Abstract
A fusion navigation algorithm for the distributed satellites system (DSS) utilizing relative range measurements is proposed in this paper. Based on the quasi-consistent extended Kalman filter (QCEKF), an on-line evaluation of the navigation precision can be provided by the fusion navigation algorithm. In addition, the upper bound for the estimation error obtained from the fusion navigation algorithm is lower than those with any groups of measurements, which indicates that the fusion navigation algorithm can automatically choose the suitable redundant measurements to improve the navigation precision. The simulations show the feasibility and effectiveness of the proposed fusion navigation algorithm.
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References
Schetter T, Campbell M, Surka D. Multiple agent-based autonomy for satellite constellations. Artif Intell, 2003, 145: 147–180
Ley W, Wittmann K, Hallmann W. Handbuch der Raumfahrttechnik. Munich: Carl Hanser Verlag GmbH & CO. KG, 2011
Tapley B D, Ries J C, Davis G W, et al. Precision orbit determination for TOPEX/POSEIDON. J Geophys Res, 1994, 99: 24383–24404
Psiaki M L. Autonomous orbit determination for two spacecraft from relative position measurements. J Guid Control Dynam, 1999, 22: 305–312
Yim J R, Crassidis J L, Junkins J L. Autonomous orbit navigation of two spacecraft system using relative line of sight vector measurements. AAS Paper 04–257, 2004
Markley F L. Autonomous navigation using landmark and intersatellite data. AIAA Paper 84–1987, 1984
Liu Y, Liu L. Orbit determination using satellite-to-satellite tracking data. Chin J Astron Astrophys, 2001, 1: 281–286
Grechkoseev A K. Study of observability of motion of an orbital group of navigation space system using intersatellite range measurements. I. J Comput Sys Sci Int, 2011, 50: 293–308
Grechkoseev A K. Study of observability of motion of an orbital group of navigation space system using intersatellite range measurements. II. J Comput Syst Sci Int, 2011, 50: 472–482
Hill K, Born G H. Autonomous interplanetary orbit determination using satellite-to-satellite tracking. J Guid Control Dynam, 2007, 30: 679–686
Hill K, Born G H. Autonomous orbit determination from lunar halo orbits using crosslink range. J Spacecraft Rockets, 2008, 45: 548–553
Huxel P J, Bishop R H. Navigation algorithms and observability analysis for formation flying missions. J Guid Control Dynam, 2009, 32: 1218–1231
Huxel P J. Navigation algorithms and observability analysis for formation flying missions. Dissertation for Ph.D. Degree. Austin: University of Texas, 2006
Shorshi G, Bar-Itzhack I Y. Satellite autonomous navigation and orbit determination using magnetometers. In: Proceedings of the 31st Conference on Decision and Control, Tucson, 1992. 542–548
Wiegand M. Autonomous satellite navigation via Kalman filter of magnetometer data. Acta Astronaut, 1996, 38: 395–403
Li Y, Xu X S. The application of EKF and UKF to the SINS/GPS integrated navigation systems. In: Proceedings of the 2nd International Conference on Information Engineering and Computer Science (ICIECS), Wuhan, 2010. 1–5
Xia H W, Diao Y H, Ma G C, et al. X-ray pulsar relative navigation approach based on extended Kalman filter. J Chin Inertial Tech, 2014, 22: 619–623
Jiang Y G, Xue W C, Huang Y, et al. The consistent extended Kalman filter. In: Proceedings of the 33rd Chinese Control Conference, Nanjing, 2014. 6838–6845
Chong C Y. Hierarchical estimation. In: Proceedings of the 2nd MIT/ONR Workshop on C3, Monterey, 1979. 205–220
Chong C Y, Chang K C, Mori S. Distributed tracking in distributed sensor networks. In: Proceedings of the American Contrlol Conference, Seattle, 1986
Chang K C, Zhi T, Saha R K. Performance evaluation of track fusion with information matrix filter. IEEE Trans Aero Elec Syst, 2002, 38: 455–466
Roy A E. Orbital Motion. 4th ed. Bristol: Institute of Physics Publishing, 2005
Bar-Shalom Y, Li X R, Kirubarajan T. Estimation with Application to Tracking and Navigation. New York: John Wiley & Sons Inc., 2001
Julier S J, Uhlmann J K. A new extension of the Kalman filter to nonlinear systems. In: Proceedings of SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, Orlando, 1997. 182–193
Jiang Y G. On quasi-consistent nonlinear Kalman filter. Dissertation for Ph.D. Degree. Beijing: University of Chinese Academy of Sciences, 2016
Sabol C, Burns R, McLaughlin C A. Satellite formation flying design and evolution. J Spacecraft Rockets, 2001, 38: 270–278
Simon D. Optimal State Estimation — Kalman, H ∞, and Nonlinear Approaches. New Jersey: John Wiley & Sons Inc., 2006
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This work was supported by National Basic Research Program of China (973 Program) (Grant No. 2014CB845303) and National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences.
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Su, Q., Huang, Y., Jiang, Y. et al. Quasi-consistent fusion navigation algorithm for DSS. Sci. China Inf. Sci. 61, 012201 (2018). https://doi.org/10.1007/s11432-016-9054-x
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DOI: https://doi.org/10.1007/s11432-016-9054-x