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Observability-based Mars Autonomous Navigation Using Formation Flying Spacecraft

Published online by Cambridge University Press:  01 August 2017

Yangwei Ou Open the ORCID record for Yangwei Ou [Opens in a new window]  and
Hongbo Zhang
Yangwei Ou
Affiliation:
(College of Aerospace Science and Engineering, National University of Defence Technology, Changsha, China)
Hongbo Zhang*
Affiliation:
(College of Aerospace Science and Engineering, National University of Defence Technology, Changsha, China)
*
(E-mail: zhanghb1304@nudt.edu.cn)
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Abstract

This paper concentrates on designing an autonomous navigation scheme for Mars exploration. In this scheme, formation flying spacecraft are used to realise absolute orbit determination when orbiting around Mars. Inertial Line-Of-Sight (LOS) vectors from “deputy” spacecraft to the “chief” are measured using radio cross-link, optical devices and attitude sensors. Since the system's observability is closely related to the navigation performance, an analytical approach is proposed to optimise the observability. In this method, the gravity gradient tensor difference is chosen as the performance index to optimise two navigation scenarios. When there is one deputy flying around the chief, optimal parameters are obtained by solving the constrained optimisation problem. When a second deputy is added into the formation, the optimal configuration is also obtained. These results reveal that the observability is mainly determined by the magnitude of the in-track and cross-track distances in the configuration. An Extended Kalman Filter (EKF) is used to estimate the position and velocity of the chief. The results of a navigation simulation confirms that adding more deputies can significantly improve the navigational performance.

Keywords

Mars explorationAutonomous navigationFormation flying spacecraftDifferential geometrical methodObservability optimisation
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 1 , January 2018 , pp. 21 - 43
Copyright
Copyright © The Royal Institute of Navigation 2017 

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