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Chinese Journal of Space Science ›› 2016, Vol. 36 ›› Issue (2): 188-195.doi: 10.11728/cjss2016.02.188

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Atmospheric Drag Coefficient Modification for Orbit Prediction Precision Improvement of LEO Space Objects

CANG Zhongya1,2, XUE Bingsen2, CHENG Guosheng1, ZHU Xiaolu1,2   

  1. 1. Institute of Space Weather, Nanjing University of Information Science and Technology, Nanjing 210044;
    2. National Satellite Meteorology Center, Beijing 100081
  • Received:2015-03-09 Revised:2015-11-18 Online:2016-03-15 Published:2016-03-03

Abstract:

Atmospheric drag is the primary disturbing force to LEO space objects since the atmospheric density is still considerable. This paper proposes a new method based on space environment indices and neural network model to modify drag coefficient. According to TLE (Two Line Element) sets, by simulating the orbit prediction and comparing prediction semi-major axis to real-time value, the optimal values of drag coefficients (B0sup*) are selected. It is found that optimal values are one or two days ahead the values in TLE, and they are all corresponded with F10.7 and Ap indices. Based on historic data, the neural network is built for drag coefficient correction to improve the orbit prediction precision. Result shows that the neural network model could timely response to space environment disturbance. This method is applied in Tiangong-1 (TG-1) and International Space Station (ISS) orbit prediction to verify its validity and universality, and it shows that the orbit prediction accuracy is improved by 50%~60% during geomagnetic disturbance while the errors are biggest. Generally, this method could improve the orbit prediction precision by 30%, the and success rate of improvement is about 80%.

Key words: Space environment, Atmospheric density perturbations, Orbit prediction, TLE sets, Neural network

CLC Number: