References

Gao

Wang

. Long-term semi-autonomous orbit determination supported by a few ground stations for navigation constellation. Sci China: Phys Mech Astron 2011; 54: 1342–1353.

Chacos

Stadter

Devereux

. Autonomous navigation and crosslink communication. Johns Hopkins Apl Technical Digest 2001; 22: 135–143.

Liu

. Method for eliminating systematic error in autonomous orbit determination of navigation satellites. Geomatics Inf Sci Wuhan Univ 2007; 32: 27–30.

Parkinson

Spilker

. Global positioning system: Theory and applications 1996; (vol. 1), Washington DC: American Institute of Aeronautics and Astronautics.

Han

Gui

. Establishment criteria, routing algorithms and probability of use of inter-satellite links in mixed navigation constellations. Adv Space Res 2013; 51: 2084–2092.

Teofilatto

Pasquale

. A fast guidance algorithm for an autonomous navigation system. Planet Space Sci 1998; 46: 1627–1632.

Lin

Qianyi

Rui

. Eliminating constellation rotation error of autonomous orbit determination using differential anchor stations. Geomatics Inf Sci Wuhan Univ 2013; 38: 920–924.

Ferguson P, How J. Decentralized estimation algorithms for formation flying spacecraft. In: AIAA guidance, navigation, and control conference, Austin, TX, 2003, pp.1–12.

Lauritzen

Spiegelhalter

. Local computations with probabilities on graphical structures and their application to expert systems. J Roy Stat Soc 1988, pp. 157–224.

10.

Lauritzen

. Propagation of probabilities, means, and variances in mixed graphical association models. J Am Stat Assoc 1992; 87: 1098–1108.

11.

Paskin MA. Thin junction tree filters for simultaneous localization and mapping. In: IJCAI’03 proceedings of the 18th international joint conference on artificial intelligence, 2003, pp.1157–1164.

12.

Funiak S, Guestrin C, Paskin M, et al. Distributed localization of networked cameras. In: Information processing in sensor networks, Nashville, TN, 2006, pp.34–42.

13.

Kim

Ong

Nettleton

. Decentralized approach to unmanned aerial vehicle navigation: Without the use of the global positioning system and preloaded maps. Proc IMechE, Part G: J Aerospace Engineering 2004; 218: 399–416.

14.

Mu H, Wu M, Ma H, et al. (eds) Lazy probability propagation on Gaussian Bayesian networks. In: 22nd international conference on tools with artificial intelligence, ICTAI 2010, 27–29 October 2010. Arras, France: IEEE Computer Society.

15.

Mu H, Dai M, Gao M, et al. (eds) Fully decentralized cooperative localization of a robot team: An efficient and centralized equivalent solution. In: 6th international conference on intelligent robotics and applications, ICIRA 2013, 25–28 September 2013. Busan, Republic of Korea: Springer Verlag.

16.

Bishop

. Pattern recognition and machine learning, New York, USA: Springer, 2006.

17.

Ananda MP, Bernstein H, Cunningham KE, et al. Global Positioning System (GPS) autonomous navigation. In: Position location and navigation symposium, 1990. Las Vegas, NV: IEEE.

18.

Montenbruck

Gill

. Satellite orbits—models, methods, and applications, Heidelberg, Germany: Springer Berlin Heidelberg, 2001.

19.

Ferreiro

Arnaiz

Sierra

. A Bayesian network model integrated in a prognostics and health management system for aircraft line maintenance. Proc IMechE, Part G: J Aerospace Engineering 2011; 225: 886–901.

20.

Kim

D-W

Postlethwaite

. Robust target tracking using distributed unmanned aerial vehicle networks. Proc IMechE, Part G: J Aerospace Engineering 2010; 224: 417–426.

21.

Ruz

Pham

. Building Bayesian network classifiers through a Bayesian complexity monitoring system. Proc IMechE, Part C: J Mechanical Engineering Science 2009; 223: 743–755.

Junction-tree-based cooperative orbit determination for crosslink-augmented satellite constellations

Abstract

Keywords

Get full access to this article

References