Simplified orbit determination algorithm for Low Earth Orbit satellites using spaceborne GPS navigation sensor

• Autorzy: Aghav S. T. , Gangal S. A.

Identyfikatory

DOI

10.2478/arsa-2014-0007

• Języki publikacji: EN

Abstrakty

EN

In this paper, the main work is focused on designing and simplifying the orbit determination algorithm which will be used for Low Earth Orbit (LEO) navigation. The various data processing algorithms, state estimation algorithms and modeling forces were studied in detail, and simplified algorithm is selected to reduce hardware burden and computational cost. This is done by using raw navigation solution provided by GPS Navigation sensor. A fixed step-size Runge-Kutta 4th order numerical integration method is selected for orbit propagation. Both, the least square and Extended Kalman Filter (EKF) orbit estimation algorithms are developed and the results of the same are compared with each other. EKF algorithm converges faster than least square algorithm. EKF algorithm satisfies the criterions of low computation burden which is required for autonomous orbit determination. Simple static force models also feasible to reduce the hardware burden and computational cost.

Słowa kluczowe

EN

GPS Navigation Sensor; atmospheric drag; extended Kalman filter

• Wydawca: Centrum Badań Kosmicznych PAN ; De Gruyter
• Czasopismo: Artificial Satellites : Journal of Planetary Geodesy
• Rocznik: 2014
• Tom: Vol. 49, No. 2
• Strony: 81--99
• Opis fizyczny: Bibliogr. 14 poz., rys., tab.

Twórcy

autor

Aghav S. T.

• Department of Electronic Science, University of Pune, Pune, India

autor

Gangal S. A.

• Department of Electronic Science, University of Pune, Pune, India

Bibliografia

• [1] Amaral J., Kuga H., Souza M., “Real time multisatellite orbit determination for constellation maintenance”, Proceedings of COBEM. NAVSTAR GPS user equipment introduction, US Government, chapter 7, 2007.
• [2] Bock H., Hugentobler U., Springer T, and Beutlerl G., "Efficient precise orbit determination of LEO satellites using GPS", Advances in Space Research,30(2), pp.295-300, 2002.
• [3] Chiaradia A., Kuga H, and Prado A., "Onboard and Real-Time Artificial Satellite Orbit Determination Using GPS", Mathematical Problems in Engineering, Hindawi Publishing Corporation, 2013.
• [4] Choi E., Yoon J., Lee B., Park S., Choi K.,"Onboard orbit determination using GPS observations based on the unscented Kalman filter",Advances in Space Research, 46,pp. 1440-1450, 2010.
• [5] Degnan, J., and Pavlis, “Laser Ranging to GPS Satellites with Centimeter Accuracy”, E.C.(1994):, GPS World, pp. 62-70, September, 1994.
• [6] Gomes V., Kuga H., Chiaradia A., “Real-Time orbit determination solution using GPS Navigation Solution”, J. Braz. Soc. Mech. Sci. & Eng., 29(3), pp. 274-278, 2007.
• [7] Grewal M., Weill L., Andrews A., “Global Positioning System, Inertial Navigation and Integration”, A John Wiley and Sons Inc, Publication, page.no 37, 2007.
• [8] Pardal P., Kuga H, and deMoraes R., “Implications of the application of recursive least squares algorithms to satellite orbit determination using GPSmeasurements,”WSEAS Transactions on Systems, vol. 8, no. 3, pp. 334-343, 2009.
• [9] Parkinson B., Spilker J., “Global Positioning System: theory and Applications”, AIAA, Vol.1, Progress in Astronautics and Aeronautica 163, 1996.
• [10] Taply B., Schutz B. and Born G, “Statistical orbit determination”, Elsevier Academic Press, USA, 2004.
• [11] Vallado D., McClain W., “Fundamentals of astrodynamics and applications”, 3rd ed., Microcosm press, Ca,2007.
• [12] Xu G., “GPS Theory, Algorithm and Applications”, 2nd Edition, Springer, 2007.
• [13] Yunck T., Bertiger W., Wu S., Bar-Sever Y., Christiansen E., Haines B., Lichten S., Muellerschoen R., and Willis P., “First Assessment of GPS-Based Reduced Dynamic Orbit Determination on TOPEX/Poseidon”, Geophys. Res. Let., V.21, pp. 541-544, 1994.
• [14] Zarchan P., “ Fundamentals of Kalman Filtering: A Practical Approach”, 2nd Edition, by AIAA, 2005.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).