Chinese Space Science and Technology ›› 2021, Vol. 41 ›› Issue (3): 70-81.doi: 10.16708/j.cnki.1000.758X.2021.0040

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Short-arc correlation analysis method based on optical observations only

JIANG Ping,ZHANG Yasheng*,TAO Xuefeng,XU Can,FANG Yuqiang,LI Zhi,WANG Hao   

  1. The Space Engineering University, Beijing 101400, China

  • Received:2020-07-22 Revised:2020-08-20 Accepted:2020-09-16 Online:2021-06-25 Published:2021-06-25
  • Contact: 张雅声
  • About author:姜平(1996-),男,硕士研究生,研究方向为空间目标短弧关联技术,。 张雅声(1974-),女,研究员,研究方向为航天任务设计与分析,。
  • Supported by:

Abstract: The initial orbit determination is important to cataloging the space objects. It is challenging to determine the initial orbit using optical anglesonly observations effectively. A prominent method to solve the shortarc problem is to correlate and match the shortarcs obtained at different times to find observations from the same object. Based on the admissible region, the minimum error between the predicted sequence and the actual observation sequence was calculated by finding the optimal orbit to fit multiple optical observations. Then, based on the study of the statistical characteristics of observation errors, the feasibility of the linearized error propagation method in applying shortarc observations was theoretically verified and a reasonable error limit was given. The chisquare test was used to determine the correlation between shortarcs. At the same time, the results applying the proposed shortarc correlation analysis method to LEO, HEO, MEO and GEO orbits were given. The difficulty of applying in the longinterval loworbit object observations was described. Aiming at resolving this difficulty, a method was proposed to improve the loworbit observations association using the error characteristic law of the angle predicted value. The results show that the success rate of correlation recognition for LEO shortarc observations has increased from 87% to 99%.

Key words: shortarc association, anglesonly, observation error, admissible region, multipoint optimization, chisquare test, error characteristics

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