An information fusion algorithm for integrated autonomous orbit determination of navigation satellites |
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| Institution: | 1. Shanghai Micro-Satellite Engineering Center, Room 4115, 99 Haike Road, Shanghai 201203, China;2. Shanghai Institute of Micro-System and Information Technology, Room 2408, 865 Changning Road, Shanghai 200050, China;1. Institute for Astronomical and Physical Geodesy, Technische Universität München, Arcisstraße 21, 80333 München, Germany;2. Deutsches Zentrum für Luft- und Raumfahrt (DLR), German Space Operations Center, Münchner Straße 20, 82234 Weßling, Germany;1. National Time Service Center, Chinese Academy of Sciences, Shu Yuan Road, 710600 Xi’an, China;2. Key Laboratory of Precision Navigation Positioning and Timing Technology, Chinese Academy of Sciences, Shu Yuan Road, 710600 Xi’an, China;3. University of Chinese Academy of Sciences, Yu Quan Road, 100049 Beijing, China;1. Level 8, James Weir Building, 75 Montrose St, Glasgow G1 1XJ, UK;2. Level 5, Technology and Innovation Centre, 99 George Street, Glasgow G1 1RD, UK |
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| Abstract: | This paper focuses on the information fusion problem of integrated autonomous orbit determination using the observations from inter-satellite-link (ISL), X-ray pulsars and star sensors. A step Kalman filter structure is proposed to solve the information fusion problem of multiple subsystems that have greatly different filtering precision. The subsystems are grouped according to their measurement accuracy and the state parameters and covariance matrix of a group can be calculated using the federated filter structure and propagated to the next group step-by-step. Simulation results show that the mean user range error (URE) of the constellation will be less than 1.5 m in 60 days using the step Kalman filter structure for information fusion. And it has better performance than the federated structure in dealing with information fusion of the astronomical observations and the ISL ranging measurements in integrated autonomous orbit determination. |
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