Guidepost-based autonomous orbit determination method for GEO satellite

被引:5
作者
Hou, Bowen [1 ]
Wang, Jiongqi [1 ]
Zhou, Haiyin [1 ]
He, Zhangming [1 ]
Li, Dong [2 ]
Liu, Xue [2 ]
机构
[1] Natl Univ Def Technol, Coll Liberal Arts & Sci, Changsha 410073, Peoples R China
[2] Unit 94,PLA 91550, Dalian 116023, Peoples R China
基金
中国国家自然科学基金;
关键词
Autonomous orbit determination; Guidepost; Camera; Observability; Cramer-Rao lower bound; Maximum correntropy unscented Kalman filter; CORRENTROPY UNSCENTED KALMAN; NAVIGATION; SPACECRAFT; ACCURACY; FILTERS; SENSOR; SYSTEM;
D O I
10.1016/j.asr.2020.10.048
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Guidepost-based navigation system is a novel autonomous orbit determination method for the GEO satellite. The system is achieved by using the camera imaging function to obtain the guidepost images and the GNSS signal receiver to obtain the pseudoranges between the GEO and the navigation satellites. Due to the high altitude of GEO satellite and the time-varying sunlight condition in the space environment, it may be difficult to obtain object image points and the distance measurements of GNSS because of the weak visibility of the guideposts. To deal with the problem, a novel integrated orbit determination system is presented. The Earth landmarks, the in-orbit spacecraft and GNSS navigation satellites whose line-of-sights and the distance can be easily obtained are used at the same time as information for the GEO satellite navigation based on the observability conditions analysis. The observability of the GEO satellite navigation system is analyzed through the physical observability, the mathematical observability and the engineering observability through the observing geometry, the rank of observability matrix and the Cramer-Rao lower bound (CRLB) respectively. Besides, the maximum correntropy unscented Kalman filter (MCUKF) algorithm is applied to improve the estimation stability of the system in the presence of non-Gaussian noises. The simulation indicates the feasibility of the proposed scheme. (C) 2020 COSPAR. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:1090 / 1113
页数:24
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