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A New Algorithm for Onboard Autonomous Orbit Determination of Navigation Satellites

Published online by Cambridge University Press:  14 October 2011

Haihong Wang ,
Zhonggui Chen ,
Jinjun Zheng  and
Haibin Chu
Haihong Wang*
Affiliation:
(501, China Academy of Space Technology, Beijing 100094, China)
Zhonggui Chen
Affiliation:
(501, China Academy of Space Technology, Beijing 100094, China)
Jinjun Zheng
Affiliation:
(501, China Academy of Space Technology, Beijing 100094, China)
Haibin Chu
Affiliation:
(501, China Academy of Space Technology, Beijing 100094, China)
*
(E-mail: [email protected])
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Abstract

Autonomous orbit determination of a navigation constellation is the process by which the orbit parameters of navigation satellites are autonomously calibrated onboard the satellites without the need for external aids. It commonly uses a satellite onboard data processing unit and a filtering method to process the measurements of inter-satellite ranges. The onboard data processing unit is the main module of autonomous navigation systems. In this paper, the two main factors that affect the accuracy of autonomous orbit determination for a navigation constellation are discussed first, and then a distributed onboard algorithm for autonomous orbit determination of navigation satellites is proposed. This method is based on a long-term ephemeris prediction and is suitable for the satellite hardware capability. The main feature of this method is that both the distributed computing method and an onboard analytical state transition matrix are used to process inter-satellite range measurements. One of the main advantages of this approach is high-speed computing since the amount of calculations needed is significantly less than that of the centralised computing method and those distributed methods that need to use an onboard numerical integrator. Another advantage of this approach is that the use of the onboard analytical state transition matrix algorithm can save a great amount of resources for both ground-to-satellite data transmissions and data storage units in satellites’ hardware. This could result in substantial cost reduction for space missions. Finally, a simulation method used for testing the proposed algorithm is presented. Results of tests over a period of 90 days show that the user range error of autonomous orbit determination derived from the proposed method is less than three metres.

Keywords

Onboard autonomous orbit determinationOnboard orbit forecastKalman filter
Type
Research Article
Information
The Journal of Navigation , Volume 64 , Supplement S1: Chinese Beidou and the Next Generation GNSS , November 2011 , pp. S162 - S179
Copyright
Copyright © The Royal Institute of Navigation 2011

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References

REFERENCES

Ananda, M. P., Bernstein, H. and Cunningham, K. E. (1991). Global Positioning System (GPS) Autonomous Navigation, Proceedings of 4th ESA International Conference on Spacecraft Guidance, Navigation Control System, ESTEC, Netherlands.Google Scholar
Hu, X. G., Huang, C. and Liao, X. H. (2000). Algorithm of state transform matrix. Chin. J. Astron Astrophys. 41(2), 113–122.Google Scholar
Lin, Y. M., Qin, Z. Z. and Chu, H. B. (2010). A Satellite Cross Link-Based GNSS Distributed Auto orbit determination Algorithm. Journal of Astronautics, 31(9), 2088–2094.Google Scholar
Liu, L., Zhang, Q. and Liao, X. H. (1999). Algorithm of satellite precise orbit determination. Chinese Science(A), 28(9), 848–855.Google Scholar
Liu, L. (2000). The orbit theory of spacecraft. National defense industry press.Google Scholar
McCarthy, D. D. and Petit, G. (2004). IERS Conventions (2003). IERS Conventions Centre, BKG, Frankfurt, Germany.Google Scholar
Rajan, J. A., (2002). Highlights of GPS II-R Autonomous Navigation, Proceedings of ION 58th Annual Meeting/GIGTF 21st Guidance Test Symposium, Albuquerque, NM.Google Scholar
Shuai, P., Qu, G. J. and Chen, Z. G. (2006). Studies on autonomous navigation techniques for navigation constellations. Engineering Science, 8(3), 22–30.Google Scholar
Springer, T. A., Beutler, G. and Rothacher, M. (1999). A new solar radiation pressure model for GPS satellites, GPS Solutions, 2(3), 50–62.CrossRefGoogle Scholar
Tapley, B. D., Schutz, B. E. and Born, G. H. (2004). Statistical Orbit Determination, Elsevier Academic Press, San Diego.Google Scholar
Wang, H. H. (2007). Man-made earth satellite orbit determination and application of comprehensive research. Ph.D. thesis of Nanjing University, Nanjing, China.Google Scholar
Wang, H. H., Liu, L. and Cheng, H. W. (2010). Ephemeris calibration of satellite interference location system. Chinese Journal of Radio Science, 25(5), 905–912.Google Scholar